NVDA - NVIDIA Corporation

Sarah: Good afternoon. My name is Sarah, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA Corporation's third quarter earnings call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question and answer session. If you would like to ask a question during this time, simply press star followed by the number one on your telephone keypad. If you would like to withdraw your question, press star 1 again. Thank you. Toshiya Hari, you may begin your conference. Thank you. Toshiya Hari: Good afternoon, everyone, and welcome to NVIDIA Corporation's conference call for the 2026. With me today from NVIDIA Corporation are Jensen Huang, president and chief executive officer, and Colette Kress, executive vice president and chief financial officer. I'd like to remind you that our call is being webcast live on NVIDIA Corporation's 2026. The content of today's call is NVIDIA Corporation's property. It cannot be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, 11/19/2025, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thank you, Toshiya. We delivered another outstanding quarter with revenue of $57 billion, up 62% year over year and a record sequential revenue growth of $10 billion or 22%. Our customers continue to lean into three platform shifts fueling exponential growth for accelerated computing, powerful AI models, and agentic applications. Yet we are still in the early innings of these transitions that will impact our work across every industry. Currently, we have visibility to a half a trillion dollars in Blackwell and Rubin revenue from the start of this year through the end of calendar year 2026. By executing our annual product cadence and extending our performance leadership through full stack design, we believe NVIDIA Corporation will be the superior choice for the $3 to $4 trillion in annual AI infrastructure build we estimate by the end of the decade. Demand for AI infrastructure continues to exceed our expectations. The clouds are sold out, and our GPU installed base, both new and previous generations, including Blackwell, Hopper, and Ampere, is fully utilized. Record Q3 data center revenue of $51 billion increased 66% year over year, a significant feat at our scale. Compute grew 56% year over year driven primarily by the GB 300 ramp while networking more than doubled given the onset of NVLink scale up and robust double-digit growth across Spectrum X Ethernet and Quantum X InfiniBand. The world hyperscalers, a trillion-dollar industry, are transforming search recommendations, and content understanding from classical machine learning to generative AI. NVIDIA CUDA excels at both and is the ideal platform for this transition, driving infrastructure investment measured in hundreds of billions of dollars. At Meta, AI recommendation systems are delivering higher quality and more relevant content, leading to more time spent on apps such as Facebook and Threads. Analyst expectations for the top CSPs and hyperscalers in 2026 aggregate CapEx have continued to increase and now sit roughly at $600 billion, more than $200 billion higher relative to the start of the year. We see the transition to accelerated computing and generative AI across current hyperscale workloads contributing toward roughly half of our long-term opportunity. Another growth pillar is the ongoing increase in compute spend driven by foundation model builders such as Anthropic, Mastral, OpenAI, Reflection, Safe Superintelligence, Thinking Machines Lab, and xAI. All scaling, compute aggressively to scale intelligence. The three scaling laws pretraining, post-training, and inference remain intact. In fact, we see a positive virtuous cycle emerging whereby the three scaling laws and access to compute are generating better intelligence and in turn increasing adoption and profits. OpenAI recently shared that their weekly user base has grown to 800 million. Enterprise customers have increased to 1 million, and their gross margins were healthy. Well, Anthropic recently reported that its annualized run rate revenue has reached $7 billion as of last month, up from $1 billion at the start of the year. We are also witnessing a proliferation of agentic AI across various industries and tasks. Companies such as Cursor Anthropic, Open Evidence, Epic, and Abridge are experiencing a surge in user growth as they supercharge the existing workforce, delivering unquestionable ROI for coders and healthcare professionals. The world's most important enterprise software platforms like ServiceNow, CrowdStrike, and SAP are integrating NVIDIA Corporation's accelerated computing and AI stack. Our new partner, Palantir, is supercharging the incredibly popular ontology platform with NVIDIA CUDA X libraries and AI models for the first time. Previously, like most enterprise software platforms, Ontology runs only on CPUs. Lowe's is leveraging the platform to build supply chain agility, reducing costs, and improving customer satisfaction. Enterprises broadly are leveraging AI to boost productivity, increase efficiency, and reduce cost. RBC is leveraging agentic AI to drive significant analysts' productivity, slashing report generation time from hours to minutes. AI and digital twins are helping Unilever accelerate content creation by 2x and cut costs by 550%. And Salesforce's engineering team has seen at least 30% productivity increase in new codevelopment after adopting Cursor. This past quarter, we announced AI factory and infrastructure projects amounting to an aggregate of 5 million GPUs. This demand spans every market CSPs, sovereigns, modern builders, enterprises, and supercomputing centers includes multiple landmark build outs. XAI's Colossus two, the world's first gigawatt scale data center. Lilly's AI factory for drug discovery, the pharmaceutical industry's most powerful data center. And just today, AWS and Humane expanded their including the deployment of up to 150,000 AI accelerators, including our GB 300, x AI and Humane also announced a partnership in which the two will jointly develop a network of world-class GPU data centers anchored by the flagship 500 megawatt facility. Blackwell gained further momentum in Q3. As GB 300 crossed over GB 200 and contributed roughly two-thirds of the total Blackwell revenue. The transition to GB 300 has been seamless. With production shipments to the majority to the major, cloud service providers, hyperscalers, and GP clouds and is already driving their growth. The Hopper platform in its thirteenth quarter since exception recorded approximately $2 billion in revenue in Q3. H '20 sales were approximately $50 million. Sizable purchase orders never materialized in the quarter due to geopolitical issues and the increasingly competitive market in China. While we were disappointed in the current state, that prevents us from shipping more competitive data center compute products to China, we are committed to continued engagement with the US and China governments. And will continue to advocate for America's ability to compete around the world. To establish a sustainable leadership and position in AI computing, America must win. The support of every developer, and be the platform of choice for every commercial business including those in China. The Rubin platform is on track to ramp in the 2026. Powered by seven chips, the Vera Rubin platform will once again deliver an x factor improvement in performance relative to Blackwell. We have received silicon back from our supply chain partners and are happy to report that NVIDIA Corporation teams across the world are executing the bring up beautifully. Rubin is our third generation rack scale system substantially redefined the manufacturability while remaining compatible with Grace Blackwell our supply chain data center ecosystem, and cloud partners have now mastered the build to installation process of NVIDIA Corporation's RAC architecture. Our ecosystem will be ready for a fast Rubin ramp. Our annual x factor performance lead increases performance per dollar while driving down computing cost for our customers. The long useful life of NVIDIA Corporation's CUDA GPUs is a significant TCO advantage over accelerators. CUDA's compatibility and our massive installed base extend the life NVIDIA Corporation systems well beyond their original estimated useful life. For more than two decades, we have optimized the CUDA ecosystem, improving existing workloads, accelerating new ones, and increasing throughput with every software release. Most accelerators without CUDA and NVIDIA Corporation's time-tested and versatile app architecture became obsolete within a few years as model technologies evolve. Thanks to CUDA, the a 100 GPUs we shipped six years ago are still running at full utilization today. Powered by vastly improved software stack. We have evolved over the past twenty-five years from a gaming GPU company to now an AI data center infrastructure company. Our ability to innovate across the CPU, the GPU, networking, and software, and ultimately drive down cost per token is unmatched across the industry. Our networking business purpose built for AI, and now the largest in the world. Generated revenue of $8.2 billion, up 162% year over year. With NVLink, InfiniBond, and Spectrum X Ethernet, all contributing to growth. We are winning in data center networking as the majority of AI deployments now include our switches with Ethernet GPU attach rates roughly on par with InfiniBand. Meta, Microsoft, Oracle, and xAI are building gigawatt AI factories with Spectrum X Ethernet switches. And each will run its operating system of choice highlighting the flexibility and openness of our platform. We recently introduced SPECTUM Spectrum XGS, a scale across technology that enables gigascale AI factories. NVIDIA Corporation is the only company with AI scale up scale out, and scale across platforms, reinforcing our unique position in the market as the AI infrastructure provider. Customer interest in NVLink Fusion continues to grow. We announced a strategic collaboration with Suzuki in October where we will integrate Fuzitsu's CPUs and NVIDIA Corporation GPUs via NVLink Fusion. Connecting our large ecosystems. We also announced a collaboration with Intel to develop multiple generations of custom data center and PC products connecting NVIDIA Corporation and Intel's ecosystems using NVLink. This week at supercomputing 25, Arm announced that it will be integrating NVLink IP for customers to build CPU SoCs that connect with NVIDIA Corporation. Currently on its fifth generation, NVLink is the only proven scale up technology available on the market today. In the latest MLPerf training results, Blackwell Ultra delivered 5x faster time to train than hopper. NVIDIA Corporation swept every benchmark. Notably, NVIDIA Corporation is the only training platform to ledge bridge f p four while meeting the MLPerf's strict accuracy standards. In semianalysis, inference max benchmark, Blackwell achieved the highest performance and lowest total cost of ownership across every model and use case. Particularly important is Blackwell's NVLink's performance on a mixture of experts. The architecture for the world's most popular reasoning models. On DeepSeek, r one, Blackwell delivered 10x higher performance per watt and 10x lower cost per token versus h 200. A huge generational leap fueled by our extreme codesign approach NVIDIA Corporation Dynamo, an open source, low latency, modular inference framework has now been adopted by every major cloud service provider leveraging Dynamos enablement and disaggregated inference the resulting such as MOE models, increase in performance of complex AI models AWS, Google Cloud, Microsoft Azure, and OCI have boosted AI inference performance for enterprise cloud customers. We are working on a strategic partnership with OpenAI focused on helping them build and deploy at least 10 gigawatts of AI data centers. In addition, we have the opportunity to invest in the company. We serve OpenAI, through their cloud partners. Microsoft Azure, OCI, and CoreWeave. We will continue to do so for the foreseeable future. As they continue to scale, we are delighted to support the company to add self build infrastructure, and we are working toward a definitive agreement and are excited to support OpenAI's growth. Yesterday, celebrated an announcement with Anthropic. For the first time, Anthropic is adopting NVIDIA Corporation and we are establishing a deep technology partnership to support Anthropics fast growth. We will collaborate to optimize anthropic models for CUDA, and deliver the best possible performance, efficiency, and TCO. We will also optimize future NVIDIA Corporation architectures for anthropic workloads. Anthropics compute commitment is initially including up to one gigawatt of compute capacity, with Grace Blackwell and Vera Rubin systems. Our strategic investments in anthropic menstrual, opening eye, reflection, thinking machines, and other represent partnerships. That grow the NVIDIA Corporation CUDA AI ecosystem and enable every model to run optimally on NVIDIA Corporation's everywhere. We will continue to invest strategically while preserving our disciplined approach to cash flow management. Physical AI is already a multibillion dollar business addressing a multitrillion dollar opportunity, and the next leg of growth for NVIDIA Corporation. Leading US manufacturers and robotics innovators are leveraging NVIDIA Corporation's three computer architecture to train on NVIDIA Corporation. Test on Omniverse computer, and deploy real world AI on Justin robotic computers. PTC and Siemens introduced new services that bring Omniverse powered digital twin workflows to their extensive installed base of customers. Companies including Belden, Caterpillar, Foxconn, Lucid Motors, Toyota, TSMC, and Wistron are building Omniverse Digital Twin factories to accelerate AI driven manufacturing and automation. Agility robotics, Amazon robotics, Figure, and skilled at AI are building our platform, tapping offerings such as NVIDIA Corporation Cosmos World Foundation Models for development, Omniverse for simulation and validation, and Jetson two power next generation intelligent robots. We remain focused on building resiliency and redundancy in our global supply chain. Last month, in partnership with TSMC, we celebrated the first Blackwell wafer produced on US soil. We'll continue to work with Fox conn, Vistron, Amcor, Spill, and others to grow our presence in The US over the next four years. Gaming revenue was $4.3 billion, up 30% year on year driven by strong demand as 42 million gamers, while thousands of fans packed the GeForce Gamer Festival in South Korea. To celebrate twenty-five years of GeForce. NVIDIA Corporation Pro Visualization has evolved into computers for engineers and developers. Whether for graphics, or for AI. Professional visualization revenue was $760 million, up 56% year over year. Was another record. Growth was driven by DGX Spark. The world's smallest AI supercomputer. Built on a small configuration of Grace Blackwell. Automotive revenue was $592 million, up 32% year over year primarily driven by self-driving solutions. We are partnering with Uber to scale the world's largest level four ready autonomous fleet built on the new NVIDIA Corporation Hyperion l four robotaxi reference architecture. Moving to the rest of the p and l. GAAP gross margins were 73.4% and non GAAP gross margins was 73.6%. Exceeding our outlook. Gross margins increased sequentially due to our data center mix, improved cycle time, and cost structure. GAAP operating expenses were up 8% sequentially and up 11% on non GAAP basis. The growth was driven by infrastructure compute, as well as higher compensation and benefits in engineering development costs. Non GAAP effective tax rate for the third quarter was just over 17%. Higher than our guidance of 16.5% due to the strong US revenue. On our balance sheet, inventory grew 32% quarter over quarter while supply commitments increased 63% sequentially. We are preparing for significant growth ahead and feel good about our ability to execute against our opportunity set. Okay. Let me turn to the outlook for the fourth quarter. Total revenue is expected to be $65 billion plus or minus 2%. At the midpoint, our outlook implies 14% sequential growth driven by continued momentum in the Blackwell architecture. Consistent with last quarter, we are not assuming any data center compute revenue from China. GAAP and non GAAP gross margins are expected to be 74.875% respectively. Plus or minus 50 basis points. Looking ahead, to fiscal year twenty twenty-seven, input costs are on the rise but we are working to hold gross margins in the mid-seventies. Gap and non GAAP operating expenses are expected to be approximately $6.7 billion and $5 billion respectively. GAAP and non GAAP other income and expenses are expected to be an income of approximately $500 million, excluding gains and losses from non-marketable and publicly held equity securities. GAAP and non GAAP tax phase are expected to be 17%. Plus or minus 1% excluding any discrete items. At this time, let me turn the call over to Jensen. For him to say a few words. Jensen Huang: Thanks, Colette. There's been a lot of talk about an AI bubble. From our vantage point, we see something very different. As a reminder, NVIDIA Corporation is unlike any other accelerator. We excel at every phase of AI. From pre-training and post-training to inference. And with our two-decade in CUDA x acceleration libraries, we are also exceptional. At science and engineering simulations, computer graphics, structured data processing, to classical machine learning. The world is undergoing three massive platform shifts at once. The first time since the dawn of Moore's Law. NVIDIA Corporation is uniquely addressing each of the three transformations. The first transition is from CPU general-purpose computing to GPU accelerated computing. As Moore's Law slows. The world has a massive investment in non-AI software. From data processing to science and engineering simulations. Representing hundreds of billions of dollars in cloud computing spend each year. Many of these applications, which ran once exclusively on CPUs, are now rapidly shifting to CUDA GPUs. Accelerated computing has reached a tipping point. Secondly, AI has also reached a tipping point. And is transforming existing applications while enabling entirely new ones. For existing applications, generative AI, is replacing classical machine learning in search ranking, recommender systems, ad targeting, click-through prediction, to content moderation, The very foundations of hyperscale infrastructure. Meta's gem a foundation model for ad recommendations trained on large-scale GPU clusters exemplifies this shift. In Q2, Meta reported over a 5% increase in ad conversions on Instagram and 3% gain on Facebook feed. Driven by generative AI based JEM. Transitioning to generative AI. Represents substantial revenue gains for hyperscalers. Now a new wave is rising. Agentic AI systems. Capable of reasoning, planning, and using tools. From coding assistants like Cursor and QuadCode to radiology tools like iDoc, legal assistants like Harvey, and AI chauffeurs like Tesla FSD and Waymo, These systems mark the next frontier of computing. The fastest growing companies in the world today OpenAI, Anthropic, xAI, Google, Cursor, Lovable, Replit, Cognition AI, Open Evidence, a bridge Tesla, are pioneering agentic AI. So there are three massive platform shifts. The transition to accelerated computing is foundational and necessary. Essential in a post-Moore's law era. The transition to generative AI is transformational, and necessary supercharging existing applications and business models. And the transition to agentic and physical AI will be revolutionary, giving rise to new applications, companies, products, services. As you consider infrastructure investments, consider these three fundamental dynamics. Each will contribute to infrastructure growth in the coming years. NVIDIA Corporation is chosen because our singular architecture enables all three transitions. And thus so for any form and modality of AI across all industries, across every phase of AI, across all of the diverse computing needs, in a cloud, and also from cloud to enterprise to robots. One architecture. Toshiya, back to you. Toshiya Hari: We will now open the call for questions. Operator, would you please poll for questions? Sarah: Thank you. At this time, I would like to remind everyone, in order to ask a question, press star, then the number one on your telephone keypad. Thank you. Your first question comes from Joseph Moore with Morgan Stanley. Your line is open. Great. Thank you. I wonder if you could update us. Joseph Moore: You talked about the $500 billion of revenue for Blackwell plus Rubin. 'twenty five and 'twenty six at GTC. At that time, you talked about $150 billion of that already having been shipped. So as the quarter's wrapped up, are those still kind of the general parameters that there's $350 billion in the next kind of, you know, fourteen months or so. And, you know, I would assume over that time, you haven't seen all the demand that there is. There's possibility of upside to those numbers as we move forward? Colette Kress: Yeah. Thanks, Joe. I'll start first with a response here on that. Yes. That's correct. We are working into our $500 billion forecast. And we are on track for that as we have finished some of the quarters. And now we have several quarters now in front of us to take us through the end of calendar year '26. The number will grow. And we will achieve, I'm sure, additional needs for compute that will be shippable by fiscal year '26. So we shipped $50 billion this quarter. But we would be not finished if we didn't say that we'll probably be taking more orders. For example, just even today, our announcements with KSA and that agreement in itself is four to 600,000 more GPUs over three years. Anthropic is also not new. So there's definitely an opportunity for us to have more on top of the $500 billion that we announced. Sarah: The next question comes from C.J. Muse with Cantor Fitzgerald. Your line is open. C.J. Muse: Yes. Good afternoon. Thank you for taking the question. There's clearly a great deal of consternation around the magnitude of AI infrastructure build outs and the ability to fund such plans in the ROI. Yet, you know, at the same time, you're talking about being sold out every stood up GP is taken. The AI world hasn't seen the enormous benefit yet know, from d 300, never mind Rubin. And Gemini three just announced Grok five coming soon. And so the question is this, when you look at that as the backdrop, do you see a realistic path for supply to catch up with demand over the next twelve to eighteen months, or or do you think it can extend beyond that time frame? Jensen Huang: Well, as you know, we've done a really good job planning our supply chain. NVIDIA Corporation's supply chain basically includes every technology company in the world. And TSMC and their packaging and our memory vendors and memory partners and all of our system ODMs have done a really good job planning with us. And we were planning for a big year. You know, we've seen for some time the three transitions that I spoke about just a second ago, accelerated computing, from general-purpose computing and it's really important to recognize that AI is not just agentic AI, but generative AI is transforming the way that hyperscalers did the work that they used to do on CPUs. Generative AI made it possible for them to move search and recommender systems and, you know, add recommendations and targeting. All of that has been generated has been moved to generative AI. And it's still transitioning. And so whether you install NVIDIA Corporation GPUs for data processing, or you did it for generative AI for your recommender system, or you're building it for agentic chatbots and the type of AIs that most people see when they think about AI, all of those applications are accelerated by NVIDIA Corporation. And so when you look at the totality of the spend, it's really important to think about each one of those layers. They're all growing. They're related, but not the same. But the wonderful thing is that they all run on NVIDIA Corporation GPUs. Simultaneously, because the quality of the AI models are improving so incredibly. The adoption of it in the different use cases, whether it's in code assistance, which NVIDIA Corporation uses fairly exhaustively, and we're not the only one. I mean, the fastest growing application in history combination of cursor and CliveCode and code OpenAI's codex and and GitHub Copilot. These applications are the fastest growing in history. And it's not just used for software engineers. It's used by because of vibe coding, it's used by engineers and marketeers all over companies. Supply chain planners, all over companies, And so I think that that's just one example, the list goes on. You know, whether it's open evidence and work that they do in health care or the work that's being done in digital video editing runway. I mean, number of it really, really exciting start ups that are taking advantage of generative AI and agentic AI is growing quite rapidly, and not to mention all using it a lot more. And so all of these exponentials not to mention, you know, just today, I was reading a text from Dennis, and he was saying that that pre-training and post-training are fully intact. You know? And Gemini three takes advantage of the scaling laws, and got it received a huge jump in quality performance model performance. And so we're seeing all of these exponentials kind of running at the same time. And just always go back to first principles and think about what's happening from each one of the dynamics that I mentioned before. General-purpose computing to accelerated computing, generative AI replacing classical machine learning, and, of course, agentic AI, which is a brand new category. Sarah: The next question comes from Vivek Arya with Bank of America Securities. Your line is open. Vivek Arya: Thanks for taking my question. I'm curious what are you making on NVIDIA Corporation content per gigawatt? In that $500 billion number? Because we have heard, you know, numbers as low as $25 billion per gigawatt of content to as high as $30 or $40 billion per gigawatt. So I'm curious what power and what dollar per gigawatt assumptions you are making as part of that $500 billion number, And then longer term, Jensen, the three to $4 trillion in data center by 2030 was mentioned. How much of that do you think will require vendor financing, and how much of that can be supported by cash flows of your large customers or governments or enterprises. Thank you. Jensen Huang: In each generation, from Ampere to Hopper, from Hopper to Blackwell, Blackwell to Rubin, we are our a part of the data center increases. And and hock regeneration was probably something along the lines of twenty some odd, twenty to twenty-five. I Blackwell generation, Grace Blackwell particularly, is probably 30 to 30, you know, say 30 plus or minus. And then Rubin is probably higher than that. And and in each one of these generations, the speed up is x factors, And therefore their TCO, the customer TCO, improves by x factors, and the most important thing is in the end, you still only have one gigawatt of power. You know, one gigawatt data center is one gig gigawatt power, and, therefore, performance per watt, the efficiency of your architecture is incredibly important. And the efficiency of your architecture can't be brute forced. There is no brute forcing about it. That one gigawatt translates directly. Your performance per watt translates directly absolutely directly to your revenues. Which is the reason why choosing the right architecture matters so much now. You know, the world doesn't have an excess of anything to squander. And so we have to be really, really you know, we we use this this concept called co-design. Across our entire stack across the frameworks and models, across the entire data center, even power and cooling, optimized across the entire supply chain in our ecosystem. And so each generation our economic contribution will be greater Our value delivered will be greater. But the most important thing is our energy efficiency per per watt is going to be extraordinary every single generation. With respect to growing into into continuing to grow our customers financing is up to them. We are we we see the opportunity to grow. For quite some time, and remember, today most of the focus has been on the hyperscalers. And one of the areas that is really misunderstood about the hyperscalers is that the investment on NVIDIA Corporation GPUs not only improves their scale, speed, and cost, for from general-purpose computing. That's number one, because Moore's Law has Moore's law scaling has really slowed. Moore's law is about driving cost down. It's about it's about deflationary cost, the incredible deflationary cost of of computing over time. But that has slowed. Therefore, a new approach is necessary for them to keep driving the cost down. Going to NVIDIA Corporation GPU computing is really the the best way to do so. The second is revenue boosting in their current business models. You know, recommender systems drive the world's hyperscalers. Every single whether it's you know, watching watching short form videos or recommending books or recommending the next item in your basket to recommending ads to recommending news to rep it's all about recommenders. The world has the Internet has trillions of pieces of content How could they possibly figure out what to put in front of you in your little tiny screen unless they have really sophisticated recommender systems to do so. Well, that has gone generative AI. So the first two things that I just said hundreds of billions of dollars of CapEx is gonna have to be invested, is fully cash flow funded. What is above it, therefore, is AgenTik AI. This is revenue is net new net new consumption, but it's also net new applications. And some of the applications I've mentioned before, but these are these new applications are also the fastest growing applications in history. Okay? So I think that I you're gonna see that once people start to appreciate what is actually happening under, you know, under the water, if you will, you know, from from the simplistic view of what's happening to CapEx investment recognizing there's these three dynamics. And then lastly, remember we were just talking about the American CSPs. Each country will fund their own infrastructure. And you have multiple countries, You have multiple industries. Most of the world's industries haven't really engaged AgenTic AI yet. And they're about to. You know? All the names of companies that that you know we're working with know, whether whether it's autonomous vehicle companies or digital twins for for physical AI for for factories and the number of factories and warehouses being built around the world. Just the number of digital biology startups that are being funded so that we could accelerate drug discovery. All of those different industries are now getting engaged, they're gonna do their own fundraising. And so don't just look at the hyperscalers, as a way to build out for the future. You gotta look at the world, you gotta look at all the different industries, and, you know, enterprise computing is gonna fund their own industry. Sarah: The next question comes from Ben Reitzes with Melius. Your line is open. Ben Reitzes: Hey, thanks a lot. Jensen, wanted to ask you about cash. Speaking of $05 trillion you may generate about $500 billion in free cash flow over the next couple of years. What are your plans for that cash? How much goes to buyback versus in the ecosystem? And how do you look at investing in the ecosystem? I think there's there's just a lot of confusion out there about how these how these deals work and your criteria for doing those, like the Anthropic, the OpenAI's, etcetera. Thanks a lot. Jensen Huang: Yeah. Appreciate the question. Of course, using cash to fund our growth No company has ever grown at the scale that we're talking about and have the connection and the depth and the breath of supply chain that NVIDIA Corporation has. The reason why our our entire customer base can rely on us is because we've secured a really you know, really resilient supply chain and we have the balance sheet to support them. When we make purchases, our suppliers can take it to the bank. When we make when we make forecasts and we plan with them, they take us seriously. Because of our balance sheet. We're not we're not making up the offtake. We know what our offtake is. And because they've been planning with us for so many years, our reputation and our credibility is incredible. And so so it takes really strong balance sheet to do that. To support the level of growth and the the rate of growth and the magnitude associated with that. So that's number one. The second thing, of course, we're gonna continue to do stock buyback. Buybacks. We're gonna continue to do that. But with respect to the investments, this is really, really important work that we do. All of the investments that we've done so far. Well all the week period is associated with expanding the reach of CUDA, expanding the ecosystem. If you look at the work, investments that we did with OpenAI, of course, that relationship we've had since 2016. Delivered the first AI supercomputer ever made. To OpenAI. So we've had a close and wonderful relationship with OpenAI since then. And everything that OpenAI does runs on NVIDIA Corporation today. So all the clouds that they they deploy in, whether it's training and inference, runs NVIDIA Corporation, and we we love working with them. The partnership that that we have with them is one so that we could work even deeper from a technical perspective so that we could support their accelerated growth This is a company that's growing incredibly fast. And don't just look at don't just look at no. What is in the press. Look at all the ecosystem partners and all the developers that are connected to OpenAI. And they're all driving consumption of it. And the quality of the AI that's being produced huge step up since a year ago. So the quality of response is extraordinary. So we we invest in OpenAI for a deep deep partnership and co-development to expand our ecosystem and to support their growth. And, of course, rather than giving up a share of our company, we get a share of their company. And we invested in them in one of the most consequential once in a generation companies once in a generation company that we have a share And so I fully expect that investment to translate to extraordinary returns. Now in the case of Anthropic, this is the first time that Anthropic will be on NVIDIA Corporation's architecture. The first time NVIDIA Corporation will be Anthropic will be on NVIDIA Corporation's architecture is the the second most successful AI in the world, in terms of total number of users But in enterprise, they're doing incredibly well. ClotCode is doing incredibly well. Clot is doing incredibly well, all of the world's enterprise. And now we have the opportunity to have a deep partnership with them and bringing Claude onto the NVIDIA Corporation platform. And so what what do we have now? NVIDIA Corporation's architecture, taking a step back, NVIDIA Corporation's architecture NVIDIA Corporation's platform, is the singular platform in the world that runs every AI model. We run OpenAI, We run Anthropic. We run XAI. Because of our deep partnership with Elon and x AI, we were able to bring that opportunity to Saudi Arabia to the KSA so that humane could also be hosting opportunity for x AI. We run x AI. We run we run Gemini. We run thinking machines. Let's see. What else do we run? We run them all. And so not to mention, we run the science models, the biology models, DNA models, gene models, chemical models, and all the different fields around the world. It's not just cognitive AI that the world uses. AI is impacting every single industry. And so we have the ability through the ecosystem investments that we make to partner with deeply partner on a technical basis with some of the best companies, most brilliant companies in the world, We are expanding the reach of our ecosystem and we're getting a share and investment in what will what will be a very successful company, oftentimes once in a generation company. And so that basic that's our that's our investment thesis. Sarah: The next question comes from Jim Schneider with Goldman Sachs. Your line is open. Jim Schneider: Afternoon. Thanks for taking my question. In the past, you've talked about roughly 40% of your shipments tied to AI inference I'm wondering as you look forward into next year, where do you expect that percentage could go in say a year's time? And can you maybe address the Rubin CPX product you expect to introduce next year and contextualize that? How big of the overall TAM you expect that can take and maybe talk about some of the target customer applications for that specific product? Thank you. Jensen Huang: CPX is designed for long context type of workload generation. And so long context, basically, before you start generating answers, you have to read a lot. Basically, you know, long context. And it could be a bunch of PDFs, it could be watching a bunch of videos, studying three d images, so on and so forth. You have to you have to absorb the context. And so CPX is designed for long context type of workloads. And it's perf per dollars it's perf per dollar is excellent. It's perf for what is excellent. And which made me forget the first part of the question. Colette Kress: Inprinting. Jensen Huang: Oh, inference. Yeah. There are three scaling laws that are that are scaling at the same time. The first scaling law called pre-training, continues to be. Very effective. And the second is post-training. Post-training basically has found incredible algorithms for improving an AI's ability to break a problem down, and solve a problem step by step. And post-training is scaling exponentially. Basically, the more compute, you apply to a model, the smarter it is. The more intelligent it is. And then the third is inference. Inference because of chain of thought, because of reasoning capabilities, AIs are essentially reading, thinking, before it answers. And the amount of computation necessary as a result of those three things has gone completely exponential. I I think that that it's hard to to know exactly what the percentage will be at any given point in time and who. But, of course, our hope our hope is that inference is a very large part of the market. Because if inference is large, then what it suggests is that people are using using it in more applications, and they're using it more frequently. And that's you know, we should all hope for inference to be very large. And this is where Grace Blackwell is just an order of magnitude better more advanced than anything in the world. The second best platform is h 200, and it's very clear now that g b 300, g b 200, and g b 300, because of MP Link 72, the scale up network that we have, achieved. And you saw and Colette talked about in the seminar analysis benchmark it's the largest single inference benchmark ever done, And GB g b 200 m b link 72 is 10 times 10 to 15 times higher performance. And so that's a big step up. It's gonna take a long time before somebody is able to take that on. And and our leadership there is is surely multiyear. Yep. And so so I think I'm hoping that inference becomes a very big deal. Our leadership in inference is extraordinary. Sarah: The next question comes from Timothy Arcuri with UBS. Your line is open. Timothy Arcuri: Thanks a lot. Jensen, many of your customers are pursuing behind the meter power, but like what's the single biggest bottleneck that worries you that could constrain your growth? Is it power or maybe it's financing, or maybe it's, you know, something else like memory or even foundry? Thanks a lot. Jensen Huang: Well, these are all issues and they're all constraints. And the reason for that, when you're growing at the rate that we are and the scale that we are, how could anything be easy? What NVIDIA Corporation is doing obviously has never been done before. And we've created a whole new industry. On the one hand, we are transitioning computing from general-purpose and classical or traditional computing accelerated computing and AI. That's on one hand. On the other hand, we created a whole new industry called AI factories. The idea that in order for software to run, you need these factories to generate it generate every single token instead of retrieving information that was pre pre created. And so so I think this whole transition requires extraordinary scale. And all the way from the supply chain of course, the supply chain, we have we have much better visibility and control over because obviously, we're incredibly good at managing our supply chain. We have great partners that we've worked with for thirty-three years. And so so the supply chain part of it, we're quite confident. Now looking down our supply chain, we've now established partnerships with so many players in land and power and shell and and, of course, financing. These things none of these things are easy. But they're all attractable, and they're all solvable things. And the most important thing that we have to do is do a good job planning We plan up the supply chain, down the supply chain, We've established a whole lot of partners and so we have a lot of routes to market. And very, you know, very importantly, our architecture has to deliver the best value to the customers that we have. And so at this point, you know, I'm I'm very confident that NVIDIA Corporation's architecture is the best performance per TCL It is the best performance per watt and therefore, for any amount of energy that is delivered our architecture will drive the most revenues. And I think the increasing rate of our success I think that we're more successful this year at this point than we were last year at this point. You know, the the number of customers coming to us and the number of platforms coming to us after they've explored others is increasing, not decreasing. And so think the the I think all of that is just, you know, all the things that I've been telling you over the years are really coming are coming true and or becoming becoming evident. Sarah: The next question comes from Stacy Rasgon with Bernstein Research. Your line is open. Stacy Rasgon: Questions. Colette, I had some questions on margins. You said for next year, you're working to hold them in the mid-seventies. So I I guess, first of all, what are the biggest cost increases? Is is it just memory, or is it something else? What are you doing to work toward that? Is it how much is, like, you know, cost optimizations versus pre buys versus pricing And then also, how should we think about OpEx growth next year given the revenues seem likely to to grow materially? From from where we're running right now? Colette Kress: Stacy. Let me see if I can start with remembering where we were with the current fiscal year that we're in. Remember earlier this year, we indicated that through cost improvements and mix that we would exit the year in our gross margins in the mid-seventies. We achieved that. So now it's time for us to communicate where are we And getting ready to also execute that in Q4. working right now in terms of next year. Next year, there are input prices. That are well known in the industries that we need to work through. And our systems are by no means very easy to work with. There are are tremendous amount of components, many different parts of it as we think about that. So we're taking all of that into account, but we do believe if we look at working again on cost improvements, cycle time, and mix, that we will work to try and hold at our gross margins in the mid-seventies. So that's our overall plan for gross margin. Your second question is around OpEx. And right now, our goal in terms of OpEx is to really make sure that we are innovating with our engineering teams, with all of our business teams, to create more and more systems for this market. As you know, right now, have a new architecture coming out, and that means they are quite busy in order to meet that goal. And so we're gonna continue to see our investments on innovating more and more both our software, both our systems, and our hardware to do so. I'll leave it turn it to Jensen if he wants to add any couple more comments. Yeah. Jensen Huang: I think that's spot on. I think the only thing that would add is is remember that we plan, we forecast, we plan, and we negotiate with our supply chain well in advance. Our supply chain have known for quite a long time our requirements and they've known for quite a long time our demand, and we've been working with them and negotiating with them for quite a long time. And so so I think the the recent surge obviously quite significant, But remember, our supply chain has been working with us for a very long time. It's a And so in many cases, we've secured a lot a lot of supply for ourselves, because, you know, obviously, they're working with the largest company in the world. In doing so. And and and we've also we've also been been working closely with them on the financial aspects of it and and securing forecasts and plans and so on and so forth. So I I think all of that has worked out well for us. Sarah: Your final question comes from the line of Aaron Rakers with Wells Fargo. Your line is open. Aaron Rakers: Jensen, the question is for you. As you think about the entropic deal that was announced and just the overall breadth of your customers, I'm curious if your thoughts around the role that AI ASICs or dedicated play in these architecture build outs that has changed at all? Have you seen you know, I think you've been fairly adamant in the past that that some of these some of these programs never really see deployments. But I'm I'm curious if if we're at a point where maybe maybe that's even changed more in favor of of just GPU architecture. Thank you. Jensen Huang: Yeah. Thank you very much. And I re I really appreciate the question. So first of all, you're competing against teams. You're you're excuse me, against a company. You're get competing against teams. And there are there just aren't that many teams in the world who are built who are extraordinary at building these incredibly complicated things. You know, back in the hopper day, and the ampere days, we would build one GPU. That's the definition of an accelerated AI system. But today, we've gotta build entire racks, entire, you know, three different types of switches. A scale up, a scale out, and a scale across switch. And it takes a lot more than one chip to build a compute node anymore. Everything about that computing system because AI needs to have memory, AI didn't used to have memory at all, Now it has to remember things. The amount of memory and context it has is gigantic. The memory the memory architecture implication is incredible. The diversity of models from mixture of experts to dense models to diffusion models to autoregressive, not to mention you know, biological models that obeys the laws of physics, the list of the list of different types of models have exploded in the last last several years. And so so the challenge is the complexity of the problem is much higher, the diversity of AI models, is incredibly, incredibly large. And so this is where you know, if I will say the five things that makes us special, if you will. You know, the first thing I would say that makes us special is that we accelerate every phase of that transition. That's the first phase. That CUDA allows us to have CUDA x for transitioning from general-purpose of accelerated computing We are incredibly good at generative AI. We're incredibly good at agentic AI. So every single phase of that, every single layer of that transition, we are excellent at. You can invest in one architecture, use it across the board, You can use what one architecture, and, not worry about the changes in the workload across those three phases. That's number one. Number two, we're excellent at every phase of AI. Everybody's always known that we're incredibly good pre-training. We're obviously very good at post-training. We're incredibly good as it turns out, at inference, because inference is really, really hard. How could thinking be easy? You know, people think that inference is one shot and therefore, it's easy. Anybody could approach the market that way. But it turns out to be the hardest of all because thinking, as it turns out, is quite hard. We're great at every phase of AI, the second thing, The third thing is we're now the only architecture in the world that runs every AI model. Every frontier AI model we run open source AI models incredibly well. We run science models, biology models, robotics models, run every single model. We're the only architecture in the world that can claim that. It doesn't matter whether you're autoregressive or diffusion based. We run everything. And we run it for every major platform as I just mentioned. So we run every model. And then the the fourth thing I would say is that in every cloud. The reason why developers love us is because we're literally everywhere. We're in every cloud. We're in every we could even make you a little tiny cloud called DGX Spark. And so we're in every computer. We're everywhere from cloud to on-prem. To robotic systems. Edge devices, PCs, you name it. One architecture things just work. It's incredible. And then the the last thing, this is probably the most important thing, the fifth thing, is if you are a cloud service provider, if you're a new company like Humane, if you're a new company like CoreWeaver, Enscaler, Nebius, or OCI for that matter, The reason why NVIDIA Corporation is the best platform for you is because our off take is so diverse. We can help you with off take. It's not about just putting a random ASIC into a data center. Where's the offtake coming from? Where's the diversity coming from? Where's the resilience coming from? The, you know, the versatility of the architecture coming from, the diversity of capability coming from. NVIDIA Corporation has such incredibly good offtake our ecosystem is so large. So these five things every phase of acceleration and transition, every phase of AI, every model, every cloud to on-prem, and, of course, finally, it all leads to offtake. Sarah: Thank you. I will now turn the call to Toshiya Hari for closing remarks. Toshiya Hari: In closing, please note we will be at the UBS Global and AI Conference on December 2. And our earnings call to discuss the results of our 2026 is scheduled for February 25. Thank you for joining us today. Operator, please go ahead and close the call. Thank you. Sarah: This concludes today's conference call. May now disconnect.

Sarah: Good afternoon. My name is Sarah, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA Corporation's Second Quarter Fiscal 2026 Financial Results Conference Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question and answer session. If you would like to ask a question during this time, simply press star followed by the number one on your telephone keypad. If you would like to withdraw your question, press star 1 again. Thank you. Toshiya Hari, you may begin your conference. Thank you. Toshiya Hari: Good afternoon, everyone, and welcome to NVIDIA Corporation's conference call for 2026. With me today from NVIDIA Corporation are Jensen Huang, president and chief executive officer, and Colette Kress, executive vice president and chief financial officer. I would like to remind you that our call is being webcast live on NVIDIA Corporation's 2026. The content of today's call is NVIDIA Corporation's property. It cannot be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, 08/27/2025, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: We delivered another record quarter while navigating what continues to be a dynamic external environment. Total revenue was $46.7 billion, exceeding our outlook as we grew sequentially across all market platforms. Data center revenue grew 56% year over year. Data center revenue also grew sequentially despite the $4 billion decline in H20 revenue. NVIDIA Corporation's Blackwell platform reached record levels, growing sequentially by 17%. We began production shipments of GB300 in Q2. Our full stack AI solutions for cloud service providers, Neo Clouds, enterprises, and sovereigns are all contributing to our growth. We are at the beginning of an industrial revolution that will transform every industry. We see $3 to $4 trillion in AI infrastructure spend by the end of the decade. The scale and scope of these build-outs present significant long-term growth opportunities for NVIDIA Corporation. The GB200 NBL system is seeing widespread adoption with deployments at CSPs and consumer Internet companies. Lighthouse model builders, including OpenAI, Meta, and Mastral, are using the GB200 NBL72 at data center scale for both training next-generation models and serving inference models in production. The new Blackwell Ultra platform has also had a strong quarter, generating tens of billions in revenue. The transition to the GB300 has been seamless for major cloud service providers due to its shared architecture, software, and physical footprint. The GB200 enables them to build and deploy GB300 racks with ease. The transition to the new GB300 rack-based architecture has been seamless. Factory builds in late July and early August were successfully converted to support the GV300 ramp. Today, full production is underway. The current run rate is back at full speed, producing approximately 1,000 racks per week. This output is expected to accelerate even further throughout the third quarter as additional capacity comes online. We expect widespread market availability in the second half of the year as CorWeave prepares to bring their GV300 instance to market as they are already seeing 10x more inference performance on reasoning models compared to H100. Compared to the previous Hopper generation, GV300 NDL72 AI factories promise a 10x improvement in token per watt energy efficiency, which translates to revenues as data centers are power limited. The chips of the Rubin platform are in fab. The Vera CPU, Rubin GPU, CX9 Supernic, NBLink 144 scale-up switch, SpectrumX scale-out and scale-across switch, and the silicon photonics processor. Rubin remains on schedule for volume production next year. Rubin will be our third-generation NVLink Rack Scale AI supercomputer with a full-scale supply chain. This keeps us on track with our pace of an annual product cadence and continuous innovation across compute, networking, systems, and software. In late July, the US government began reviewing licenses for sales of H20 to China customers. While a select number of our China-based customers have received licenses over the past few weeks, we have not shipped any H20 based on those licenses. USG officials have expressed an expectation that the US will receive 15% of the revenue generated from licensed H20 sales, but to date, the USG has not published a regulation codifying such a requirement. We have not included H20 in our Q3 outlook as we continue to work through geopolitical issues. If geopolitical issues reside, we should ship $2 to $5 billion in H20 revenue in Q3. And if we had more orders, we can bill more. We continue to advocate for the US government to approve Blackwell for China. Our products are designed and sold for beneficial commercial use, and every license sale we make will benefit the US economy, the US leadership, in highly competitive markets. We want to win the hearts of every developer. America's AI technology stack can be the world's standard if we race and compete globally. Notably in the quarter was an increase in Hopper 100 and H200 shipments. We also sold approximately $650 million of H20 in Q2 to an unrestricted customer outside of China. The sequential increase in Hopper demand indicates the breadth of data center workloads that run on accelerated computing and the power of CUDA libraries and full stack optimizations, which continuously enhance the performance and economic value of our platform. As we continue to deliver both Hopper and Blackwell GPUs, we are focusing on meeting the soaring global demand. This growth is fueled by capital expenditures from the cloud to enterprises, which are on track to invest $600 billion in data center infrastructure and compute this calendar year alone, nearly doubling in two years. We expect annual AI infrastructure investments to continue growing, driven by several factors: reasoning agentic AI requiring orders of magnitude more training and inference compute, global build-outs for Sovereign AI, enterprise AI adoption, and the arrival of physical AI and robotics. Blackwell has set the benchmark as it is the new standard for AI inference performance. The market for AI inference is expanding rapidly with reasoning and agentic AI gaining traction across industries. Blackwell's RackScale NVLink and CUDA full stack architecture addresses this by redefining the economics of inference. New NVFP44 bit precision and NVLink 72 on the GB300 platform delivers a 50x increase in energy efficiency per token compared to Hopper, enabling companies to monetize their compute at unprecedented scale. For instance, a $3 million investment in GV200 can generate $30 million in token revenue, a 10x return. NVIDIA Corporation's software innovation combined with the strength of our developer ecosystem has already improved Blackwell's performance by more than 2x since its launch. Advances in CUDA, TensorRT LLM, and Dynamo are unlocking maximum efficiency. CUDA library contributions from the open-source community along with NVIDIA Corporation's open libraries and frameworks are now integrated into millions of workflows. This powerful flywheel of collaborative innovation between NVIDIA Corporation and global community contribution strengthens NVIDIA Corporation's performance leadership. NVIDIA Corporation is a top contributor to OpenAI models, data, and software. Blackwell has introduced a groundbreaking numerical approach to large language model pre-training. Using NBFP4 computations on the GB300 can now achieve 7x faster training than the H100, which uses FP8. This innovation delivers the accuracy of 16-bit precision with the speed and efficiency of 4-bit, setting a new standard for AI factor efficiency and scalability. The AI industry is quickly adopting this revolutionary technology, with major players such as AWS, Google Cloud, Microsoft Azure, and OpenAI, as well as Cohere, Mistral, Kimi AI, Perplexity, Reflection, and Runway, already embracing it. NVIDIA Corporation's performance leadership was further validated in the latest MLPerth training benchmarks, where the GB200 delivered a clean sweep. Be on the lookout for the upcoming MLPerf inference results in September, which will include benchmarks based on the Blackwell Ultra. NVIDIA RTX Pro servers are in full production for the world system makers. These are air-cooled PCIe-based systems integrated seamlessly into standard IT environments and run traditional enterprise 90 companies, including many global leaders, are already adopting RTX Pro servers. Hitachi uses them for real-time simulation and digital twins, Lily for drug discovery, Hyundai for factory design and AV validation, and Disney for immersive storytelling. As enterprises modernize data centers, RTX Pro servers are poised to become a multibillion-dollar product line. Sovereign AI is on the rise as the nation's ability to develop its own AI using domestic infrastructure, data, and talent presents a significant opportunity for NVIDIA Corporation. NVIDIA Corporation is at the forefront of landmark initiatives across the UK and Europe. The European Union plans to invest €20 billion to establish 20 AI factories across France, Germany, Italy, and Spain, including five gigafactories to increase its AI compute infrastructure tenfold. In the UK, the Isambard AI supercomputer powered by NVIDIA Corporation was unveiled as the country's most powerful AI system, delivering 21 exaflops of AI performance to accelerate breakthroughs in fields of drug discovery and climate modeling. We are on track to achieve over $20 billion in Sovereign AI revenue this year, more than double that of last year. Networking delivered record revenue of $7.3 billion, and escalating demands of AI compute clusters necessitate high if and low latency networking. This represents a 46% sequential and 98% year-on-year increase with strong demand across Spectrum X Ethernet, InfiniBand, and NVLink. Our Spectrum X enhanced Ethernet solutions provide the highest throughput and lowest latency network for Ethernet AI workloads. Spectrum X Ethernet delivered double-digit sequential and year-over-year growth with annualized revenue exceeding $10 billion. At hot chips, we introduced SPECTRUM, XGS, Ethernet, a technology designed to unify disparate data centers into gigascale AI super factories. Farweave is an initial adopter of the solution, which is projected to double GPU to GPU communication speed. InfiniBand revenue nearly doubled sequentially, fueled by the adoption of XDR technology, which provides double the bandwidth improvement over its predecessor, especially valuable for the model builders. The world's fastest switch NVLink, with 14x the bandwidth of PCIe Gen five, delivered strong growth as customers deployed Brace Blackwell and D Link Rack Scale systems. The positive reception to NVLink fusion, which allows semicustom AI infrastructure, has been widespread. Japan's upcoming Fugaku NEXT will integrate Fujitsu's CPUs with our architecture via NVLink Fusion. It will run a range of workloads, including AI, supercomputing, and quantum computing. Fugaku NEXT joins a rapidly expanding list of leading quantum supercomputing and research centers running on NVIDIA Corporation's CUDA Q quantum platform, including ULEC, AIST, NNF, and NERSC. Supported by over 300 ecosystem partners, including AWS, Google Quantum, AI, Quantinuum, QEra, and SciQuantum. Justin Thor, our new robotics computing platform, is now available. Thor delivers an order of magnitude greater AI performance and energy efficiency than NVIDIA AGX Orin. It runs the latest generative and reasoning AI models at the edge in real-time, enabling state-of-the-art robotics. Adoption of NVIDIA Corporation's robotics full stack platform is growing at a rapid rate. Over 2 million developers and 1,000 plus hardware, software applications, and sensor partners are taking our platform to market. Leading enterprises across industries have adopted Thor, including Agility Robotics, Amazon Robotics, Boston Dynamics, Caterpillar, Figure, Hexagon, Medtronic, and Meta. Robotic applications require exponentially more compute on the device and in infrastructure, representing a significant long-term demand driver for our data center platform. NVIDIA Omniverse with Cosmos is our data center physical AI digital platform built for the development of robot and robotic systems. This quarter, we announced a major expansion of our partnership with Siemens to enable AI automatic factories. Leading European robotics companies, including Agile Robots, neurorobotics, and universal robots, are building their latest innovations with the Omniverse platform. Transitioning to a quick summary of our revenue by geography. China declined on a sequential basis to low single digits percentage of data center revenue. Note, our Q3 outlook does not include H20 shipments to China customers. Singapore revenue represented 22% of second quarter's billed revenue as customers have centralized their invoicing in Singapore. Over 99% of data center compute revenue billed to Singapore was for US-based customers. Our gaming revenue was a record $4.3 billion, a 14% sequential increase and a 49% jump year on year. This was driven by the ramp of Blackwell GeForce GPUs as strong sales continued as we increase supply availability. This quarter, we shipped GeForce RTX 5060 desktop GPU. It brings double the performance along with advanced ray tracing, neural rendering, and AI-powered DLSS four. Gameplay to millions of gamers worldwide. Blackwell is coming to GeForce NOW in September. This is GeForce NOW's most significant upgrade, offering RTX 5080 class performance, minimal latency, and 5K resolution at 120 frames per second. We are also doubling the GeForce NOW catalog to over 4,500 titles, the largest library of any cloud gaming service. For AI enthusiasts, on-device AI performs the best RTX GPUs. We partnered with OpenAI to optimize their open-source GPT models for high-quality, fast, and efficient inference on millions of RTX-enabled window devices. With the RTX platform stack, window developers can create AI applications designed to run on the world's largest AI PC user base. Professional visualization revenue reached $601 million, a 32% year-on-year increase. Growth was driven by an adoption of the high-end RTX workstation GPUs and AI-powered workloads like design, simulation, and prototyping. Key customers are leveraging our solutions to accelerate their operations. Activision Blizzard uses RTX workstations to enhance creative workflows, while robotics innovator Figure AI powers its humanoid robots with RTX embedded GPUs. Automotive revenue, which includes only in-car compute revenue, was $586 million, up 69% year on year, primarily driven by self-driving solutions. We have begun shipments of NVIDIA Thor SoC, the successor to Orin. Thor's arrival coincides with the industry's accelerating shift to vision, language, model architecture, generative AI, and higher levels of autonomy. Thor is the most successful robotics and AV computer we have ever created or will power. Our full stack drive AV software platform is now opening up billions to new revenue opportunities for NVIDIA Corporation while improving vehicle safety and autonomy. Now moving to the rest of our P&L. GAAP gross margin was 72.4%, and non-GAAP gross margin was 72.7%. These figures include a $180 million or 40 basis point benefit from releasing previously reserved H20 inventory. Excluding this benefit, non-GAAP gross margins would have been 72.3%, still exceeding our outlook. GAAP operating expenses rose 86% on a non-GAAP basis sequentially. This increase was driven by higher compute and infrastructure costs as well as higher compensation and benefit costs. To support the ramp of Blackwell and Blackwell Ultra, inventory increased sequentially from $11 billion to $15 billion in Q2. While we prioritize funding, our growth and strategic initiatives in Q2, we returned $10 billion to shareholders through share repurchases and cash dividends. Our board of directors recently approved a $60 billion share repurchase authorization to add to our remaining $14.7 billion of authorization at the end of Q2. Okay. Let me turn it to the outlook for the third quarter. Total revenue is expected to be $54 billion plus or minus 2%. This represents over $7 billion in sequential growth. Again, we do not assume any H20 shipments to China customers in our outlook. GAAP and non-GAAP gross margins are expected to be 73.3% and 73.5%, respectively, plus or minus 50 basis points. We continue to expect to exit the year with non-GAAP gross margins in the mid-seventies. GAAP and non-GAAP operating expenses are expected to be approximately $5.9 billion and $4.2 billion, respectively. For the full year, we expect operating expenses to grow in the high thirties range year over year, up from our prior expectations of the mid-thirties. We are accelerating investments in the business to address the magnitude of growth opportunities that lie ahead. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $500 million, excluding gains and losses from non-marketable and publicly held equity securities. GAAP and non-GAAP tax rates are expected to be 16.5%, plus or minus 1%, excluding any discrete items. Further financial data are included in the CFO commentary and other information available on our website. In closing, let me highlight upcoming events for the financial community. We will be at the Goldman Sachs Technology Conference on September 8 in San Francisco. Our annual NDR will commence the October GTC data center begins on October 27 with Jensen's keynote scheduled for the twenty-eighth. We look forward to seeing you at these events. Our earnings call to discuss the results of our 2026 is scheduled for November 19. We will now open the call for questions. Operator, would you please poll for questions? Sarah: Thank you. At this time, I would like to remind everyone in order to ask a question, press star, then the number one on your telephone keypad. Thank you. Your first question comes from C.J. Muse with Cantor Fitzgerald. Your line is open. C.J. Muse: I guess with wafer-in to rack-out lead times of twelve months, you confirmed on the call today that Rubin is on track for ramp in the second half. And, obviously, many of these investments are multiyear projects contingent upon power, cooling, etcetera. I was hoping perhaps you could you take a high-level view and speak to, you know, your vision for growth into 2026. And as part of that, if you could kinda comment between network and data center, would be very helpful. Thank you. Toshiya Hari: Yeah. Thanks, C.J. At the highest level of growth, drivers would be the evolution, the introduction, if you will, of reasoning agentic AI. In a word, chatbots used to be one shot. You give it a prompt, and it would generate the answer. Now the AI does research, it thinks and does a plan. And it might use tools. And so it's called long thinking. And the longer it thinks, oftentimes, it produces better answers. And the amount of computation necessary for one shot versus reasoning agentic AI models could be a 100 times, a thousand times, and you potentially even more as the amount of research and basically reading and comprehension that it goes off to do. So the amount of computation that has resulted in agentic AI has grown tremendously. And, of course, the effectiveness has also grown tremendously. Because of agentic AI, the amount of hallucination has dropped significantly. You can now use it, you can now use tools and perform tasks. Enterprises have been opened up. As a result of agentic AI and vision language models, we now are seeing a breakthrough in physical AI in robotics, autonomous systems. So the last year, AI has made tremendous progress. And agentic systems, reasoning systems, is completely revolutionary. Now we built the Blackwell MVLink 72 system, a rack scale computing system for this moment. We have been working on it for several years. This last year, we transitioned from MVLink eight, which is a node scale computing. Each node is a computer. To now NVLink 72 where each rack is a computer. That disaggregation of NVLink 72 into a rack scale system was extremely hard to do. But the results are extraordinary. We are seeing orders of magnitude speed up and, therefore, energy efficiency and, therefore, cost-effectiveness of token generation because of MBLink 72. And so over the next over the next couple of years, you are gonna over well, you asked about longer term. Over the next five years, we are gonna scale into with Blackwell, with Rubin, and follow-ons to scale into a effectively a $3 to $4 trillion AI infrastructure opportunity. The last couple of years, you have seen that CapEx has grown in just the top four CSPs by has doubled and grown to about $600 billion. So we are in the beginning of this build-out, and the AI technology advances have really enabled AI to be able to adopt and solve problems to many different industries. Sarah: Your next question comes from Vivek Arya with Bank of America Securities. Your line is open. Vivek Arya: Colette, just wanted to clarify the $2 billion to $5 billion in China. What needs to happen and what is the sustainable pace of that China business as you get into Q4? And then Jensen, for you on the competitive landscape, several of your large customers already have or are planning many ASIC projects. I think one of your you know, ASIC competitors, Broadcom, signaled that they could grow their AI business almost, you know, 55, 60% next year. Any scenario in which you see the market moving more towards ASICs and away from NVIDIA Corporation GPU? Just what are you hearing from your customers? How are they managing this split between their use of merchant silicon and ASICs. Thank you. Thanks for that. So let me first answer your question regarding what will it take for the H20s to be shipped. There is interest in our H20s. There is the initial set of licenses that we received. And then additionally, we do have supply that we are ready, and that is why we communicated that somewhere in the range of about $2 to $5 billion this quarter we could potentially ship. We are still waiting on several of the geopolitical issues going back and forth between the governments and the companies trying to determine their purchases and what they want to do. So it is still open at this time, and we are not exactly sure what that full amount will be that this quarter. However, if more interest arrives, more licenses arrive, again, we can also still build additional H20 and ship more as well. Jensen Huang: NVIDIA Corporation builds very different things than ASICs. But let us talk about ASICs first. A lot of projects are started. Many startup companies are created. Very few products go into production. And the reason for that is it is really hard. Accelerated computing is unlike general-purpose computing. You do not write software and just compile it into a processor. Accelerated computing is a full stack co-design problem. And AI factories in the last several years have become so much more complex. Because the scale of the problems has grown so significantly. It is really the ultimate, the most extreme computer science problem the world has ever seen, obviously. And so the stack is complicated. The models are changing incredibly fast. From generative based on autoregressive to generative based on diffusion to mixed models to multimodality, the number of different models that are coming out that are either derivatives of transformers or evolutions of transformers is just daunting. One of the advantages that we have is that NVIDIA Corporation is available in every cloud. We are available from every computer company. We are available from the cloud to on-prem to edge to robotics. On the same programming model. And so it is sensible that every framework in the world supports NVIDIA Corporation. When you are building a new model architecture, releasing it on NVIDIA Corporation's most sensible. And so the diversity of our platform both in the ability to evolve into any architecture, the fact that we are everywhere, and, also, we accelerate the entire pipeline. You know, everything from data processing to pretraining to post-training with reinforcement learning all the way out to inference. And so when you build a data center with NVIDIA Corporation, platform in it, the utility of it is best. The lifetime usefulness is much, much longer. And then I would just say that in addition to all of that, it is just a really extremely complex systems problem anymore. You know, people talk about the chip itself. There is one ASIC. The GPU that many people talk about. But in order to build Blackwell the platform, and Reuben, the platform, we had to build CPUs, that connect fast memory, low extremely energy-efficient memory. For large KB caching necessary for agentic AI. To the GPU to a super NIC to a scale-up switch we call NVLink, completely revolutionary when we are in our fifth generation now. To a scale-out switch, whether it is quantum or Spectrum X Ethernet, to now scale across switches so that we could prepare for these AI super factories. With multiple gigawatts of computing all connected together. We call that Spectrum XGS. We just announced that at Hotchips this week. And so the complications, the complexity of everything that we do is really quite extraordinary. It is just done at a really, really extreme scale now. And then lastly, if I could just say one more thing. You know, we are in every cloud for a good reason. Not only do are we the most energy-efficient, our perf per watt is the best of any computing platform. And in a world of power-limited data centers, perf per watt drives directly to revenues. And, you know, you have heard me say before, that in a lot of ways, the more you buy, the more you grow. And because our perf per dollar, the performance per dollar is so incredible, you also have extremely great margins. So the growth opportunity with NVIDIA Corporation's architecture and the gross margins opportunity with NVIDIA Corporation's architecture is absolutely the best. And so there are a lot of reasons why NVIDIA Corporation has chosen by every cloud and every startup and every computer company. We are, you know, really a holistic full stack solution for AI factories. Sarah: Your next question comes from Ben Reitzes with Melius. Your line is open. Ben Reitzes: Hey, thanks a lot. Jensen, I wanted to ask you about your $3 to $4 trillion in data center infrastructure spend by the end of the decade. Previously, you talked about something in the $1 trillion range, which I believe was just for compute. By 2028. If you take past comments, you know, $3 to $4 trillion would imply maybe $2 trillion plus in compute spend. And just wanted to know if that was right and that is what you are seeing by the end of the decade. And wondering what you think your share will be of that, your share right now of total infrastructure compute-wise is very high. So, wanted to see. And also if there are any bottlenecks you are concerned about, like power. To get to the $3 to $4 trillion. Thanks a lot. Jensen Huang: Yeah. Thanks. As you know, the CapEx of just the top four hyperscalers has doubled in two years. As the AI revolution went into full steam, as the AI race is now on, the CapEx spend has doubled to $600 billion per year. There are five years between now and the end of the decade. And $600 billion only represents the top four hyperscalers. We still have the rest of the enterprise companies building on-prem. You have enter you have cloud service providers building around the world. United States represents about 60% of the world's compute. And over time, you would think that artificial intelligence would reflect GDP scale and growth. And so and would be, of course, accelerating GDP growth. And so our contribution to that is a large part of the AI infrastructure out of a gigawatt AI factory, which can go anywhere from 50 to, you know, plus or minus 10%, let us say, 50 to $60 billion, we represent about 35 plus or minus of that. And 35 out of fifty or so billion dollars for a gigawatt data center. And of course, what you get for that is not a GPU. Think people, you know, were famous for building the GPU and inventing the GPU. But as you know, over the last decade, we have really transitioned to become an AI infrastructure company. It takes six chips just to build six different types of chips just to build an AI, a Rubin AI supercomputer. And just to scale that out, you know, to a gigawatt, you have hundreds of thousands of GPU compute nodes. And a whole bunch of racks. And so we are really an AI infrastructure company and we are hoping to continue to contribute to growing this industry, making AI more useful, and then very importantly, driving the performance per watt because the world, as you mentioned, limiters it will always likely be power limitations or AI or AI building limitations. And so we need to squeeze as much out of that factory as possible. NVIDIA Corporation's performance per unit of energy used drives the revenue growth of that factory. It directly translates. If you have a 100-megawatt factory, perf per 100 megawatt drives your revenues. It is tokens per 100 megawatts of factory. In our case, also, the performance per dollar spent is so high that your gross margins are also the best. But anyhow, these are the limiters going forward. And $3 to $4 trillion is fairly sensible for the next five years. Sarah: Next question comes from Joe Moore of Morgan Stanley. Your line is open. Joe Moore: Great. Thank you. Congratulations on reopening the China opportunity. Can you talk about the long-term prospects there? You have talked about, I think, half of AI software world being there. You know, how much can NVIDIA Corporation grow in that business? And you know, how important is it that you get the Blackwall architecture ultimately licensed there? Jensen Huang: The China market, I have estimated to be above $50 billion of opportunity for us this year. If we were able to address it, with competitive products and if it is $50 billion this year, you would expect it to grow say, 50% per year. As the rest of the world's AI market is growing as well. It is the second-largest computing market in the world, and it is also the home of AI researchers. About 50% of the world's AI researchers are in China. The vast majority of the leading open-source models are created in China. And so it is fairly important, I think, for the American technology companies to be able to address that market. And open source, as you know, is created in one country, but it is used all over the world. The open-source models that have come out of China are really excellent. DeepSeek, of course, gained global notoriety. QN is excellent. Kimi is excellent. There is a whole bunch of new models that are coming out. They are multimodal. They are great language models. And it is really fueled the adoption of AI in enterprises around the world because enterprises want to build their own custom proprietary software stacks. And so open-source model is really important for enterprise. It is really important for SaaS. Who also would like to build proprietary systems. It has been really incredible for robotics around the world. And so open source is really important. And it is important that the American companies are able to address it. This is going to be a very large market. We are talking to the administration about the importance of American companies to be able to address the Chinese market. And, as you know, H20 has been approved for companies that are not on the entities list. And many licenses have been approved. And so I think the opportunity for us to bring Blackwell to the China market is a real possibility. And so we just have to keep advocating the sensibility of and the importance of American tech companies to be able to lead and win the AI race. And help make the American tech stack the global standard. Sarah: Your next question comes from the line of Aaron Rakers with Wells Fargo. Your line is open. Aaron Rakers: Yes. Thank you for the question. I want to go back to the Spectrum XGS announcement this week and, you know, thinking about the Ethernet product now pushing over $10 billion of annualized revenue. You know, Jess, what is the opportunity set that you see for Spectrum XGS? Do you think about this as kind of the data center interconnect layer? Any thoughts on the sizing of this opportunity? You know, within that Ethernet portfolio? Thank you. Jensen Huang: We now offer three networking technologies. One is for scale-up, one is for scale-out, and one for scale across. Scale-up is so that we could build the largest possible virtual GPU, the virtual compute node. NVLink is revolutionary. NVLink 72 is what made it possible for Blackwell to deliver such an extraordinary generational jump over Hopper's MBLink eight. At a time when we have long-thinking models, agentic AI reasoning systems, the NVLink basically amplifies the memory bandwidth, which is really critical for reasoning systems. And so NVLink 72 is fantastic. We then scale out with networking, which we have two. We have InfiniBand, which is unquestionably the lowest latency, the lowest jitter, the best scale-out network. It does require more expertise in managing those networks, and for supercomputing, for the leading model makers, InfiniBand, quantum InfiniBand is the unambiguous choice. If you were to benchmark an AI factory, ones with InfiniBand are the best performance. For those who would like to use Ethernet because their whole data center is built with Ethernet, we have a new type of Ethernet called Spectrum Ethernet. Spectrum Ethernet is not off the shelf. It has a whole bunch of new technologies designed for low latency and low jitter and congestion control, and it has the ability to come closer, much, much closer to InfiniBand than anything that is out there. And that is we call that Spectrum X Ethernet. And then finally, we have Spectrum XGS, a gigascale for connecting multiple data centers, multiple AI factories into a super factory. A gigantic system. And you are going to see that networking obviously is very important in AI factories. In fact, choosing the right networking, the performance, the throughput, improvement. Going from, you know, 65% to 85% or 90%. That kind of step up because of your networking capability effectively makes networking free. You know? Choosing the right networking, you are basically paying, you know, you will get a return on it like you cannot believe because the AI factory, a gigawatt, as I mentioned before, could be $50 billion. And so the ability to improve the efficiency of that factory by tens of percents results in $1 to $20 billion worth of effective benefit. So, you know, the networking is a very important part of it. It is the reason why NVIDIA Corporation dedicates so much in networking. That is the reason why we purchased Mellanox five and a half years ago. And Spectrum X, as we mentioned earlier, is now quite a sizable business, and it is only about a year and a half old. So Spectrum X is a home run. All three of them are going to be fantastic. NVLink scale-up, SpectrumX, and InfiniBand, scale-out. And then Spectrum XGS for scale across. Sarah: Your next question comes from Stacy Rasgon with Bernstein Research. Your line is open. Stacy Rasgon: Hi, guys. Thanks for taking my question. Have a more tactical question for Colette. So on the guide, we are up, you know, over $7 billion. The vast bulk of that is going to be from data center. How do I think about apportioning that $7 billion out across Blackwell versus Hopper versus networking? I mean, it looks like Blackwell was probably $27 billion in the quarter up from maybe $23 billion last quarter. You know, Hopper is still $6 or $7 billion. Post the H20. Like, do you think the Hopper strength continues? Because how do I think about parsing that $7 billion out across all the three those three different components? Colette Kress: Thanks, Stacy, for the question. First part of it, looking at our growth between Q2 and Q3, Blackwell is still going to be the lion's share of what we have in terms of data center. But keep in mind, that helps both our compute side as well as it helps our networking side. Because we are selling those significant systems that are incorporating the NVLink that Jensen just spoke about. Selling Hopper, we are still selling it. H100, H200s, we are. But, again, they are HCX systems, and I still believe our Blackwell will be the lion's share of what we are doing. On there. So we will continue. We do not have any more specific details, in terms of how we will finish our quarter but you should expect Blackwell again to be the driver of the growth. Sarah: Your next question comes from Jim Schneider of Goldman Sachs. Your line is open. Jim Schneider: Good afternoon. Thanks for taking my question. You have been very clear about the reasoning model opportunity that you see and you have also been relatively clear about the technical specs for Rubin, but maybe you could provide a little bit of context about how you view the Rubin product transition going forward. What incremental capability does that offer to customers? And would you say that Rubin is a bigger, smaller, or similar step up in terms of performance for capability perspective relative to what we saw with Blackwell? Thank you. Jensen Huang: Yeah. Thanks. Ruben. Ruben, we are on an annual cycle. And the reason why we are on an annual cycle is because we can do so to accelerate the cost reduction and maximize the revenue generation for our customers. When we increase the perf per watt, the token generation per amount of usage of energy. We are effectively driving the revenues of our customers. The perf per watt of Blackwell will be for reasoning systems an order of magnitude higher than Hopper. And so for the same amount of energy and everybody's data center energy limited by definition, for any data center, that we using Blackwell, you will be able to maximize your revenues. Compared to anything we have done in the past compared to anything in the world today. And because the perf per dollar the performance is so good, that the perf per dollar invested in the capital would also allow you to improve your gross margins. To the extent that we have great ideas for every single generation, we could improve their revenue generation, improve the AI capability, improve the margins, of our customers, by releasing new architectures. And so we advise our partners, our customers to pace themselves and to build these data centers on an annual rhythm. Ruben is going to have a whole bunch of new ideas. I paused for a second because, you know, I have got plenty of time between now and a year from now to tell you about all the breakthroughs that Rubens are going to bring. But Ruben has a lot of great ideas. I am anxious to tell you but I cannot right now. And I will save it for a GTC to tell you more and more about it. But, nonetheless, for the next year, we are ramping really hard into now Grace Blackwell, GB200, and then now 300, we are ramping really hard into data centers. This year is obviously a record-breaking year. I expect next year to be a record-breaking year. And while we continue to increase the performance of AI capabilities as we race towards artificial superintelligence on the one hand, and continue to increase the revenue generation capabilities of our hyperscalers on the other hand. Sarah: Your final question comes from Timothy Arcuri with UBS. Your line is open. Timothy Arcuri: Thanks a lot. Jensen, I wanted to ask you just answer the question. You threw out a number. You said 50% CAGR for the AI market. So I am wondering how much visibility that you have into next year. Is that kind of a reasonable bogey in terms of how much your data center revenue should grow next year? I would think you will grow at least in line with that CAGR. And maybe are there any puts and takes to that? Thanks. Jensen Huang: Well, I think the best way to look at it is we have reasonable forecasts from our large customers for next year. A very, very significant forecast. And we still have a lot of businesses that we are still winning. And a lot of startups that are still being created. Do not forget that the number of startups for AI native startups was $100 billion was funded last year. This year, the year is not even over yet. It is $180 billion funded. If you look at AI native, the top AI native startups that are generating revenues, last year was $2 billion. This year is $20 billion. Next year, being 10 times higher than this year, is not inconceivable. And the open-source models are now opening up large enterprises, SaaS companies, industrial companies, robotics companies, to now join the AI revolution another source of growth and you know, whether it is AI natives or enterprise SaaS or industrial AI, or startups. We are just seeing just enormous amount of interest in AI and demand for AI. Right now, the buzz is I am sure all of you know about the buzz out there. The buzz is everything sold out. H1 Hers sold out. H2 hundreds are sold out. Large CSPs are coming out renting capacity from other CSPs. And so the AI native startups really scrambling to get capacity. So that they could train their reasoning models. And so the demand is really, really high. But the long-term outlook between where we are today, CapEx has doubled in two years. It is now running about $600 billion a year just in the large hyperscalers. For us to grow into that $600 billion a year representing a significant part of that CapEx is not unreasonable. And so I think the next several years, surely through the decade, we see just a really fast-growing, really significant growth opportunities ahead. Let me conclude with this. Blackwell is the next-generation AI platform the world has been waiting for. Delivers an exceptional generational leap. NVIDIA Corporation's NVLink 72 rack scale computing is revolutionary. Arriving just in time as reasoning AI models drive order of magnitude increases in training and inference performance requirement. Blackwell Ultra is ramping at full speed, and the demand is extraordinary. Our next platform, Rubin, is already in fab. We have six new chips that represent the Rubin platform. They have all taped out the TSMC. Rubin will be our third-generation MB Link Rack Scale AI supercomputer. And so we expect to have a much more mature and fully scaled-up supply chain. Blackwell and Rubin AI factory platforms will be scaling into the $3 to $4 trillion global AI factory build-out through the end of the decade. Customers are building ever-greater scale AI factories. From thousands of Hopper GPUs in tens of megawatt data centers to now hundreds of thousands of Blackwells in 100-megawatt facilities and soon, we will be building millions of Rubin GPU platforms powering multi-gigawatt multisite AI super factories. With each generation, demand only grows. One-shot chatbots have evolved into reasoning agentic AI, that research plan, and use tools. Driving orders of magnitude jump and compute for both training and inference, agentic AI is reaching maturity, and has opened the enterprise market to build domain and company-specific AI agents. For enterprise workflows, products, services. The age of physical AI has arrived unlocking entirely new industries in robotics, industrial automation, every industry and every industrial company will need to build two factories. One to build the machines, and another to build their robotic AI. This quarter, NVIDIA Corporation reached record revenues and an extraordinary milestone in our journey. The opportunity ahead is immense. A new industrial revolution has started. The AI race is on. Thanks for joining us today, and I look forward to addressing you next week. Next earnings call. Thank you. Sarah: This concludes today's conference call. You may now disconnect.

Sarah: Good afternoon. My name is Sarah, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA Corporation's First Quarter Fiscal 2026 Financial Results Conference Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question and answer session. If you would like to ask a question during this time, please press the star key followed by the number one on your telephone keypad. Thank you. Toshiya Hari, you may begin your conference. Toshiya Hari: Thank you. Good afternoon, everyone, and welcome to NVIDIA Corporation's conference call for the first quarter fiscal 2026. With me today from NVIDIA Corporation are Jensen Huang, president and chief executive officer, and Colette Kress, executive vice president and chief financial officer. I'd like to remind you that our call is being webcast live on NVIDIA Corporation's investor relations website. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2026. The content of today's call is NVIDIA Corporation's property. It cannot be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10-K and 10-Q, and the reports that we may file on form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 28, 2025, and based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thank you, Toshiya. We delivered another strong quarter, with revenue of $44 billion, up 69% year over year, exceeding our outlook in what proved to be a challenging operating environment. Data center revenue of $39 billion grew 73% year on year. The AI workloads have transitioned strongly to inference, and AI factory build-outs are driving significant revenue. Our customers' commitments are firm. On April 9, the US government issued new export controls on H20, our data center GPU designed specifically for the China market. We sold H20 with the approval of the previous administration. Although our H20 has been in the market for over a year and does not have a market outside of China, the new export controls on H20 did not provide a grace period to allow us to sell through our inventory. In Q1, we recognized $4.6 billion in H20 revenue, which occurred prior to April 9, but also recognized a $4.5 billion charge as we wrote down inventory and purchase obligations tied to orders we had received prior to April 9. We were unable to ship $2.5 billion in H20 revenue in the first quarter due to the new export controls. The $4.5 billion charge was less than what we initially anticipated as we were able to reuse certain materials. We are still evaluating our limited options to supply data center compute products compliant with the US government's revised export control rules. Losing access to the China AI accelerator market, which we believe will grow to nearly $50 billion, would have a material adverse impact on our business going forward and benefit our foreign competitors in China and worldwide. Our Blackwell ramp, the fastest in our company's history, drove a 73% year-on-year increase in data center revenue. Blackwell contributed nearly 70% of data center compute revenue in the quarter, with a transition from Hopper nearly complete. The introduction of GB200 NBL was a fundamental architectural change to enable data center scale workloads and to achieve the lowest cost per inference token. These systems are complex to build, we have seen a significant improvement in manufacturing yields, and rack shipments are moving to strong rates to end customers. GB200 and VO racks are now generally available for model builders, enterprises, and sovereign customers to develop and deploy AI. On average, major hyperscalers are each deploying nearly 1,000 NBL72 racks, or 72,000 Blackwell GPUs per week, and are on track to further ramp output this quarter. Microsoft, for example, has already deployed tens of thousands of Blackwell GPUs and is expected to ramp to hundreds of thousands of GB200s with OpenAI as one of its key customers. Key learnings from the GB200 ramp will allow for a smooth transition to the next phase of our product roadmap, Blackwell Ultra. Sampling of GB300 systems began earlier this month at the major CSPs, and we expect production shipments to commence later this quarter. GB300 will leverage the same architecture, same physical footprint, and the same electrical and mechanical specifications as GB200. The GB300 drop-in design will allow CSPs to seamlessly transition their systems and manufacturing used for GB200 while maintaining high yields. B300 GPUs, with 50% more HBM, will deliver another 50% increase in dense FP4 inference compute performance compared to the B200. We remain committed to our annual product cadence with our roadmap extending through 2028, tightly aligned with the multiple-year planning cycles of our customers. We are witnessing a sharp jump in inference demand. OpenAI, Microsoft, and Google are seeing a step function leap in token generation. Microsoft processed over 100 trillion tokens in Q1, a fivefold increase on a year-over-year basis. This exponential growth in Azure OpenAI is representative of strong demand for Azure AI Foundry, as well as other AI services across Microsoft's platform. Inference serving startups are now serving models using B200, tripling their token generation rate and corresponding revenues for high-value reasoning models such as Deepseeker One. As reported by Artificial Artificial Analysis, NVIDIA Corporation's Blackwell NBL72 turbocharges AI inference throughput by 30x for the new reasoning models sweeping the industry. Developer engagements increased with adoption ranging from LLM providers such as Perplexity to financial service institutions such as Capital One, who reduced agentic chatbot latency by 5x with Dynamo. In the latest MLPerf inference results, we submitted our first results using GB200 NBL72, delivering up to 30x higher inference throughput compared to our 8 GPU H200 submission on the challenging Llama 3.1 benchmark. This feat was achieved through a combination of tripling the performance per GPU as well as 9x more GPUs, all connected on a single NVLink domain. And while Blackwell is still early in its life cycle, software optimizations have already improved its performance by 1.5x in the last month alone. We expect to continue improving the performance of Blackwell through its operational life as we have done with Hopper and Ampro. For example, we increased the inference performance of Hopper by four times over two years. This is the benefit of NVIDIA Corporation's programmable CUDA architecture and rich ecosystem. The pace and scale of AI factory deployments are accelerating, with nearly 100 NVIDIA Corporation-powered AI factories in flight this quarter, a twofold increase year over year, with the average number of GPUs powering each factory also doubling in the same period. More AI factory projects are starting across industries and geographies. NVIDIA Corporation's full-stack architecture is underpinning AI factory deployments as industry leaders like AT&T, BYD, Capital One, Foxconn, MediaTek, and Telenor are strategically vital sovereign clouds like those recently announced in Saudi Arabia, Taiwan, and the UAE. We have a line of sight to projects requiring tens of gigawatts of NVIDIA Corporation AI infrastructure in the not-too-distant future. The transition from generative to agentic AI, AI capable of perceiving, reasoning, planning, and acting, will transform every industry, every company, and country. We envision AI agents as a new digital workforce capable of handling tasks ranging from customer service to complex decision-making processes. We introduced the Llama NemoTron family of open reasoning models designed to supercharge agentic AI platforms for enterprises. Built on the Llama architecture, these models are available as NIMS or NVIDIA Corporation inference microservices with multiple sizes to meet diverse deployment needs. Our post-training enhancements have yielded a 20% accuracy boost and a 5x increase in inference speed. Leading platform companies including Accenture, Cadence, Deloitte, and Microsoft are transforming work with our reasoning models. NVIDIA Corporation's Nexmo microservices are generally available across industries and are being leveraged by leading enterprises to build, optimize, and scale AI applications. With Nexmo, Cisco increased model accuracy by 40% and improved response time by 10x in its code assistant. Nasdaq realized a 30% improvement in accuracy and response time in its AI platform's search capabilities. Shell's custom LLM achieved a 30% increase in accuracy when trained with NVIDIA Corporation's Nexmo. Nemo's parallelism techniques accelerated model training time by 20% when compared to other frameworks. We also announced a partnership with Yam Brands, the world's largest restaurant company, to bring NVIDIA Corporation AI to 500 of its restaurants this year, expanding to 61,000 restaurants over time to streamline order taking, optimize operations, and enhance service across its restaurants. For AI-powered cybersecurity, leading companies like Checkpoint, Cloudstrike, and Palo Alto Networks are using NVIDIA Corporation's AI security and software stack to build, optimize, and secure agentic workflows, with CloudStrike realizing 2x faster detection, triage with 50% less compute cost. Moving to networking, sequential growth in network resumed in Q1 with revenue up 64% quarter over quarter to $5 billion. Our customers continue to leverage our platform to efficiently scale up and scale out AI factory workloads. We created the world's fastest switch, NVLink, for scale-up or NVLink compute fabric in its fifth generation offers 14x the bandwidth of PCIe Gen 5. NVLink72 carries 130 terabytes per second, a bandwidth in a single rack equivalent to the entirety of the world's peak Internet traffic. NVLink is a new growth vector and is off to a great start, with Q1 shipments exceeding $1 billion. At Computex, we announced NVLink Fusion. Hyperscale customers can now build semi-custom CCUs and accelerators that connect directly to the NVIDIA Corporation platform with NVLink. We are now enabling key partners, including ASIC providers such as MediaTek, Norbel, Alchip Technologies, and Astera Labs, as well as CPU suppliers such as Pizizzo and Qualcomm, to leverage NVLink Fusion to connect our respective ecosystems. For scale-out, our enhanced Ethernet offerings deliver the highest throughput low latency networking for AI. Spectrum X posted strong sequential and year-on-year growth and is now annualizing over $8 billion in revenue. Adoption is widespread across major CSPs and consumer Internet companies, including Coreweave, Microsoft Azure, Oracle Cloud, and XAI. This quarter, we added Google Cloud and Meta to the growing list of SpectrumX customers. Introduced Spectrum X and Quantum X silicon photonics switches, featuring the world's most advanced copacage optics. These platforms will enable next-level AI factory scaling to millions of GPUs through the increasingly power efficiency by 3.5x and network resiliency by 10x while accelerating customer time to market by 1.3x. Transitioning to a quick summary of our revenue by geography. China, as a percentage of our data center revenue, is slightly below our expectations and down sequentially due to H20 export licensing controls. For Q2, we expect a meaningful decrease in China data center revenue. As a reminder, while Singapore represented nearly 20% of our Q1 build revenue as many of our large customers use Singapore for centralized invoicing, our products are almost always shelved elsewhere. Note that over 99% of H100, H200, and Blackwell data center compute revenue billed to Singapore was for orders from US-based customers. Moving to gaming and AI PCs. Gaming revenue was a record $3.8 billion, increasing 48% sequentially and 42% year on year. Strong adoption by gamers, creatives, and AI enthusiasts have made Blackwell our fastest ramp ever. Against the backdrop of robust demand, we greatly improved our supply and availability in Q1 and expect to continue these efforts in Q2. AI is transforming PC and creator and gamers. With a 100 million user installed base, GeForce represents the largest footprint for PC developers. This quarter, we added to our AI PC laptop offerings, including models capable of running Microsoft's Copilot. Plus, this past quarter, we brought Blackwell architecture to mainstream gaming with its launch of GeForce RTX 5060 and 5060 Ti, starting at just $299. The RTX 5060 also debuted in laptops starting at $1,099. These systems double the frame rate and slash latency. These GeForce RTX 5060 and 5060 TI desktop GPUs and laptops are now available. In console gaming, the recently unveiled Nintendo Switch 2 leverages NVIDIA Corporation's neural rendering and AI technologies, including next-generation custom RTX GPUs with DLSS technology to deliver a giant leap in gaming performance to millions of players worldwide. Nintendo has shipped over 150 million Switch consoles to date, making it one of the most successful gaming systems in history. Moving to crow visualization. Revenue of $509 million was flat sequentially and up 19% year on year. Tariff-related uncertainty temporarily impacted Q1 systems. Demand for our AI workstations is strong, and we expect sequential revenue growth to resume in Q2. NVIDIA Corporation's DGX Spark and Station revolutionized personal computing by putting the power of an AI supercomputer in a desktop form factor. DGX Spark delivers up to one petaflop of AI compute while DGX Station offers an incredible 20 petaflops and is powered by the GB300 superchip. DGX Spark will be available in calendar Q3, and DGX Station later this year. We have deepened Omniverse's integration and adoption into some of the world's leading software companies, including SAP and Schneider Electric. New Omniverse blueprints such as MEGA for at-scale robotic fleet management are being leveraged by Keyon Group, Pegatron, Accenture, and other leading companies to enhance industrial operations. At Computex, we showcased Omniverse's great traction with technology manufacturing leaders, including TSMC, Quanta, Foxconn, and Pegatron. Using Omniverse, TSMC saves months in work by designing fabs virtually. Foxconn accelerates thermal simulations by 150x. Pegatron reduced assembly line defect rates by 67%. Lastly, with our automotive group, revenue was $567 million, down 1% sequentially, but up 72% year on year. Year-on-year growth was driven by the ramp of self-driving across a number of customers and robust end demand for NEVs. We are partnering with GM to build the next-gen vehicles, factories, and robots using NVIDIA Corporation's AI simulation and accelerated computing. We are now in production with our full-stack solution for Mercedes Benz, starting with the new CLA, hitting roads in the next few months. We announced Isaac Groot, N1, the world's first open fully customizable foundation model for humanoid robots, enabling generalized reasoning and skill development. We also launched new open NVIDIA Corporation Cosmo World Foundation models. Leading companies include OneX, Agility Robots, Figueroa, Uber, and Wabi. We've begun integrating Cosmos into their operations for synthetic data generation. While agility robotics, Boston Dynamics, and XPEN robotics are harnessing Isaac simulation to advance their humanoid efforts. GE Healthcare is using the new NVIDIA Corporation Isaac platform for healthcare simulation built on NVIDIA Corporation Omniverse and using NVIDIA Corporation Cosmos. The platform speeds development of robotic imaging and surgery systems. The era of robotics is here. Billions of robots, hundreds of millions of autonomous vehicles, and hundreds of thousands of robotic factories and warehouses will be developed. Alright. Moving to the rest of the P&L. GAAP gross margins and non-GAAP gross margins were 60.5% and 61% respectively. Excluding the $4.5 billion charge, Q1 non-GAAP gross margins would have been 71.3%, slightly above our outlook at the beginning of the quarter. Sequentially, GAAP operating expenses were up 7% and non-GAAP operating expenses were up 6%, reflecting higher compensation and employee growth. Our investments include expanding our infrastructure capabilities and AI solutions, and we plan to grow these investments throughout the fiscal year. In Q1, we returned a record $14.3 billion to shareholders in the form of share repurchases and cash dividends. Our capital return program continues to be a key element of our capital allocation strategy. Let me turn to the outlook for the second quarter. Total revenue is expected to be $45 billion, plus or minus 2%. We expect modest sequential growth across all of our platforms. In the data center, we anticipate the continued ramp of Blackwell to be partially offset by a decline in China revenue. Note, our outlook reflects a loss in H20 revenue of approximately $8 billion for the second quarter. GAAP and non-GAAP gross margins are expected to be 71.8% and 72% respectively, plus or minus 50 basis points. We expect better Blackwell profitability to drive modest sequential improvement in gross margins. We are continuing to work towards achieving gross margins in the mid-seventies range late this year. GAAP and non-GAAP operating expenses are expected to be approximately $5.7 billion and $4 billion, respectively. We continue to expect full-year fiscal year 2026 operating expense growth to be in the mid-thirty percent range. GAAP and non-GAAP other income and expenses are expected to be an income of $450 million, excluding gains and losses from non-marketable and publicly held equity securities. GAAP and non-GAAP tax rates are expected to be 16.5%, plus or minus 1%, excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website, including a new financial information AI agent. Let me highlight upcoming events for the financial community. We will be at the B of A Global Technology Conference in San Francisco on June 4, the Rosenblatt Virtual AI Summit, and the Aztec Investor Conference in London on June 10, and GTC Paris at Viva Tech on June 11 in Paris. We look forward to seeing you at these events. Our earnings call to discuss the results of our second quarter of fiscal 2026 is scheduled for August 27. Well, now let me turn it over to Jensen to make some remarks. Jensen Huang: Thanks, Colette. We've had a busy and productive year. Let me share my perspective on some topics we're frequently asked. On export control, China is one of the world's largest AI markets and a springboard to global success. With half of the world's AI researchers based there, the platform that wins China is positioned to lead globally. Today, however, the $50 billion China market is effectively closed to US industry. The H20 export ban ended our Hopper data center business in China. We cannot reduce Hopper further to comply. As a result, we are taking a multibillion-dollar write-off on inventory that cannot be sold or repurposed. We are exploring limited ways to compete, but Hopper is no longer an option. China's AI moves on with or without US chips. It has to compute to train and deploy advanced models. The question is not whether China will have AI, it already does. The question is whether one of the world's largest AI markets will run on American platforms. Shielding Chinese chipmakers from US competition only strengthens them abroad and weakens America's position. Export restrictions have spurred China's innovation and scale. The AI race is not just about chips. It's about which stack the world runs on. As that stack grows to include 6G and quantum, US global infrastructure leadership is at stake. The US has based its policy on the assumption that China cannot make AI chips. That assumption was always questionable and now it's clearly wrong. China has enormous manufacturing capability. In the end, the platform that wins the AI developers wins AI. Export controls should strengthen US platforms, not drive half of the world's AI talent to rivals. On DeepSeq, DeepSeq and QN from China are among the best open-source AI models. Released freely, they've gained traction across the US, Europe, and beyond. Deepseeker One, like ChatGPT, introduced reasoning AI that produces better answers the longer it thinks. Reasoning AI enables step-by-step problem-solving, planning, and tool use, turning models into intelligent agents. Reasoning is compute-intensive and requires hundreds to thousands more tokens per task than previous one-shot inference. Reasoning models are driving a step function surge in inference demand. AI scaling laws remain firmly intact not only for training but now inference too, requiring massive scale compute. So DeepSeq also underscores the strategic value of open-source AI. When popular models are trained and optimized on US platforms, it drives usage, feedback, and continuous improvement, reinforcing American leadership across the stack. US platforms must remain the preferred platform for open-source AI. That means supporting collaboration with top developers globally, including in China. America wins when models like DeepSeq and QN run best on American infrastructure. Regarding onshore manufacturing, President Trump has outlined a bold vision to reshore advanced manufacturing, create jobs, and strengthen national security. Future plants will be highly computerized and roboticized. We share this vision. TSMC is building six fabs and two advanced packaging plants in Arizona to make chips for NVIDIA Corporation. Process qualification is underway, with volume production expected by year-end. Spill and Amcor are also investing in Arizona, constructing packaging, assembly, and test facilities. In Houston, we're partnering with Foxconn to construct a million-square-foot factory to build AI supercomputers. Wistron is building a similar plant in Fort Worth, Texas. To encourage and support these investments, we've made substantial long-term purchase commitments, a deep investment in America's AI manufacturing future. Our goal from chip to supercomputer, built in America, within a year. Each GB200 MBLink72 rack contains 1.2 million components and weighs nearly two tons. No one has produced supercomputers on this scale. Our partners are doing an extraordinary job. On AI diffusion rule, President Trump rescinded the AI diffusion rule, calling it counterproductive, and proposed a new policy to promote US AI tech with trusted partners. On his Middle East tour, he announced historic investments. I was honored to join him in announcing a 500-megawatt AI infrastructure project in Saudi Arabia and a 5-gigawatt AI campus in the UAE. President Trump wants US tech to lead. The deals he announced are wins for America, creating jobs, advancing infrastructure, generating tax revenue, and reducing the US trade deficit. The US will always be NVIDIA Corporation's largest market and home to the largest installed base of our infrastructure. Every nation now sees AI as core to the next industrial revolution, a new industry that produces intelligence and essential infrastructure for every economy. Countries are racing to build national AI platforms to elevate their digital capabilities. At Computex, we announced Taiwan's first AI factory in partnership with Foxconn and the Taiwan government. Last week, I was in Sweden to launch its first national AI infrastructure. Japan, Korea, India, Canada, France, the UK, Germany, Italy, Spain, and more are now building national AI factories to empower startups, industries, and societies. Sovereign AI is a new growth engine for NVIDIA Corporation. Toshiya, back to you. Thank you. Toshiya Hari: Operator, we will now open the call for questions. Can you please poll for questions? Sarah: Thank you. At this time, I would like to remind everyone in order to ask a question, please press the star key followed by the number one on your telephone keypad. Your first question comes from the line of Joe Moore with Morgan Stanley. Your line is open. Joe Moore: Great. Thank you. You guys have talked about this scaling up of inference around reasoning models for, you know, at least a year now, and we've really seen that come to fruition. As you talked about, we've heard it from your customers. Can you give us a sense for how much of that demand you know, you're able to serve and give us a sense for maybe how big the inference business is for you guys and, you know, do we need full-on NBL72 Rackscale solutions for reasoning inference going forward? Jensen Huang: Well, we would like to serve all of it. And I think we're on track to serve most of it. Grace Blackwell and VLINK72 is the ideal engine today, the ideal computer thinking machine, if you will, for reasoning AI. There's a couple of reasons for that. The first reason is that the token generation amount, the number of tokens reasoning goes through, is a hundred, a thousand times more than a one-shot chatbot. You know, it's essentially thinking to itself, breaking down a problem step by step. It might be planning multiple paths to an answer. It could be using tools, reading PDFs, reading web pages, watching videos, and then producing a result, an answer. The longer it thinks, the better the answer, the smarter the answer is. And so what we would like to do and the reason why Grace Blackwell was designed to give such a giant step up in inference performance is so that you could do all this and still get a response as quickly as possible. Compared to Hopper, Grace Blackwell is some forty times higher speed and throughput. And so this is going to be a huge benefit in driving down the cost while improving the quality of response with excellent quality of service at the same time. So that's the fundamental reason. That was a core driving reason for Grace Blackwell MBLink72. Of course, in order to do that, we had to reinvent literally redesign the entire way that these supercomputers are built. But now we're in full production. It's going to be exciting. It's going to be incredibly exciting. Sarah: The next question comes from Vivek Arya with Bank of America Securities. Your line is open. Vivek Arya: Thanks for the question. Just clarification for Colette first. So on the China impact, I think previously it was mentioned that at about $15 billion. So you had the $8 billion in Q2. So is there still some left as a headwind for the remaining quarters? How to model that? And then a question, Jensen, for you. Back at GTC, you had outlined a path towards almost a trillion dollars of AI spending over the next few years. Where are we in that build-out? And do you think it's going to be uniform that you will see every spender, whether it's CSP, sovereign enterprises, or build-out? Should we expect some periods of digestion in between? Just what are your customer discussions telling you about how to model growth for next year? Colette Kress: Yes, Vivek. Thanks so much for the question. Regarding H20, yes, we recognized $4.6 billion in H20 in Q1. We were unable to ship $2.5 billion, so the total for Q1 should have been $7 billion. When we look at our Q2, our Q2 is going to be meaningfully down in terms of China data center revenue. And we had highlighted in terms of the amount of orders that we had planned for H20 in Q2, and that was $8 billion. Now going forward, we did have other orders going forward that we will not be able to fulfill. That is what was incorporated, therefore, in the amount that we wrote down of the $4.5 billion. That write-down was about inventory and purchase commitments. And our purchase commitments were about what we expected regarding the orders that we had received. Going forward, though, it's a bigger issue regarding the amount of the market that we will not be able to serve. We assess that TAM to be close to about $50 billion in the future, as we don't have a product to enable for China. Jensen Huang: In fact, probably the best way to think through it is that AI is several things. Of course, we know that AI is this incredible technology that's going to transform every industry. You know, from, of course, the way we do software to healthcare and financial services to, you know, retail, to, I guess, every industry. Transportation, manufacturing. And we're at the beginning of that. But maybe another way to think about that is where do we need intelligence? Where do we need digital intelligence? And it's in every country. It's in every industry. And we know because of that, we recognize that AI is also an infrastructure. It's a way of developing a technology that requires factories. And these factories produce tokens. And they, as I mentioned, are important to every single industry in every single country. And so on that basis, we're really at the very beginning of it. Because the adoption of this technology is really kind of in its early stages. Now we've reached an extraordinary milestone. With AIs that are reasoning, are thinking, what people call inference time scaling. You know, of course, it created a whole new we've entered an era where inference is going to be a significant part of the compute workload. But anyhow, it's going to be a new infrastructure, and we're building it out in the cloud. The United States is really the early starter and available in US clouds. And this is our largest market, our largest installed base, and we can continue to see that happening. But beyond that, we're going to have to see AI go into enterprise, which is on-prem. Because so much of the data is still on-prem, access control is really important. It's really hard to move all of every company's data into the cloud. And so we're going to move AI into the enterprise. And you saw that we announced a couple of really exciting new products. Our RTX Pro enterprise AI server that runs everything enterprise and AI. Our DGX Spark and DGX Station, which is designed for developers who want to work on-prem. And so enterprise AI is just taking off. Telcos, today, a lot of the telco infrastructure will be in the future software-defined and built on AI. And so 6G is going to be built on AI. And that infrastructure needs to be built out. And as I said, it's very, very early stages. And then, of course, every factory today that makes things will have an AI factory that sits with it. And the AI factory is going to be creating AI and operating AI for the factory itself, but also to power the products and the things that are made by the factory. So it's very clear that every company will have AI factories, and very soon, there'll be robotics companies, robot companies, and those companies will be also building AIs to drive the robots. So we're at the beginning of all of this build-out. Sarah: The next question comes from C.J. Muse with Cantor Fitzgerald. Your line is open. C.J. Muse: Yeah. Good afternoon. Thank you for taking the question. There have been many large GPU cluster investments announced in the last month, and you alluded to a few of them with Saudi Arabia, the UAE. And then also, you know, we've heard from Oracle and XAI, just to name a few. So my question is, are there others that have yet to be announced of the same kind of scale and magnitude? And perhaps more importantly, how are these orders impacting your lead times for Blackwell and your current visibility sitting here today, you know, almost halfway through 2025? Jensen Huang: Well, we have more orders today than we did at the last time I spoke about orders at GTC. However, we're also increasing our supply chain and building out our supply chain. They're doing a fantastic job. We're building it here onshore in the United States. But we're going to keep our supply chain quite busy for several many more years coming. And with respect to further announcements, I'm going to be on the road next week through Europe. And it's just about every country needs to build out AI infrastructure, and there are umpteen AI factories being planned. I think in the remarks, Colette mentioned there's some 800 AI factories being built. There's a whole bunch that haven't been announced. And I think the important concept here, which makes it easier to understand, is that like other technologies that impact literally every single industry, of course, electricity was one, and it became infrastructure. Of course, the information infrastructure, which we now know as the Internet, affects every single industry, every country, every society. Intelligence is surely one of those things. I don't know any company, industry, country who thinks that intelligence is optional. It's essential infrastructure. And so we've now digitalized intelligence. And so I think we're clearly in the beginning of the build-out of this infrastructure. And every country will have it. I'm certain of that. Every industry will use it. That, I'm certain of. And what's unique about this infrastructure is that it needs factories. You know, it's a little bit like the energy infrastructure. Electricity. It needs factories. We need factories to produce this intelligence. And the intelligence is getting more sophisticated. We were talking about earlier that we had a huge breakthrough in the last couple of years with reasoning AI, and now there are agents that reason. And there are super agents that use a whole bunch of tools, and then there's clusters of super agents where agents are working with agents solving problems. So you could just imagine compared to one-shot chatbots, and the agents that are now using AI, built on these large language models, much more compute-intensive, they really need to be in R. And so I think we're in the beginning of the build-out. And there should be many, many more announcements in the future. Sarah: Your next question comes from Ben Reitzes with Melius. Your line is open. Ben Reitzes: Yeah. Hi. Thanks for the question. I wanted to ask, you know, first to Colette, a little clarification around the guidance and maybe putting it in a different way. The $8 billion for H20 just seems like, you know, it's roughly $3 billion more than most people thought with regard to what you'd be foregoing in the second quarter. So that would mean that, you know, with regard to your guidance, the rest of the business, you know, in order to hit $45 billion, is doing $2 to $3 billion or so better. So, you know, I was wondering if that math made sense to you. And then, you know, in terms of the guidance, that would imply the non-China business is doing a bit better than the Street expected. So, you know, wondering, you know, what the primary driver was there in your view. And then this second part of my question, you know, Jensen, I know you guide one quarter at a time, but with regard to the AI diffusion rule being lifted and this momentum with sovereign, you know, there's been times in your history where you've said on calls like this where you know, you have more conviction in sequential growth throughout the year, etcetera. And given the unleashing of demand with AI diffusion being, you know, revoked and the supply chain increasing, you know, does the environment give you more conviction in sequential growth as we go throughout the year? So first one for Colette, and then next one for Jensen. Thanks so much. Colette Kress: Thanks, Ben, for the question. When we look at our Q2 guidance and our commentary that we provided, that had the export controls not occurred, we would have had orders about $8 billion for H20. That's correct. That was a possibility for what we would have had in our outlook for this quarter in Q2. So what we also have talked about here is the growth that we've seen in Blackwell. Blackwell across many of our customers as well as the growth that we continue to have in terms of supply that we need for our customers. So putting those together, that's where we came through with the guidance that we provided. I'm going to turn the rest over to Jensen to see how he wants to Thanks. Thanks. Jensen Huang: Thanks, Ben. I would say compared to the beginning of the year, compared to GTC time frame, there are four positive surprises. The first positive surprise is the step function demand increase of reasoning AI. I think it is fairly clear now that AI is going through exponential growth. And reasoning AI really busted through. Concerns about hallucination or its ability to really solve problems, and I think a lot of people are crossing that barrier and realizing how incredibly effective agentic AI is. And reasoning AI is. So number one is inference reasoning. And the exponential growth there. Demand growth. The second one you mentioned AI diffusion. It's really terrific to see that the AI diffusion role was rescinded. President Trump wants America to win. And he also realizes that we're not the only country in the race. And he wants the United States to win. And recognizes that we have to get the American stack out to the world and have the world build on top of American stacks instead of alternatives. And so AI diffusion happened. The rescinding of it happened at almost precisely the time that the countries around the world are awakening to the importance of AI as an infrastructure, not just as a technology of great curiosity and great importance, but infrastructure for their industries and startups and society. Just as they had to build out infrastructure for electricity and the Internet, you've got to build out infrastructure for AI. I think that's an awakening. And that creates a lot of opportunity. The third is enterprise AI. Agents work. And agents are doing these agents are really quite successful. Much more than generative AI, agentic AI, is game-changing. You know, agents can understand ambiguous and rather implicit instructions and are able to problem solve and use tools and have memory and so on. And so I think enterprise AI is ready to take off, and it's taken us a few years to build a computing system that is able to integrate, run enterprise AI stacks, run enterprise IT stacks, but add AI to it. And this is the RTX Pro enterprise server that we announced at Computex just last week. And just about every major IT company has joined us, and I'm super excited about that. And so computing is one part of it. But remember, enterprise IT is really three pillars. It's compute, storage, and networking. And we've now put all three of them together finally, and we're going to market with that. And then lastly, industrial AI. Remember, one of the implications of the world reordering, if you will, is regions onshoring manufacturing and building plants everywhere. In addition to AI factories, of course, there are new electronics manufacturing, chip manufacturing, being built around the world. And all of these new plants and these new factories are creating exactly the right time when Omniverse and AI and all the work that we're doing with robotics is emerging. And so this fourth pillar is quite important. Every factory will have an AI factory associated with it. And in order to create these physical AI systems, you really have to train a vast amount of data. So back to more data, more training, more AIs to be created, more computers. And so these four drivers are really kicking into turbocharge. Sarah: Your next question comes from Timothy Arcuri with UBS. Your line is open. Timothy Arcuri: Thanks a lot. Jensen, I wanted to ask about China. It sounds like the July guidance assumes there's no SKU replacement for the H20. But if the president wants the US to win, it seems like you're going to have to be allowed to ship something into China. So I guess I had two points on that. First of all, have you been approved to ship a new modified version into China? And are you currently building it, but you just can't ship it in fiscal Q2? And then you were sort of run rating $7 to $8 billion a quarter into China. Can we get back to those sorts of quarterly run rates once you get something that you're, you know, allowed to ship back into China? I think we're all trying to figure out how much to add back to our models and when. So, you know, whatever you can say there would be great. Thanks. Jensen Huang: The president has a plan. He has a vision. I trust him. With respect to our export controls, it's a set of limits. And the new set of limits pretty much makes it impossible for us to reduce Hopper any further, you know, for any productive use. And so the new limits, the new limits, you know, it's kind of the end of the road for Hopper. We have some limited options, and so the key is to understand the limits. The key is to understand the limits and see if we can come up with interesting products that could continue to serve the Chinese market. We don't have anything at the moment. But we're considering it. We're thinking about it. Obviously, the limits are quite stringent at the moment. And we have nothing to announce today. And when the time comes, you know, we'll engage the administration and discuss that. Sarah: Your final question comes from the line of Aaron Rakers with Wells Fargo. Your line is open. Jake: Hi. This is Jake on for Aaron. Thanks for taking the question and congrats on a great quarter. I was wondering if you could give some additional color around the strength you saw within the networking business, particularly around the adoption of your Ethernet solutions at CSPs, as well as any change you're seeing in network attach rates. Jensen Huang: Yeah. Thank you for that. We now have three networking platforms. Maybe four. The first one is the scale-up platform. To turn a computer into a much larger computer. Scaling up is incredibly hard to do. Scaling out is easier to do, but scaling up is hard to do. And that platform is called NVLink. And NVLink comes with it chips and switches and enabling spines, and it's really complicated. But anyways, that's our new platform. Scale-up platform. In addition to InfiniBand, we also have SpectrumX. We've been fairly consistent that Ethernet was designed for a lot of traffic that is independent. But in the case of AI, you have a lot of computers working together. And the traffic of AI is insanely bursty. Latency matters a lot because the AI is thinking. And it wants to get worked on as quickly as possible. And you've got a whole bunch of nodes working together. And so we enhanced Ethernet, added capabilities like extremely low latency, congestion control, adaptive routing, the type of technologies that were available only in InfiniBand to Ethernet. And as a result, we improved the utilization of Ethernet in these clusters. These clusters are gigantic. From as low as 50% to as high as 85%, 90%. And so the difference is if you had a cluster that's $10 billion, and you improved its effectiveness by 40%, that's worth $4 billion. It's incredible. And so Spectrum X has been really quite frankly a home run in this last quarter. As we said in the prepared remarks, we added two very significant CSPs to the Spectrum X adoption. And then the last one is BlueField, which is our control plane. And so in those four, the control plane and network, which is used for storage, is used for security, and for many of these clusters that want to achieve isolation among its users, multi-tenant clusters, and still be able to use and have extremely high performance, bare metal performance, BlueField is ideal for that and is used in a lot of the key cases. So we have these four networking platforms. They're all growing. And we're doing really well. I'm very proud of the team. And so over to you. Sarah: That is all the time we have for questions. Jensen, I will turn the call back to you. Jensen Huang: Thank you. This is the start of a powerful new wave of growth. Grace Blackwell is in full production. We're off to the races. We now have multiple significant growth engines. Inference, once the light of workload, is surging with revenue-generating AI services. AI is growing faster and will be larger than any platform shifts before, including the Internet, mobile, and cloud. Blackwell is built to power the full AI lifecycle, from training frontier models to running complex inference and reasoning agents at scale. Training demand continues to rise with breakthroughs in post-training and reinforcement learning and synthetic data generation. But inference is exploding. Reasoning AI agents require orders of magnitude more compute. The foundations of our next growth platforms are in place and ready to scale. Sovereign AI, nations are investing in AI infrastructure like they once did for electricity and the Internet. Enterprise AI must be deployable on-prem and integrated with existing IT. Our RTX Pro, DGX Spark, and DGX Station Enterprise AI systems are ready to modernize the $500 billion IT infrastructure on-prem or in the cloud. Every major IT provider is partnering with us. Industrial AI, from training to digital twin simulation to deployment, NVIDIA Corporation's Omniverse and Isaac Groot are powering next-generation factories and humanoid robotic systems worldwide. The age of AI is here. From AI infrastructures, inference at scale, sovereign AI, enterprise AI, and industrial AI, NVIDIA Corporation is ready. Join us at GTC Paris. I'll keynote at Viva Tech on June 11, talking about quantum GPU computing, robotic factories, and robots, and celebrate our partnerships building AI factories across the region. The NVIDIA Corporation van will tour France, the UK, Germany, and Belgium. Thank you for joining us at the earnings call today. See you in Paris. Sarah: This concludes today's conference call. You may now disconnect.

Christa: Good afternoon. My name is Christa, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA Corporation's Fourth Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question and answer session. If you would like to ask a question during this time, simply press star followed by the number one on your telephone keypad. And if you would like to withdraw your question, Thank you. Stewart Stecker. You may begin your conference. Thank you. Stewart Stecker: Good afternoon, everyone, and welcome to NVIDIA Corporation's conference call for the fourth quarter of fiscal 2025. With me today from NVIDIA Corporation are Jensen Huang, president and chief executive officer, and Colette Kress, executive vice president and chief financial officer. I'd like to remind you that our call is being webcast live on NVIDIA Corporation's Investor website. Webcast will be available for replay until the conference call discuss our financial results, the first quarter of fiscal 2026. The content of today's call is NVIDIA Corporation's property. It can't be reproduced or transcribed without prior written consent. During this call, we may make forward-looking statements based on current expectations, these are subject to a number of significant risks and uncertainties and our actual results may differ materially. A discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release. Our most recent forms 10-K and 10-Q, and the reports that we may file on form 8-K with the Securities and Exchange Commission. All our statements are made as of today, February 26, 2025, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. Confined and reconciliation of these non-GAAP financial measures GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Stewart. Q4 was another record quarter. Revenue of $39.3 billion was up 12% sequentially and up 78% year on year. And above our outlook, of $37.5 billion. For fiscal 2025, revenue was $130.5 billion. Up 114% in the prior year. Let's start with data center. Data center revenue for fiscal 2025 was $115.2 billion. More than doubling from the prior year. In the fourth quarter, it is in a revenue of $35.6 billion was a record, up 16% sequentially and 93% year on year. As the Blackwell ramp commenced, and Hopper 200 continued to contribute growth, In Q4, Blackwell sales exceeded our expectations. We delivered $11 billion of Blackwell revenue to meet strong demand. This is the fastest product ramp in our company's history. Unprecedented in its speed and scale. Blackwell production is in full gear across multiple configurations, and we are increasing supply quickly. Expanding customer adoption. Our Q4 data center compute revenue jumped 18% sequentially and over 2x year on year. Customers are racing to scale infrastructure to train the next generation of cutting-edge models and unlock the next level of AI capabilities. With Blackwell, it will be common for these clusters to start with 100,000 GPUs or more. Shipments have already started for multiple infrastructures of this size. Post-training and model customization are fueling demand for NVIDIA Corporation infrastructure and software as developers and enterprises leverage techniques such as fine-tuning, reinforcement learning, and distillation to tailor models for domain-specific use cases. Hugging Face alone hosts over 90,000 derivatives traded from the Llama Foundation model. The scale of post-training and model customization is massive and can collectively demand orders of magnitude more compute than pretraining. Our inference demand is accelerating. Driven by test time scaling and new reasoning models. Like OpenAI's O3, DeepSeq R1, and Grok 3. Long-thinking reasoning AI can require 100x more compute per task compared to one-shot inferences. Blackwell was architected for reasoning AI inference. Blackwell supercharges reasoning AI models with up to 25x higher token throughput and 20x lower cost versus Hopper 100. It is revolutionary. Transformer engine is built for LLM. And mixer of experts inference. And its NVLink domain delivers 14x the throughput of PCIe Gen 5. Ensuring the response time, throughput, and cost efficiency needed to tackle the growing complexity of inferences scale. Companies across industries are tapping into NVIDIA Corporation's full-stack inference platform to boost performance and slash cost. Now tripled inference throughput and cut cost by 66% using NVIDIA Corporation TensorRT for its screenshot feature. Perplexity sees 435 million monthly queries and reduced its inference costs 3x with NVIDIA Corporation Triton inference server and TensorRT LLM. Microsoft Bing achieved a 5x speedup at major TCO savings for visual search across billions of images with NVIDIA Corporation, TensorRT, and acceleration libraries. Blackwell has great demand for inference. Many of the early GV200 deployments are earmarked for inference. A first for a new architecture. Blackwell addresses the entire AI market from pretraining, post-training, to inference across clouds, to on-premise, to enterprise. Its programmable architecture accelerates every AI model and over 4,400 applications ensuring large infrastructure investments against obsolescence in rapidly evolving markets. Our performance and pace of innovation are unmatched. We're driven to a 200% reduction in inference cost in just the last two years. We delivered the lowest TCO and the highest ROI. And full-stack optimizations for NVIDIA Corporation and our large ecosystem including 5.9 million developers continuously improve our customers' economics. In Q4, large CSPs represented about half of our data center revenue. And these sales increased nearly 2x year on year. Large CSPs were some of the first to stand up Blackwell, with Azure, GCP, AWS, and OCI, bringing GV200 systems to cloud regions around the world to meet surging customer demand for AI. Regional cloud hosting NVIDIA Corporation GPUs increased as a percentage of data center revenue. Reflecting continued AI factory build-outs globally and rapidly rising demand for AI reasoning models and agents. Coreweave launched a 100,000 GV200 cluster-based instance with NVLink switch and Quantum-2 InfiniBand. Consumer Internet revenue grew 3x year on year. Driven by an expanding set of generative AI and deep learning use cases. These include recommender systems, vision language understanding, synthetic data generation search, and agentic AI. For example, XAI is adopting the GV200 to train and inference its next generation of Grok AI models. Meta's cutting-edge Andromeda, advertising engine runs on NVIDIA Corporation's Grace Hopper Superchip. Serving vast quantities of ads across Instagram, Facebook applications. Andromeda harnesses Grace Hopper's fast interconnect and large memory to boost inference, throughput by 3x. Enhanced ad personalization, and deliver meaningful jumps in monetization and ROI. Enterprise revenue increased nearly 2x year on accelerating demand model fine-tuning. Agentic AI workflows. And GPU-accelerated data processing. We introduced NVIDIA Corporation Llama Numitron model family nodes to help developers create and deploy AI agents across a range of applications, including customer support, fraud detection, and product supply chain and inventory management. Leading AI agent platform providers, including SAP and ServiceNow, are among the first to use new models. Health care leaders, IQVIA, and Lumenon. And Mayo Clinic as well as ARC and Institute are using NVIDIA Corporation AI to speed drug discovery enhance genomic research, and pioneer advanced health care services with generative and agentic AI. As AI expands beyond the digital world, NVIDIA Corporation infrastructure and software platforms are increasingly being adopted to power robotics and physical AI development. One of the early and largest robotics applications and autonomous vehicles were virtually every AV company is developing on NVIDIA Corporation, in the data center. NVIDIA Corporation's automotive vertical revenue is expected to grow to approximately $5 billion this fiscal year. At CES, Hyundai Motor Group announced it is adopting NVIDIA Corporation Technologies to accelerate AV and robotics development and smart factory initiatives. Vision transformers, self-supervised learning, multimodal sensor fusion, and high-fidelity simulation are driving breakthroughs in AV development and will require 10x more compute. At TDX, we announced the NVIDIA Corporation Cosmos World foundation model platform. Just as language foundation models have revolutionized language AI, Cosmos is a physical AI to revolutionize robotics. The robotics and automotive companies, including ride-sharing giant Uber, are among the first to adopt the platform. From a geographic perspective, sequential growth in our data center revenue was strongest in the US, driven by the initial ramp of Blackwell. Countries across the globe are building their AI ecosystems and demand for compute infrastructure is surging. France's €200 billion AI investment and the EU's €200 billion Invest AI initiative offer a glimpse into the build-out that will redefine global AI infrastructure in the coming years. Now as a percentage of total data center revenue, data center sales in China remained well below levels seen onset of export controls. China shipments absent any change in regulations, we believe that will remain roughly at the current percentage. The market in China for data center solutions remained very competitive. We will continue to comply with export controls while serving our customers. Networking revenue declined 3% sequentially. Our networking attached to GPU compute systems is robust at over 75%. We are transitioning from small NVLink 8 with InfiniBand to large NVLink 72. The Spectrum X. Spectrum X and NVLink switch revenue increased and represents a major new growth sector. We expect networking a return to growth in Q1. AI requires a new class of networking. NVIDIA Corporation offers NVLink switch systems for scale of compute. For scale out, we offer Quantum InfiniBand for HPC supercomputers, and SpectrumX for Ethernet environments. Spectrum X enhances the Ethernet for AI computing and has been a huge success. Microsoft Azure OCI, Fortease, and others are building large AI factories with SpectrumX. The first Stargate data centers will use Spectrum X. Yesterday, Cisco announced integrating Spectrum X into their networking portfolio to help enterprises build AI infrastructure. With its large enterprise footprint and global reach, Cisco will bring NVIDIA Corporation Ethernet to every industry. Now moving to gaming and AR PCs. Gaming revenue of $2.5 billion decreased 22% sequentially and 11% year on year. Full year revenue of $11.4 billion increased 9% year on year. And demand remains strong throughout the holiday. However, Q4 shipments were impacted by supply constraints. We expect strong sequential growth in Q1 as supply increases. The new GeForce RTX 50 series desktop and laptop GPUs are here. Built for gamers, creators, and developers, they fuse AI and graphics, redefining visual computing. Powered by the Blackwell architecture, fifth-generation tensor cores, and fourth-generation RT cores and featuring up to 3,400 AI TOPS. These GPUs deliver a 2x performance leap and new AI-driven rendering, including neural shaders, digital human technologies, geometry, and lighting. The new DLSS 4 boosts frame rates up to 8x with AI-driven frame generation turning one rendered frame into three. It also features the industry's first real-time application of transformer models packing 2x more parameters and 4x to compute unprecedented visual fidelity. We also announced a wave of GeForce Blackwell laptop GPUs with new NVIDIA Corporation Max-Q technology that extends battery life, by up to an incredible 40%. These laptops will be available starting in March from the world's top manufacturers. Moving to our professional visualization business. Revenue of $511 million was up 5% sequentially and 10% year on year. Full year revenue of $1.9 billion increased 21% year on year. Key industry verticals driving demand include automotive and health care. NVIDIA Corporation Technologies and generative AI are reshaping design engineering, and simulation workloads. Increasingly, these technologies are being leveraged in leading software platforms. From ANSYS, Cadence, and Siemens fueling demand for NVIDIA Corporation RTX workstations. Now moving to automotive. Revenue was a record $570 million, up 27% sequentially and up 103% year on year. Full year revenue of $1.7 billion increased 55% year on year. Strong growth was driven by the continued ramp in autonomous vehicles, including cars and robotaxis. At CES, we announced Toyota the world's largest automaker will build its next generation vehicles on NVIDIA Corporation Oren, running the safety-certified NVIDIA Corporation Drive OS. We announced Aurora and Continental. Will deploy driverless trucks at scale powered by NVIDIA Corporation Drive 4. Finally, our end-to-end autonomous vehicle platform, NVIDIA Corporation DRIVE Hyperion, has passed industry safety assessments by Ryland, two of the industry's foremost authorities, automotive-grade safety and cybersecurity, NVIDIA Corporation is the first AV platform to receive a comprehensive set of third-party assessments. Moving to the rest of the P&L. GAAP gross margins, was 73%. And non-GAAP gross margins were 73.5%. Down sequentially as expected with our first deliveries of the Blackwell architecture. As discussed last quarter, Blackwell is a customizable AI infrastructure with several different types of NVIDIA Corporation build chips. Multiple networking options, and for air and liquid-cooled data center. We exceeded our expectations in Q4, in ramping Blackwell, increasing system availability, providing several configurations to our customers. As Blackwell ramps, we expect gross margins to be in the low seventies. We initially, we are focused on expediting the manufacture as they race to build out Blackwell infrastructure. When fully ramped, we have many opportunities to improve the cost and gross margin. Will improve and return to the mid-seventies. Late this fiscal year. Sequentially, GAAP operating expenses were up 9% and non-GAAP operating expenses were 11%, reflecting higher engineering development costs and higher compute and infrastructure costs for new product introductions. In Q4, we returned $8.1 billion to shareholders, the form of share repurchases cash dividends. Let me turn to the outlook in the first quarter. Total revenue is expected to be $43 billion. Plus or minus 2%. Continuing with its strong demand, we expect a significant ramp of Blackwell in Q1. We expect sequential growth. In both data center and gaming. Within data center, we expect sequential growth from both. Compute and networking. GAAP and non-GAAP gross margins are expected to be 70.6%. And 71% respectively. Plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $5.2 billion and $3.6 billion. We expect full year fiscal year 2026 operating expenses grow to grow to be in the mid-thirties. GAAP and non-GAAP other incoming expenses are expected to be an income of approximately $400 million. Excluding gains and losses, from non-marketable and publicly held equity securities. GAAP and non-GAAP tax rates are expected to be 17% plus or minus 1% excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website. Including a new financial information AI agent. In closing, let me highlight upcoming events for the financial community. We will be at the TD Cowen Healthcare Conference in Boston on March 3rd. And at the Morgan Stanley Technology, Media, and Telecom Conference in San Francisco. On March 5th. Please join us for our annual GTC conference starting Monday, March 17th, in San Jose, California. Jensen will deliver a news-packed keynote on March 18th, and we will host a Q&A session for our financial analysts. Next day, March 19th. We look forward to seeing you at these events. Our earnings call to discuss the results for our first quarter of fiscal 2026 is scheduled for May 28th, 2025. We are going to open up the call, operator. To questions. If you could start that, that would be great. Christa: Thank you. At this time, I would like I also ask that you please limit yourself to one question. For any additional questions, please requeue. And your first question comes from C.J. Muse with Cantor Fitzgerald. Please go ahead. C.J. Muse: Yeah. Good afternoon. Thank you for taking the question. I guess, for me, Jensen, as test time compute and reinforcement learning shows such promise, we're clearly seeing increasing blurring in the lines between training and inference. What does this mean for the potential future of potentially inference-dedicated clusters? And how do you think about the overall impact to NVIDIA Corporation and your customers? Thank you. Jensen Huang: Yeah. I appreciate that, C.J. There are now multiple scaling laws. There's the pretrained scaling laws. And that's gonna continue to scale because we have multimodality. We have data that came from reasoning that are now used to pretraining. And then the second is post-training scaling law. Using reinforcement learning human feedback, reinforcement learning AI feedback, reinforcement learning verifiable rewards, the amount of computation you use for post-training is actually higher than pretraining. And it's kinda sensible in the sense that you could while you're using reinforcement learning, generate an enormous amount of synthetic data or synthetically generated tokens. AI models are basically generating tokens to train AI models. That's post-train. And the third part, this is the part that you mentioned, is test time compute or reasoning. Long thinking, inference scaling, basically the same ideas. And there's you have chain of thought, you have search. The amount of tokens generated, the amount of inference compute needed, is already a hundred times more than the one-shot examples and the one-shot capabilities of large language models in the beginning and that's just the beginning. This is just the beginning. The idea that the next generation could have thousands of times and even hopefully extremely thoughtful and simulation-based and search-based models that could be hundreds of thousands, millions of times more compute than today, is in our future. And so the question is how do you design such an architecture? Some of the models are autoregressive. Some of the models are diffusion-based. Some of the times you want your data center to have disaggregated inference. Sometimes it's compacted. And so it's hard to figure out what is the best configuration of a data center, which is the reason why NVIDIA Corporation's architecture is so popular. We run every model. We are great at training. The vast majority of our compute today is actually inference, and Blackwell takes all of that to a new level. We designed Blackwell with the idea of reasoning models in mind. And you look at training, it's many times more performant. But what's really amazing is for long-thinking, test time scaling reasoning AI models, we're tens of times faster, 25 times higher throughput. And so Blackwell is gonna be incredible across the board. And when you have a data center, that allows you to configure and use your data center based on are you doing more pretraining now, post-training now? Or scaling out your inference our architecture is fungible, and easy to use. In all of those different ways. And so we're seeing, in fact, much, much more concentration of a unified architecture than ever before. Christa: Your next question comes from the line of Joseph Moore with JPMorgan. Please go ahead. Joseph Moore: I wonder if you could talk about GV200 at CES. You sort of talked about the complexity of the rack-level systems and the challenges you have. And then as you said in the prepared remarks, we've seen a lot of general availability. You know, where are you in terms of that ramp? Are there still bottlenecks to consider at a systems level above and beyond the chip level? And just you know, have you maintained your enthusiasm for the NVLink 72 platforms? Jensen Huang: Well, I'm more enthusiastic today than I was at CES. And the reason for that is because we shipped a lot more to CES. We have some 350 plants manufacturing the one and a half million components that go into each one of the Blackwell racks. Base Blackwell racks. Yes. It's extremely complicated. And we successfully and incredibly ramped up Grace Blackwell. Delivering some $11 billion of revenues last quarter. We're gonna have to continue to scale as demand is quite high and customers are anxious and impatient to get their Blackwell systems. You'd probably seen on the web a fair number of celebrations about Grace Blackwell Systems coming online and we have them, of course. We have a fairly large installation of Grace Blackwell for our own engineering and our own design teams and software teams. Coreweave has now gone public about the successful bring-up of theirs. Microsoft has. Of course, OpenAI has. And you're starting to see many come online. So I think the answer to your question is nothing is easy about what we're doing. But we're doing great, and all of our partners are doing great. Christa: Your next question comes from the line of Vivek Arya with Bank of America Securities. Please go ahead. Vivek Arya: Thank you for taking my question. Could I just you wouldn't mind confirming if Q1 is the bottom for gross margins? And then, Jensen, my question is for you. What is on your dashboard to give you the confidence that the strong demand can sustain into next year and has DeepSeq and whatever innovations they came up with, has that changed that view in any way? Thank you. Colette Kress: Let me first take the first part of the question. Regarding the gross margin. During our Blackwell ramp, our gross margins will be in the low seventies. At this point, we are focusing on expediting our manufacturing. Expediting our manufacturing is to make sure that we can provide customers as soon as possible. Our Blackwell is fully ramped. And once it does, I'm sorry. Blackwell fully ramps, we can improve our cost and our gross margin. So we expect to probably be in the mid-seventies later this year. You know, walking through what you heard, Jensen speak about the systems and their complexity. They are customizable in some cases. They've got multiple networking options. Have liquid cool and water-cooled. So we know there is an opportunity for us to improve these gross margins going forward. But right now, we are gonna focus on getting the manufacturing plate into our customers as soon as possible. Jensen Huang: We know several things, Vivek. We have a fairly good line of sight of the amount of capital investment that data centers are building out towards. We know that going forward, the vast majority of software is gonna be based on machine learning. And so accelerated computing and generative AI, reasoning AI, are going to be the type of architecture you want in your data center. We have, of course, forecast and plans from our top partners. And we also know that there are many innovative really exciting start-ups that are still coming online. As new opportunities for developing the next breakthroughs in AI, whether it's agentic AIs, reasoning AIs, or physical AIs. The number of start-ups are still quite vibrant and each one of them needs a fair amount of computing infrastructure. So I think the whether it's the near-term signals or the mid-term signals. Near-term signals, of course, are, you know, POs and forecasts and things like that. Mid-term signals, would be the level of infrastructure and CapEx scale out compared to previous years. And then the long-term signals it has to do with the fact that we know fundamentally software has changed. From hand coding that runs on CPUs through machine learning and AI-based software that runs on GPUs and accelerated computing systems. So we have a fairly good sense that this is the future of software. And then maybe as you roll it out, another way to think about that is we've really only touched consumer AI and search and some amount of consumer generative AI. Advertising, recommenders, kind of the early days of software. The next wave's coming. Agentic AI for enterprise, physical AI for robotics. And Sovereign AI has different regions build out their AI for their own ecosystems. And so each one of these are barely off the ground, and we can see them. We can see them because, you know, obviously, we're in the center of much of this development. And we can see great activity happening in all these different places. And these will happen. So near-term, mid-term, long-term. Christa: Your next question comes from the line of Matt Ramsay with Cowen. Please go ahead. Matt Ramsay: Yeah. Good afternoon. Thanks for taking my question. Your next generation Blackwell Ultra is set to launch in the second half of this year. In line with the team's annual product cadence. Jensen, can you help us understand the demand dynamics for Ultra given that you'll still be ramping the current generation Blackwell solutions? How do your customers and the supply chain also manage the simultaneous ramps of these two products and is the team still on track to execute Blackwell Ultra in the second half of this year? Jensen Huang: Yes. Blackwell Ultra is second half. As you know, the first Blackwell was have we had a hiccup? That probably cost us a couple of months. We're fully recovered, of course. The team did an amazing job recovery. And all of our supply chain partners and just so many people helped us recover at the speed of light. And so now we've successfully ramped production of Blackwell. But that doesn't stop the next train. The next train is you know, it's on an annual rhythm. And, Blackwell Ultra with, new networking, new memories, and, of course, new processors and all of that is coming online. We've been working with all of our partners and customers laying this out. They have all of the necessary information. And we'll work with everybody to do the proper transition. This time between Blackwell, Blackwell Ultra, the system architecture is exactly the same. It's a lot harder going from Hopper to Blackwell because we went from an NVLink 8 system to a NVLink 72 base system. So the chassis, the architecture of the system, the hardware, the power delivery, all of that had to change. This was quite a challenging transition. But the next transition will slot right in. Grace Blackwell Ultra will slot right in. We've also already revealed and been working very closely with all of our partners on the click after that. And the click after that is called Vera Rubin. And, all of our partners are getting up to speed on the transition of that. And so preparing for that transition and, again, we're gonna provide a big, big, huge step up. And so come to GTC, and I'll hold on to you about Blackwell Ultra, Vera Rubin, and then show you what's the one click after that. Really, really exciting new product, so come to GTC, please. Christa: Your next question comes from the line of Timothy Arcuri with UBS. Please go ahead. Timothy Arcuri: Thanks a lot. Jensen, we hear a lot about custom ASICs. Can you kinda speak to the balance between custom ASIC and merchant GPU? We hear about some of these heterogeneous super clusters to use both GPU and ASIC. Is that something customers are planning on building or will these infrastructures remain fairly distinct? Thanks. Jensen Huang: Well, we build very different things than ASICs. In some ways, completely different in some areas we intercept. We're different in several ways. One, NVIDIA Corporation's architecture is general. You know, whether you've optimized for autoregressive models or diffusion-based models or vision-based models or multimodal models or text models. We're great in all of it. We're great in all of it because our software stack is so our architecture is responsible. Our software stack is ecosystem is so rich that we're the initial target of, you know, most exciting innovations and algorithms. And so by definition, we're much, much more general than narrow. We're also really good from the end to end. From data processing, the curation of the training data, to the training of the data, of course, to reinforcement learning used in post-training. All the way to inference with test time scaling. So, you know, we're general. We're end to end. And we're everywhere. And because we're not in just one cloud, we're in every cloud, we could be on-prem. We could be in, you know, in a robot. Our architecture is much more accessible. And a great target initial target for anybody who's starting up a new company. And so we're everywhere. And then the third thing I would say is that our performance and our rhythm is so incredibly fast. Remember that these data centers are always fixed in size. They're fixed in size or they're fixed in power. And if our performance per watt is anywhere from 2x to 4x to 8x, which is not unusual. It translates directly to revenues. And so if you have a 100-megawatt data center, if the performance or the throughput that 100-megawatt or that gigawatt data center is four times or eight times higher your revenues for that gigawatt data center is eight times higher. And the reason that is so different than data centers of the past is because AI factories are directly monetizable through its tokens generated. And so the token throughput of our architecture being so incredibly fast is just incredibly valuable to all of the companies that are building these things for revenue generation reasons. And capturing the fast ROIs. So I think the third reason is performance. And then the last thing that I would say is the software stack is incredibly hard. Building an ASIC is no different than what we do. We have to build a new architecture. And the ecosystem that sits on top of our architecture is ten times more complex today than it was two years ago. And that's fairly obvious because the amount of software this world building on top of architecture is growing exponentially and AI is advancing very quickly. So bringing that whole ecosystem on top of multiple chips is hard. And so I would say that those four reasons and then finally, I will say this. Just because the chip is designed doesn't mean it gets deployed. And you've seen this over and over again. There are a lot of chips that get built. But when the time comes a business decision has to be made. And that business decision is about deploying a new engine, a new processor into a limited AI factory in size and power and find. And our technology is, you know, not only more advanced, more performant, it has much, much better software capability, and very importantly, our ability to deploy is lightning fast. And so these things are enough for the faint of heart as everybody knows now. And so there's a lot of different reasons why we do well. Why we win. Christa: Your next question comes from the line of Ben Reitzes with Melius Research. Please go ahead. Ben Reitzes: Yeah. Hi. Ben Reitzes here. Hey. Thanks a lot for the question. Hey, Jensen. It's a geography-related question. You know, you did a great job explaining some of the demand underlying, you know, factors here on the strength. But the US was up about $5 billion or so sequentially. And I think, you know, there is a concern about whether the US can pick up the slack if there's regulations towards other geographies. And I was just wondering as we go throughout the year, you know, if this kind of surge in the US continues and it's gonna be whether that's okay. And if that underlies your growth rate, how can you keep growing so fast with this mix shift towards the US? Your guidance looks like China is probably up sequentially. So just wondering if you could go through that dynamic and maybe Colette can weigh in. Thanks a lot. Jensen Huang: China is approximately the same percentage as Q4. And as in as previous quarters. It's about half of what it was before the export control. But it's approximately the same in percentage. With respect to geographies, the takeaway is that AI is software. It's modern software. It's incredible modern software. But it's modern software. And AI has gone mainstream. AI is used in delivery services everywhere, shopping services everywhere. You know? You were to buy a quart of milk is delivered to you. AI was involved. And so almost everything that a consumer service provides AI's at the core of it. Every student will use AI as a tutor. Health care services use AI. Financial services use AI. No fintech company will not use AI. Every fintech company will. Climate tech company uses AI. Mineral Discovery now uses AI. The number of every higher education, every university, uses AI. So I think it is fairly safe to say that AI has gone mainstream. And that it's being integrated into every application. And our hope is that, of course, the technology continues to advance safely and advance in a helpful way to our society. And with that, you know, we're I do believe that we're at the beginning of this new transition. And what I mean by that in the beginning, is remember behind us has been decades of data centers and decades of computers that have been built. And they've been built for a world of hand coding and general-purpose computing. And CPUs and so on and so forth. And going forward, I think it's fairly safe to say that that world is going to be almost all software will be infused with AI. All software and all services will be based on ultimately based on machine learning, and the data flywheel is gonna part of improving software and services. And that the future computers will be accelerated. The future computers will be based on AI. And we're really three years into that journey. And in modernizing computers that have taken decades to build out. And so I'm fairly sure that we're in the beginning of this new era. And then lastly, no technology has ever had the opportunity to address a larger part of the world's GDP than AI. No software tool ever has. And so this is now a software tool that can address a much larger part of the world's GDP, more than any time in history. And so the way we think about growth and the way we think about whether something is big or small. Has to be in the context of that. And when you take a step back and look at it from that perspective, we're really just in the beginnings. Christa: Your next question comes from the line of Aaron Rakers with Wells Fargo. Please go ahead. Erin, your line is open. Your next question comes from Mark Lipacis with Evercore ISI. Please go ahead. Marshall Pappas: Hi. This is Marshall Pappas. Thanks for taking the question. Question. I had a clarification and a question. Colette, for the clarification. Did you say that enterprise within the data center grew 2x year on year for the January quarter? And if so, does that would that make it the faster growing than the hyperscalers? And then, Jensen, for you, the question, hyperscalers are the biggest purchasers of your solutions, but they buy equipment for both internal and external workloads, external workloads being cloud services that enterprises use. So the question is, can you give us a sense of how that hyperscale expense splits between that external workload and internal and as these new AI workflows and applications come up, would you expect enterprises to become a larger part of that consumption mix? And does that impact how you develop your service your ecosystem? Thank you. Colette Kress: Sure. Thanks for the question regarding our enterprise business. Yes. It grew 2x. Very similar to what we were seeing with our large CSPs. Keep in mind, these are both important areas to understand. Working with the CSPs can be working on large language models. Can be working on inference on their own work? But keep in mind, that is also where the enterprises are surfacing. Your enterprises are both with your CSPs, as well as in terms of building on their own. They're both growing quite well. Jensen Huang: The CSPs are about half of our business. And the CSPs have internal consumption, and external consumption, as you say. And we're using of course, used for internal consumption. We work very closely with all of them to optimize workloads that are internal to them because they have a large infrastructure of NVIDIA Corporation gear that they could take advantage of. And the fact that we could be used for AI on the one hand, video processing on the other hand, data processing like Spark. We're fungible. And so the useful life. Our infrastructure is much better. If the useful life is much longer, then the TCO is also lower. And so the second part is how do we see the growth of enterprise or not CSPs, if you will, going forward? And the answer is I believe, long term. It is by far larger. And the reason for that is because if you look at the computer industry today, and what is not served by the computer industry is largely industrial. Let me give you an example. When we say enterprise, and let's say let's use a car company as an example because they make both soft things and hard things. And so in the case of a car company, the employees would be what we call enterprise. And agentic AI and software planning systems and tools, and we have some really exciting things to with you guys at GTC. Those agentic systems are for employees to make employees more productive. To design, to market, plan, to operate their company. That's agentic AIs. On the other hand, the cars that they manufacture also need AI. They need an AI system that trains the cars treats this entire giant fleet of cars, and you know, today, there's some billion cars on the road. Someday, there'd be a billion cars on the road, and every single one of those cars will be, you know, robotic cars. And they'll all be collecting data, and we'll be improving them using an AI factory where they whereas they have a car factory today, in the future, they'll have a car factory and an AI factory. And then inside the car itself is a robotic system. And so as you can see, there are three computers involved. And there's the computer that helps the people. There's the computer that builds the AI for it. The machineries. It could be, of course. Could be a tractor. It could be a lawnmower. It could be a human or a robot that's being developed today. It could be a building. It could be a warehouse. These physical systems require a new type of AI we call physical AI. They can't just understand the meaning of words and languages but they have to understand the meaning of the world. Friction and inertia, object permanence, and cause and effect, and all of those types of things that are common sense to you and I. But you know, AI has to go learn those physical effects. So we call that physical AI. That whole part of using agentic AI to revolutionize the way we work inside companies. That's just starting. This is now the beginning of the agentic AI era. And you hear a lot of people talking about it and got some really great things going on. And then there's the physical AI after that, and then there's robotic systems after that. And so these three computers are all brand new. And my sense is that long term, this will be by far a larger of a mold which kinda makes sense. You know, the world the world's GDP is represented by either heavy industries industrials. And companies that are providing for those. Christa: Your next question comes from the line of Aaron Rakers with Wells Fargo. Please go ahead. Aaron Rakers: Yeah. Thanks for letting me back in. Jensen, I'm curious as we now approach the two-year anniversary of really the Hopper inflection that you saw in 2023 in Gen AI in general. We think about the roadmap you have in front of us, how do you think about the infrastructure that's been deployed from a replacement cycle perspective and whether, you know, if it's GV300 or if it's the Rubin cycle where we start to see maybe some refresh opportunity. I'm just curious to how you look at that. Jensen Huang: Yeah. I appreciate it. First of all, people are still using Voltas. And Pascals, and Amperes. And the reason for that is because they're always things that because CUDA is so programmable, you could use it right well, one of the major use cases right now is data processing and data curation. You find a circumstance that an AI model is not very good at? You present that circumstance to a vision language model, let's say. Let's say it's a car? You present that circumstance to a vision language model, the vision language model actually looks at the circumstances. It's a this isn't this is what happened, and I wasn't very good at it. You then take that response, this the prompt, and you go and prompt an AI model to go find in your whole link of data of other circumstances like that. Whatever that circumstance was. And then you use an AI to do domain randomization and generate a whole bunch of other examples. And then from that, you can go train the model. And so you could use the Amperes to go and do data processing and data curation and machine learning-based search. And then you create the training dataset, which you then present to your Hopper systems for training. And so each one of these architectures are completely are you know, they're all CUDA compatible, and so everything runs on everything. But if you have infrastructure in place, and you can put the less intensive workloads onto the installed base of the past. All of our CPUs are very well employed. Christa: We have time for one more question, and that question comes from Atif Malik with Citi. Please go ahead. Atif Malik: Hi. Thank you for taking my question. I have a follow-up question on gross margins, Colette. I understand there are many moving parts that will yield and NVLink 72 and Ethernet mix. And you kind of tiptoed the earlier question if April quarter is the bottom. But second half would have to ramp, like, 200 basis point per quarter to get to the mid-seventies range that you're giving, for the end of the fiscal year. And we still don't know much about tariffs impact to broader semiconductor. So what kind of gives you the confidence in that trajectory in the back half of this year? Colette Kress: Yeah. Thanks for the question. Our gross margins, they're quite complex. In terms of the material. And everything that we put together in a Blackwell system. Tremendous amount of opportunity to look at a lot of different pieces of that. On how we can better improve our gross margins over time. Remember, we have many different configurations as well. On Blackwell. That will be able to help us do that. So, together, working after we get some of these really strong ramping completed for our customers we can begin a lot of that work. If not, we're gonna probably start as soon as possible. If we can improve it in the short term, we will also do that. Tariffs, at this point, it's a little bit of an unknown. It's an unknown until we understand further what the US government's plan is, its timing, it's where, and how much. So at this time, we are awaiting but again, we would, of course, always follow export control and or tariffs in that manner. Christa: Ladies and gentlemen, that does conclude our question and answer session. I'm sorry. Thank you. Jensen Huang: No. No. I'm gonna just wanna thank you. Up to, Jensen? And, like, the medium, a couple things. I just wanna thank you. Thank you, Colette. Demand for Blackwell is extraordinary. AI is evolving beyond perception. And generative AI into reasoning. With reasoning AI, we're observing another scaling law. Inference time or test time scaling. The more computation the more the model thinks the smarter the answer. Models like OpenAI's Grok 3, DeepSeq R1, are reasoning models that apply inference time scale. Reasoning models can consume a hundred times more compute. Future reasoning models can consume much more compute. DeepSeq R1 has ignited global enthusiasm. It's an excellent innovation. But even more importantly, it has open-sourced a world-class reasoning AI model. Nearly every AI developer is applying R1. Or chain of thought and reinforcement learning techniques like R1. To scale their model's performance. We now have three scaling laws, as I mentioned earlier. Driving the demand for AI computing. The traditional scaling laws of AI remain intact. Foundation models are being enhanced with multimodality. And pretraining is still growing. But it's no longer enough. We have two additional scaling dimensions. Post-training scaling, where reinforcement learning fine-tuning, model distillation, require orders of magnitude more compute than pretraining alone. Inference time scaling and reasoning where a single query can demand a hundred times more compute. We designed Blackwell for this moment a single platform that can easily transition from pretraining, post-training, and test time scaling. Blackwell's MP4 transformer engine, and NVLink 72 scale-up fabric. And new software technologies let Blackwell process reasoning AI models 25 times faster than Hopper. Blackwell, in all of these configurations, is in full production. Each Grace Blackwell NVLink 72 rack is an engineering marvel. One and a half million components produced across 350 manufacturing sites by nearly a hundred thousand factory operators. AI is advancing at light speed. We're at the beginning of reasoning AI and inference time scaling. But we're just at the start of the age of AI. Multimodal AIs. Enterprise AI, Sovereign AI. And physical AI are right around the corner. We will grow strongly in 2025. Going forward, data centers will dedicate most of CapEx to accelerated computing and AI. Data centers will increasingly become AI factories. And every company will have a either rented or self-operated. I wanna thank all of you for joining us today. Come join us at GTC in a couple of weeks gonna be talking about Blackwell Ultra, Rubin, and other new computing networking, reasoning AI, physical AI products. And a whole bunch more. Thank you. Christa: This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is Joel, and I'll be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's Third Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. [Operator Instructions] Thank you. Stewart Stecker, you may begin your conference. Stewart Stecker: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the third quarter of fiscal 2025. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the fourth quarter of fiscal 2025. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, November 20, 2024, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thank you, Stewart. Q3 was another record quarter. We continued to deliver incredible growth. Revenue of $35.1 billion was up 17% sequentially and up 94% year-on-year and, well above our outlook of $32.5 billion. All market platforms posted strong sequential and year-over-year growth, fueled by the adoption of NVIDIA accelerated computing and AI. Starting with Data Center, another record was achieved in Data Center. Revenue of $30.8 billion, up 17% sequential and up 112% year-on-year. NVIDIA Hopper demand is exceptional and sequentially, NVIDIA H200 sales increased significantly to double-digit billions, the fastest product ramp in our company's history. The H200 delivers up to 2 times faster inference performance and up to 50% improved TCO. Cloud service providers were approximately half of our data center sales with revenue increasing more than 2 times year-on-year. CSPs deployed NVIDIA H200 infrastructure and high-speed networking with installations scaling to tens of thousands of GPUs to grow their business and serve rapidly rising demand for AI training and inference workloads. NVIDIA H200-powered cloud instances are now available from AWS, CoreWeave, and Microsoft Azure with Google Cloud and OCI coming soon. Alongside significant growth from our large CSPs, NVIDIA GPU regional cloud revenue jumped 2 times year-on-year as North America, EMEA, and Asia Pacific regions ramped NVIDIA cloud instances and sovereign cloud buildout. Consumer Internet revenue more than doubled year-on-year as companies scaled their NVIDIA Hopper infrastructure to support next-generation AI models, training, multimodal and agentic AI, deep learning recommender engines, and generative AI inference and content creation workloads. NVIDIA's Ampere and Hopper infrastructures are fueling inference revenue growth for customers. NVIDIA is the largest inference platform in the world. Our large installed base and rich software ecosystem encourage developers to optimize for NVIDIA and deliver continued performance and TCL improvements. Rapid advancements in NVIDIA's software algorithms boosted Hopper inference throughput by an incredible 5 times in one year and cut time to first token by 5 times. Our upcoming release of NVIDIA NIM will boost Hopper Inference performance by an additional 2.4 times. Continuous performance optimizations are a hallmark of NVIDIA and drive increasingly economic returns for the entire NVIDIA installed base. Blackwell is in full production after a successfully executed mass change. We shipped 13,000 GPU samples to customers in the third quarter, including one of the first Blackwell DGX engineering samples to OpenAI. Blackwell is a full stack, full infrastructure, AI data center scale system with customizable configurations needed to address a diverse and growing AI market from x86 to ARM, training to inferencing GPUs, InfiniBand to Ethernet switches, and NVLINK and from liquid-cooled to air-cooled. Every customer is racing to be the first to market. Blackwell is now in the hands of all of our major partners and they are working to bring up their Data Centers. We are integrating Blackwell systems into the diverse Data Center configurations of our customers. Blackwell demand is staggering and we are racing to scale supply to meet the incredible demand customers are placing on us. Customers are gearing up to deploy Blackwell at scale. Oracle announced the world's first Zettascale AI Cloud computing clusters that can scale to over 131,000 Blackwell GPUs to help enterprises train and deploy some of the most demanding next-generation AI models. Yesterday, Microsoft announced they will be the first CSP to offer in private preview Blackwell-based cloud instances powered by NVIDIA GB200, and Quantum InfiniBand. Last week, Blackwell made its debut on the most recent round of MLPerf Training results, sweeping the per GPU benchmarks and delivering a 2.2 times leap in performance over Hopper. The results also demonstrate our relentless pursuit to drive down the cost of compute. Just 64 Blackwell GPUs are required to run the GPT-3 benchmark compared to 256 H100s or a 4 times reduction in cost. NVIDIA Blackwell architecture with NVLINK Switch enables up to 30 times faster inference performance and a new level of inference scaling throughput and response time that is excellent for running new reasoning inference applications like OpenAI's o1 model. With every new platform shift, a wave of start-ups is created. Hundreds of AI native companies are already delivering AI services with great success. Through Google, Meta, Microsoft, and OpenAI are the headliners and Anthropic, Perplexity, Mistral, Adobe Firefly, Runway, Midjourney, Lightricks, Harvey, Codeium, Cursor, and Bridge are seeing great success, while thousands of AI-native startups are building new services. The next wave of AI are Enterprise AI and Industrial AI. Enterprise AI is in full throttle. NVIDIA AI Enterprise, which includes NVIDIA NeMo and NIM microservices is an operating platform of agentic AI. Industry leaders are using NVIDIA AI to build Co-Pilots and agents. Working with NVIDIA, Cadence, Cloudera, Cohesity, NetApp, Nutanix, Salesforce, SAP and ServiceNow are racing to accelerate development of these applications with the potential for billions of agents to be deployed in the coming years. Consulting leaders like Accenture and Deloitte are taking NVIDIA AI to the world's enterprises. Accenture launched a new business group with 30,000 professionals trained on NVIDIA AI technology to help facilitate this global build-out. Additionally, Accenture with over 770,000 employees is leveraging NVIDIA-powered Agentic AI applications internally, including in one case that cuts manual steps in marketing campaigns by 25% to 35%. Nearly 1,000 companies are using NVIDIA NIM and the speed of its uptake is evident in NVIDIA AI Enterprise monetization. We expect NVIDIA AI Enterprise full year revenue to increase over 2 times from last year and our pipeline continues to build. Overall, our software, service, and support revenue is annualizing at $1.5 billion, and we expect to exit this year annualizing at over $2 billion. Industrial AI and robotics are accelerating. This is triggered by breakthroughs in physical AI, foundation models that understand the physical world. Like NVIDIA NeMo for enterprise AI agents, we built NVIDIA Omniverse for developers to build, train, and operate industrial AI and robotics. Some of the largest industrial manufacturers in the world are adopting NVIDIA Omniverse to accelerate their businesses, automate their workflows, and to achieve new levels of operating efficiency. Foxconn, the world's largest electronics manufacturer is using digital twins and industrial AI built on NVIDIA Omniverse to speed the bring up of its Blackwells factories and drive new levels of efficiency. In its Mexico facility alone, Foxconn expects to reduce -- a reduction of over 30% in annual kilowatt-hour usage. From a geographic perspective, our Data Center revenue in China grew sequentially due to shipments of export-compliant copper products to industries. As a percentage of total Data Center revenue, it remains well below levels prior to the onset of export controls. We expect the market in China to remain very competitive going forward. We will continue to comply with export controls while serving our customers. Our sovereign AI initiatives continue to gather momentum as countries embrace NVIDIA accelerated computing for a new industrial revolution powered by AI. India's leading CSPs include Tata Communications and Yotta Data Services are building AI factories for tens of thousands of NVIDIA GPUs. By year-end, they will have boosted NVIDIA GPU deployments in the country by nearly 10 times. Infosys, TSE, Wipro are adopting NVIDIA AI Enterprise and upskilling nearly 0.5 million developers and consultants to help clients build and run AI agents on our platform. In Japan, SoftBank is building the nation's most powerful AI supercomputer with NVIDIA DGX Blackwell and Quantum InfiniBand. SoftBank is also partnering with NVIDIA to transform the telecommunications network into a distributed AI network with NVIDIA AI Aerial and AI-RAN platform that can process both 5G RAN on AI on CUDA. We are launching the same in the US with T-Mobile. Leaders across Japan, including Fujitsu, NEC and NTT are adopting NVIDIA AI Enterprise and major consulting companies, including EY, Strategy, and Consulting will help bring NVIDIA AI technology to Japan's industries. Networking revenue increased 20% year-on-year. Areas of sequential revenue growth include InfiniBand and Ethernet switches, SmartNICs, and BlueField DPUs. The networking revenue was sequentially down, networking demand is strong and growing and we intend -- anticipate sequential growth in Q4. CSPs and supercomputing centers are using and adopting the NVIDIA InfiniBand platform to power new H200 clusters. NVIDIA Spectrum-X Ethernet for AI revenue increased over 3 times year-on-year and our pipeline continues to build with multiple CSPs and consumer Internet companies planning large cluster deployments. Traditional Ethernet was not designed for AI. NVIDIA Spectrum-X uniquely leverages technology previously exclusive to InfiniBand to enable customers to achieve massive scale of their GPU compute. Utilizing Spectrum-X, xAI's Colossus, 100,000 Hopper Supercomputer will experience zero application latency degradation and maintained 95% data throughput versus 60% for traditional Ethernet. Now moving to gaming and AI PCs. Gaming revenue of $3.3 billion increased 14% sequentially and 15% year-on-year. Q3 was a great quarter for gaming with notebook, console, and desktop revenue, all growing sequentially and year-on-year. RTX end-demand was fueled by strong back-to-school sales as consumers continue to choose GeForce RTX GPUs and devices to power gaming, creative, and AI applications. Channel inventory remains healthy and we are gearing up for the holiday season. We began shipping new GeForce RTX AI PCs with up to 321 AI tops from ASUS and MSI with Microsoft's Copilot+ capabilities anticipated in Q4. These machines harness the power of RTX ray tracing and AI technologies to supercharge gaming, photo and video editing, image generation, and coding. This past quarter, we celebrated the 25th anniversary of the GeForce 256, the world's first GPU. The transforming computing graphics to igniting the AI revolution, NVIDIA's GPUs have been the driving force behind some of the most consequential technologies of our time. Moving to ProViz. Revenue of $486 million was up 7% sequentially and 17% year-on-year. NVIDIA RTX workstations continue to be the preferred choice to power professional graphics, design, and engineering-related workloads. Additionally, AI is emerging as a powerful demand driver, including autonomous vehicle simulation, generative AI model prototyping for productivity-related use cases, and generative AI, content creation in media and entertainment. Moving to Automotive. Revenue was a record $449 million, up 30% sequentially and up 72% year-on-year. Strong growth was driven by self-driving ramps of NVIDIA Orin and robust end-market demand for NAVs. Volvo Cars has rolling out its fully electric SUV built on NVIDIA Orin and DriveOS. Okay. Moving to the rest of the P&L. GAAP gross margin was 74.6% and non-GAAP gross margin was 75%, down sequentially, primarily driven by a mix-shift of the H100 systems to more complex and higher cost systems within Data Center. Sequentially, GAAP operating expenses and non-GAAP operating expenses were up 9% due to higher compute, infrastructure, and engineering development costs for new product introductions. In Q3, we returned $11.2 billion to shareholders in the form of share repurchases and cash dividends. Well, let me turn to the outlook for the fourth quarter. Total revenue is expected to be $37.5 billion, plus or minus 2%, which incorporates continued demand for Hopper architecture and the initial ramp of our Blackwell products, while demand is greatly exceed supply, we are on track to exceed our previous Blackwell revenue estimate of several billion dollars as our visibility into supply continues to increase. On gaming, although sell-through was strong in Q3, we expect fourth quarter revenue to decline sequentially due to supply constraints. GAAP and non-GAAP gross margins are expected to be 73% and 73.5%, respectively, plus or minus 50 basis points. Blackwell is a customizable AI infrastructure with several different types of NVIDIA-build chips, multiple networking options, and for air and liquid-cooled Data Centers. Our current focus is on ramping to strong demand, increasing system availability, and providing the optimal mix of configurations to our customer. As Blackwell ramps, we expect gross margins to moderate to the low-70s. When fully ramped, we expect Blackwell margins to be in the mid-70s. GAAP and non-GAAP operating expenses are expected to be approximately $4.8 billion and $3.4 billion, respectively. We are a data center scale AI infrastructure company. Our investments include building data centers for development of our hardware and software stacks and to support new introductions. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $400 million, excluding gains and losses from non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 16.5% plus or minus 1%, excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR websites. In closing, let me highlight upcoming events for the financial community. We will be attending the UBS Global Technology and AI Conference on December 3rd, in Scottsdale. Please join us at CES in Las Vegas, where Jensen will deliver a keynote on January 6th, and we will host a Q&A session for financial analysts the next day on January 7th. Our earnings call to discuss results for the fourth quarter of fiscal 2025 is scheduled for February 26th, 2025. We will now open the call for questions. Operator, can you poll for questions, please? Operator: [Operator Instructions] Your first question comes from the line of C.J. Muse of Cantor Fitzgerald. Your line is open. C.J. Muse: Yes, good afternoon. Thank you for taking the question. I guess just a question for you on the debate around whether scaling for large language models have stalled. Obviously, we're very early here but would love to hear your thoughts on this front. How are you helping your customers as they work through these issues? And then obviously, part of the context here as we're discussing clusters that have yet to benefit from Blackwell. So is this driving even greater demand for Blackwell? Thank you. Jensen Huang: A foundation model pre-training scaling is intact and it's continuing. As you know, this is an empirical law, not a fundamental physical law, but the evidence is that it continues to scale. What we're learning, however, is that it's not enough that we've now discovered two other ways to scale. One is post-training scaling. Of course, the first generation of post-training was reinforcement learning human feedback, but now we have reinforcement learning AI feedback and all forms of synthetic data generated data that assists in post-training scaling. And one of the biggest events and one of the most exciting developments is Strawberry, ChatGPT o1, OpenAI's o1, which does inference time scaling, what's called test time scaling. The longer it thinks, the better and higher-quality answer it produces and it considers approaches like chain of thought and multi-path planning and all kinds of techniques necessary to reflect and so on and so forth and it's intuitively, it's a little bit like us doing thinking in our head before we answer a question. And so we now have three ways of scaling and we're seeing all three ways of scaling. And as a result of that, the demand for our infrastructure is really great. You see now that at the tail-end of the last generation of foundation models were at about 100,000 Hoppers. The next generation starts at 100,000 Blackwells. And so that kind of gives you a sense of where the industry is moving with respect to pre-training scaling, post-training scaling, and then now very importantly inference time scaling. And so the demand is really great for all of those reasons. But remember, simultaneously, we're seeing inference really starting to scale out for our company. We are the largest inference platform in the world today because our installed base is so large and everything that was trained on Amperes and Hoppers inference incredibly on Amperes and Hoppers. And as we move to Blackwells for training foundation models, it leads behind it a large installed base of extraordinary infrastructure for inference. And so we're seeing inference demand go up. We're seeing inference time scaling go up. We see the number of AI-native companies continue to grow. And of course, we're starting to see enterprise adoption of agentic AI really is the latest rage. And so we're seeing a lot of demand coming from a lot of different places. Operator: Your next question comes from the line of Toshiya Hari of Goldman Sachs. Your line is open. Toshiya Hari: Hi, good afternoon. Thank you so much for taking the question. Jensen, you executed the mass change earlier this year. There were some reports over the weekend about some heating issues. On the back of this, we've had investors ask about your ability to execute to the roadmap you presented at GTC this year with Ultra coming out next year and the transition to [Ruben] (ph) in 2026. Can you sort of speak to that? And some investors are questioning that. So if you can sort of speak to your ability to execute on time, that would be super helpful. And then a quick part B, on supply constraints, is it a multitude of componentry that's causing this? Or is it specifically [HBM] (ph)? Is it supply constraints? Are the supply constraints getting better? Are they worsening? Any sort of color on that would be super helpful as well. Thank you. Jensen Huang: Yes, thanks. Thanks. So let's see, back to the first question. Blackwell production is in full steam. In fact, as Colette mentioned earlier, we will deliver this quarter more Blackwells than we had previously estimated. And so the supply chain team is doing an incredible job working with our supply partners to increase Blackwell, and we're going to continue to work hard to increase Blackwell through next year. It is the case that demand exceeds our supply and that's expected as we're in the beginnings of this generative AI revolution as we all know. And we're at the beginning of a new generation of foundation models that are able to do reasoning and able to do long thinking and of course, one of the really exciting areas is physical AI, AI that now understands the structure of the physical world. And so Blackwell demand is very strong. Our execution is on -- is going well. And there's obviously a lot of engineering that we're doing across the world. You see now systems that are being stood up by Dell and CoreWeave, I think you saw systems from Oracle stood up. You have systems from Microsoft and they're about to preview their Grace Blackwell systems. You have systems that are at Google. And so all of these CSPs are racing to be first. The engineering that we do with them is, as you know, rather complicated. And the reason for that is because although we build full stack and full infrastructure, we disaggregate all of the -- this AI supercomputer and we integrate it into all of the custom data centers in architectures around the world. That integration process is something we've done several generations now. We're very good at it, but still, there's still a lot of engineering that happens at this point. But as you see from all of the systems that are being stood up, Blackwell is in great shape. And as we mentioned earlier, the supply and what we're planning to ship this quarter is greater than our previous estimates. With respect to the supply chain, all right, there are seven different chips, seven custom chips that we built in order for us to deliver the Blackwell systems. The Blackwell systems go in air-cooled or liquid-cooled, NVLink 8 or NVLink 72 or NVLink 8, NVLink 36, NVLink 72 we have x86 or Grace and the integration of all of those systems into the world's data centers is nothing short of a miracle. And so the component supply chain necessary to ramp at the scale you have to go back and take a look at how much Blackwell we shipped last quarter, which was zero. And in terms of how much Blackwell total systems will ship this quarter, which is measured in billions, the ramp is incredible. And so almost every company in the world seems to be involved in our supply chain. And we've got great partners, everybody from, of course, TSMC and Amphenol, the connector company, incredible company, Vertiv and SK Hynix and Micron Spill, Amkor and KYEC and there's Foxconn and the many the factories that they've built and Quanta and Wiwynn and gosh, Dell and HP and Super Micro, Lenovo and the number of companies is just really quite incredible, Quanta. And I'm sure I've missed partners that are involved in the ramping up of Blackwell, which I really appreciate. And so anyways, I think we're in great shape with respect to the Blackwell ramp at this point. And then lastly, your question about our execution of our roadmap. We're on an annual roadmap and we're expecting to continue to execute on our annual roadmap. And by doing so, we increase the performance, of course, of our platform, but it's also really important to realize that when we're able to increase performance and do so at X factors at a time, we're reducing the cost of training, we're reducing the cost of inferencing, we're reducing the cost of AI so that it could be much more accessible. But the other factor that's very important to note is that when there's a data center of some fixed size and a data center always is of some fixed size. It could be, of course, 10s of megawatts in the past and now it's most data centers are now 100 megawatts to several 100 megawatts and we're planning on gigawatt data centers, it doesn't really matter how large the data centers are, the power is limited. And when you're in the power-limited data center, the best -- the highest performance per watt translates directly into the highest revenues for our partners. And so on the one hand, our annual roadmap reduces cost, but on the other hand, because our perf per watt is so good compared to anything out there, we generate for our customers the greatest possible revenues. And so that annual rhythm is really important to us and we have every intentions of continuing to do that. And everything is on track as far as I know. Operator: Your next question comes from the line of Timothy Arcuri of UBS. Your line is open. Timothy Arcuri: Thanks a lot. I'm wondering if you can talk about the trajectory of how Blackwell is going to ramp this year. I know, Jensen, you did just talk about Blackwell being better than I think you had said several billions of dollars in January. It sounds like you're going to do more than that. But I think in recent months also, you said that Blackwell crosses over Hopper in the April quarter. So I guess I had two questions. First of all, is that still the right way to think about it that Blackwell will crossover Hopper in April? And then Colette, you kind of talked about Blackwell bringing down gross margin to the low-70s as it ramps. So I guess if April is the crossover, is that the worst of the pressure on gross margin? So you're going to be kind of in the low-70s as soon as April. I'm just wondering if you can sort of shape that for us. Thanks. Jensen Huang: Colette, why don't you start? Colette Kress: Sure. Let me first start with your question, Tim. Thank you regarding our gross margins, and we discussed our gross margins as we are ramping Blackwell in the very beginning and the many different configurations, the many different chips that we are bringing to market, we are going to focus on making sure we have the best experience for our customers as they stand that up. We will start growing into our gross margins, but we do believe those will be in the low 70s in that first part of the ramp. So you're correct, as you look at the quarters following after that, we will start increasing our gross margins and we hope to get to the mid-70s quite quickly as part of that ramp. Jensen Huang: Hopper demand will continue through next year, surely the first several quarters of the next year. And meanwhile, we will ship more Blackwells next quarter than this. And we'll ship more Blackwells the quarter after that than our first quarter. And so that kind of puts it in perspective. We are really at the beginnings of two fundamental shifts in computing that is really quite significant. The first is moving from coding that runs on CPUs to machine learning that creates neural networks that runs on GPUs. And that fundamental shift from coding to machine learning is widespread at this point. There are no companies who are not going to do machine learning. And so machine learning is also what enables generative AI. And so on the one hand, the first thing that's happening is a trillion dollars’ worth of computing systems and data centers around the world is now being modernized for machine learning. On the other hand, secondarily, I guess, is that, that on top of these systems are going to be -- we're going to be creating a new type of capability called AI. And when we say generative AI, we're essentially saying that these data centers are really AI factories. They're generating something. Just like we generate electricity, we're now going to be generating AI. And if the number of customers is large, just as the number of consumers of electricity is large, these generators are going to be running 24/7. And today, many AI services are running 24/7, just like an AI factory. And so we're going to see this new type of system come online, and I call it an AI factory because that's really as close to what it is. It's unlike a data center of the past. And so these two fundamental trends are really just beginning. And so we expect this to happen this growth -- this modernization and the creation of a new industry to go on for several years. Operator: Your next question comes from the line of Vivek Arya of Bank of America Securities. Your line is open. Vivek Arya: Thanks for taking my question. Colette, just to clarify, do you think it's a fair assumption to think NVIDIA could recover to kind of mid-70s gross margin in the back half of calendar 2025? Just wanted to clarify that. And then, Jensen my main question historically, when we have seen hardware deployment cycles, they have inevitably included some digestion along the way. When do you think we get to that phase, or is it just too premature to discuss that because you're just the start of Blackwell? So how many quarters of shipments do you think is required to kind of satisfy this first wave? Can you continue to grow this into calendar 2026? Just how should we be prepared to see what we have seen historically, right, the periods of digestion along the way of a long-term kind of secular hardware deployment? Colette Kress: Okay. Vivek, thank you for the question. Let me clarify your question regarding gross margins. Could we reach the mid-70s in the second half of next year? And yes, I think it is reasonable assumption or a goal for us to do, but we'll just have to see how that mix of ramp goes. But yes, it is definitely possible. Jensen Huang: The way to think through that, Vivek, is I believe that there will be no digestion until we modernize a trillion dollars with the data centers. Those -- if you look at the world's data centers, the vast majority of it is built for a time when we wrote applications by hand and we ran them on CPUs. It's just not a sensible thing to do anymore. If you have -- if every company's CapEx, if they're ready to build a data center tomorrow, they ought to build it for a future of machine-learning and generative AI. Because they have plenty of old data centers. And so what's going to happen over the course of next X number of years, and let's assume that over the course of four years, the world's data centers could be modernized as we grow into IT. As you know, IT continues to grow about 20%, 30% a year, let's say. And let's say by 2030, the world's data centers for computing is, call it a couple of trillion dollars. And we have to grow into that. We have to modernize the data center from coding to machine learning. That's number one. The second part of it is generative AI, and we're now producing a new type of capability that world has never known, a new market segment that the world has never had. If you look at OpenAI, it didn't replace anything. It's something that's completely brand new. It's in a lot of ways as when the iPhone came, it was completely brand new. It wasn't really replacing anything. And so we're going to see more and more companies like that. And they're going to create and generate out of their services, essentially intelligence. Some of it would be digital artist intelligence like Runway. Some of it would be basic intelligence, like OpenAI. Some of it would be legal intelligence like Harvey. Digital marketing intelligence like [Reuters] (ph), so on and so forth. And the number of these companies, these -- what are they call AI-native companies are just in hundreds and almost every platform shift there was -- there were Internet companies as you recall, there were cloud-first companies. They were mobile-first companies and now they're AI natives. And so these companies are being created because people see that there's a platform shift and there's a brand new opportunity to do something completely new. And so my sense is that we're going to continue to build out to modernize IT, modernize computing, number one. And then number two, create these AI factories that are going to be for a new industry for the production of artificial intelligence. Operator: Your next question comes from the line of Stacy Rasgon of Bernstein Research. Your line is open. Stacy Rasgon: Hi, guys. Thanks for taking my questions. Colette, I had a clarification and a question for you. The clarification, just when you say low-70s gross margins, is 73.5 count as low-70s, or do you have something else in mind? And for my question, you're guiding total revenues and so I mean, total Data Center revenues in the next quarter must be up quote-unquote several billion dollars, but it sounds like Blackwell now should be up more than that. But you also said Hopper was still strong. So like is Hopper down sequentially next quarter? And if it is like why? Is it because of the supply constraints? Is China has been pretty strong is China is kind of rolling off a bit into Q4. So any color you can give us on sort of the Blackwell ramp and the Blackwell versus Hopper behavior into Q4 would be really helpful. Thank you. Colette Kress: So first starting on your first question there, Stacy, regarding our gross margin and defined low. Low, of course, is below the mid, and let's say we might be at 71%, maybe about 72%, 72.5%, we're going to be in that range. We could be higher than that as well. We're just going to have to see how it comes through. We do want to make sure that we are ramping and continuing that improvement, the improvement in terms of our yields, the improvement in terms of the product as we go through the rest of the year. So we'll get up to the mid-70s by that point. The second statement was a question regarding our Hopper and what is our Hopper doing. We have seen substantial growth for our H200, not only in terms of orders but the quickness in terms of those that are standing that up. It is an amazing product and it's the fastest-growing and ramping that we've seen. We will continue to be selling Hopper in this quarter, in Q4 for sure, that is across-the-board in terms of all of our different configurations and our configurations include what we may do in terms of China. But keep that in mind that folks are also at the same time looking to build out their Blackwell. So we've got a little bit of both happening in Q4. But yes, is it possible for Hopper to grow between Q3 and Q4, it's possible, but we'll just have to see. Operator: Your next question comes from the line of Joseph Moore of Morgan Stanley. Your line is open. Joseph Moore: Great. Thank you. I wonder if you could talk a little bit about what you're seeing in the inference market. You've talked about Strawberry and some of the ramifications of longer scaling inference projects. But you've also talked about the possibility that as some of these Hopper clusters age that you could use some of the Hopper latent chips for inference. So I guess, do you expect inference to outgrow training in the next kind of 12-month time frame, and just generally your thoughts there? Jensen Huang: Our hopes and dreams is that someday, the world does a ton of inference. And that's when AI has really succeeded, right. It's when every single company is doing inference inside their companies for the marketing department and forecasting department and supply chain group and their legal department and engineering, of course and coding, of course. And so we hope that every company is doing inference 24/7. And that there will be a whole bunch of AI native startups, thousands of AI native startups that are generating tokens and generating AI and every aspect of your computer experience from using Outlook to PowerPointing or when you're sitting there with Excel, you're constantly generating tokens. And every time you read a PDF, open a PDF, it generated a whole bunch of tokens. One of my favorite applications is NotebookLM, this Google application that came out. I use the living daylights out of it just because it's fun. And I put every PDF, every archive paper into it just to listen to it as well as scanning through it. And so I think -- that's the goal is to train these models so that people use it. And there's now a whole new era of AI if you will, a whole new genre of AI called physical AI, just those large language models understand the human language and how we the thinking process, if you will. Physical AI understands the physical world and it understands the meaning of the structure and understands what's sensible and what's not and what could happen and what won't and not only does it understand but it can predict and roll out a short future. That capability is incredibly valuable for industrial AI and robotics. And so that's fired up so many AI-native companies and robotics companies and physical AI companies that you're probably hearing about. And it's really the reason why we built Omniverse. Omniverse is so that we can enable these AIs to be created and learn in Omniverse and learn from synthetic data generation and reinforcement learning physics feedback instead of human feedback is now physics feedback. To have these capabilities, Omniverse was created so that we can enable physical AI. And so that the goal is to generate tokens. The goal is to inference and we're starting to see that growth happening. So I'm super excited about that. Now let me just say one more thing. Inference is super hard. And the reason why inference is super hard is because you need the accuracy to be high on the one hand. You need the throughput to be high so that the cost could be as low as possible, but you also need the latency to be low. And computers that are high throughput as well as low latency is incredibly hard to build. And these applications have long context lengths because they want to understand, they want to be able to inference within understanding the context of what's -- what they're being asked to do. And so the context length is growing larger and larger. On the other hand, the models are getting larger, they're multimodality. Just the number of dimensions that inference is innovating is incredible. And this innovation rate is what makes NVIDIA's architecture so great because our ecosystem is fantastic. Everybody knows that if they innovate on top of CUDA on top of NVIDIA's architecture, they can innovate more quickly and they know that everything should work. And if something were to happen, it's probably likely their code and not ours. And so that ability to innovate in every single direction at the same time, having a large installed base so that whatever you create could land on a NVIDIA computer and be deployed broadly all around the world in every single data center all the way out to the edge into robotic systems, that capability is really quite phenomenal. Operator: Your next question comes from the line of Aaron Rakers of Wells Fargo. Your line is open. Aaron Rakers: Yes, thanks for taking the question. I wanted to ask you as we kind of focus on the Blackwell cycle and think about the data center business. When I look at the results this last quarter, Colette, you mentioned that obviously, the networking business was down about 15% sequentially, but then your comments were that you were seeing very strong demand. You mentioned also that you had multiple cloud CSP design wins for these large-scale clusters. So I'm curious if you could unpack what's going on in the networking business and where maybe you've seen some constraints and just your confidence in the pace of Spectrum-X progressing to that multiple billions of dollars that you previously had talked about. Thank you. Colette Kress: Let's first start with the networking. The growth year-over-year is tremendous and our focus since the beginning of our acquisition of Mellanox has really been about building together the work that we do in terms of -- in the Data Center. The networking is such a critical part of that. Our ability to sell our networking with many of our systems that we are doing in data center is continuing to grow and do quite well. So this quarter is just a slight dip down and we're going to be right back up in terms of growing. They're getting ready for Blackwell and more and more systems that will be using not only our existing networking but also the networking that is going to be incorporated in a lot of these large systems that we are providing them to. Operator: Your next question comes from the line of Atif Malik of Citi. Your line is open. Atif Malik: Thank you for taking my question. I have two quick ones for Collette. Colette, on the last earnings call, you mentioned that sovereign demand is in low double-digit billions. Can you provide an update on that? And then can you explain the supply-constrained situation in gaming? Is that because you're shifting your supply towards data center? Colette Kress: So first starting in terms of sovereign AI, such an important part of growth, something that is really surfaced with the onset of generative AI and building models in the individual countries around the world. And we see a lot of them and we talked about a lot of them in the call today and the work that they are doing. So our sovereign AI and our pipeline going forward is still absolutely intact as those are working to build these foundational models in their own language, in their own culture, and working in terms of the enterprises within those countries. And I think you'll continue to see this be growth opportunities that you may see with our regional clouds that are being stored up and/or those that are focusing in terms of AI factories for many parts of the sovereign AI. This is areas where this is growing not only in terms of in Europe, but you're also seeing this in terms of growth in terms of -- in the Asia-Pac as well. Let me flip to your second question that you asked regarding gaming. So our gaming right now from a supply, we're busy trying to make sure that we can ramp all of our different products. And in this case, our gaming supply, given what we saw selling through was moving quite fast. Now the challenge that we have is how fast could we get that supply getting ready into the market for this quarter. Not to worry, I think we'll be back on track with more suppliers we turn the corner into the new calendar year. We're just going to be tight for this quarter. Operator: Your next question comes from the line of Ben Reitzes of Melius Research. Your line is open. Ben Reitzes: Yes. Hi. Thanks a lot for the question. I wanted to ask Colette and Jensen with regard to sequential growth. So very strong sequential growth this quarter and you're guiding to about 7%. Do your comments on Blackwell imply that we reaccelerate from there as you get more supply? Just in the first half, it would seem that there would be some catch-ups. So I was wondering how prescriptive you could be there. And then, Jensen, just overall, with the change in administration that's going to take place here in the US and the China situation, have you gotten any sense, or any conversations about tariffs, or anything with regard to your China business? Any sense of what may or may not go on? It's probably too early, but wondering if you had any thoughts there. Thanks so much. Jensen Huang: We guide one quarter at a time. Colette Kress: We are working right now on the quarter that we're in and building what we need to ship in terms of Blackwell. We have every supplier on the planet working seamlessly with us to do that. And once we get to next quarter, we'll help you understand in terms of that ramp that we'll see to the next quarter and after that. Jensen Huang: Whatever the new administration decides, we will of course support the administration. And that's our -- the highest mandate. And then after that, do the best we can. And just as we always do. And so we have to simultaneously and we will comply with any regulation that comes along fully and support our customers to the best of our abilities and compete in the marketplace. We'll do all of these three things simultaneously. Operator: Your final question comes from the line of Pierre Ferragu of New Street Research. Your line is open. Pierre Ferragu: Hey, thanks for taking my question. Jensen, you mentioned in your comments you have the pre-trainings, the actual language models and you have reinforcement learning that becomes more and more important in training and in inference as well. And then you have inference itself. And I was wondering if you have a sense like a high-level typical sense of out of an overall AI ecosystem like maybe one of your clients or one of the large models that are out there. Today, how much of the compute goes into each of these buckets? How much for the pre-training, how much for the reinforcement, and how much into inference today? Do you have any sense for how it's splitting and where the growth is the most important as well? Jensen Huang: Well, today it's vastly in pre-training a foundation model because as you know, post-training, the new technologies are just coming online and whatever you could do in pre-training and post-training, you would try to do so that the inference cost could be as low as possible for everyone. However, there are only so many things that you could do priority. And so you'll always have to do on-the-spot thinking and in-context thinking and a reflection. And so I think that the fact that all three are scaling is actually very sensible based on what we are. And in the area of foundation model, now we have multimodality foundation models and the amount of petabytes of video that these foundation models are going to be trained on is incredible. And so my expectation is that for the foreseeable future, we're going to be scaling pre-training, post-training as well as inference time scaling and which is the reason why I think we're going to need more and more compute and we're going to have to drive as hard as we can to keep increasing the performance by X factors at a time so that we can continue to drive down the cost and continue to increase their revenues and get the AI revolution going. Thank you. Operator: Thank you. I'll now turn the call back over to Jensen Huang for closing remarks. Jensen Huang: Thank you. The tremendous growth in our business is being fueled by two fundamental trends that are driving global adoption of NVIDIA computing. First, the computing stack is undergoing a reinvention, a platform shift from coding to machine learning. From executing code on CPUs to processing neural networks on GPUs. The trillion-dollar installed base of traditional Data center infrastructure is being rebuilt for Software 2.0, which applies machine learning to produce AI. Second, the age of AI is in full steam. Generative AI is not just a new software capability, but a new industry with AI factories manufacturing digital intelligence, a new industrial revolution that can create a multi-trillion dollar AI industry. Demand for Hopper and anticipation for Blackwell, which is now in full production are incredible for several reasons. There are more foundation model makers now than there were a year ago. The computing scale of pre-training and post-training continues to grow exponentially. There are more AI-native start-ups than ever and the number of successful inference services is rising. And with the introduction of ChatGPT o1, OpenAI o1, a new scaling law called test time scaling has emerged. All of these consume a great deal of computing. AI is transforming every industry, company, and country. Enterprises are adopting agentic AI to revolutionize workflows. Over time, AI coworkers will assist employees in performing their jobs faster and better. Investments in industrial robotics are surging due to breakthroughs in physical AI. Driving new training infrastructure demand as researchers train world foundation models on petabytes of video and Omniverse synthetically generated data. The age of robotics is coming. Countries across the world recognize the fundamental AI trends we are seeing and have awakened to the importance of developing their national AI infrastructure. The age of AI is upon us and it's large and diverse. NVIDIA's expertise, scale, and ability to deliver full stack and full infrastructure let us serve the entire multi-trillion dollar AI and robotics opportunities ahead. From every hyperscale cloud, enterprise private cloud to sovereign regional AI clouds, on-prem to industrial edge and robotics. Thanks for joining us today and catch up next time. Operator: This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is Abby and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's Second Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. [Operator Instructions] Thank you. And Mr. Stewart Stecker, you may begin your conference. Stewart Stecker: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the second quarter of fiscal 2025. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I would like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the third quarter of fiscal 2025. The content of today's call is NVIDIA's property. It cannot be reproduced or transcribed without prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of risks, significant risks, and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K, and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, August 28th, 2024, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. Let me highlight an upcoming event for the financial community. We will be attending the Goldman Sachs Communacopia and Technology Conference on September 11 in San Francisco, where Jensen will participate in a keynote fireside chat. Our earnings call to discuss the results of our third quarter of fiscal 2025 is scheduled for Wednesday, November 20th, 2024. With that, let me turn the call over to Colette. Colette Kress: Thanks, Stewart. Q2 was another record quarter. Revenue of $30 billion was up 15% sequentially and up 122% year-on-year and well above our outlook of $28 billion. Starting with data center, data center revenue of $26.3 billion was a record, up 16% sequentially and up 154% year-on-year, driven by strong demand for NVIDIA Hopper, GPU computing, and our networking platforms. Compute revenue grew more than 2.5 times, networking revenue grew more than 2 times from the last year. Cloud service providers represented roughly 45% for our data center revenue and more than 50% stemmed from the consumer, Internet, and enterprise companies. Customers continue to accelerate their Hopper architecture purchases, while gearing up to adopt Blackwell. Key workloads driving our data center growth include generative AI, model training, and inferencing. Video, image, and text data pre and post-processing with CUDA and AI workloads, synthetic data generation, AI-powered recommender systems, SQL, and vector database processing as well. Next-generation models will require 10 to 20 times more compute to train with significantly more data. The trend is expected to continue. Over the trailing four quarters, we estimate that inference drove more than 40% of our data center revenue. CSPs, consumer Internet companies, and enterprises benefit from the incredible throughput and efficiency of NVIDIA's inference platform. Demand for NVIDIA is coming from frontier model makers, consumer Internet services, and tens of thousands of companies and startups building generative AI applications for consumers, advertising, education, enterprise and healthcare, and robotics. Developers desire NVIDIA's rich ecosystem and availability in every cloud. CSPs appreciate the broad adoption of NVIDIA and are growing their NVIDIA capacity given the high demand. NVIDIA H200 platform began ramping in Q2, shipping to large CSPs, consumer Internet, and enterprise company. The NVIDIA H200 builds upon the strength of our Hopper architecture and offering, over 40% more memory bandwidth compared to the H100. Our data center revenue in China grew sequentially in Q2 and is a significant contributor to our data center revenue. As a percentage of total data center revenue, it remains below levels seen prior to the imposition of export controls. We continue to expect the China market to be very competitive going-forward. The latest round of MLPerf inference benchmarks highlighted NVIDIA's inference leadership with both NVIDIA, Hopper, and Blackwell platforms combining to win gold medals on all tasks. At Computex, NVIDIA with the top computer manufacturers unveiled an array of Blackwell architecture-powered systems and NVIDIA networking for building AI factories and data centers. With the NVIDIA MGX modular reference architecture, our OEMs and ODM partners are building more than 100 Blackwell-based systems designed quickly and cost-effectively. The NVIDIA Blackwell platform brings together multiple GPU, CPU, DPU, NVLink, NVLink switch, and the networking chips systems, and NVIDIA CUDA software to power the next-generation of AI across the cases, industries, and countries. The NVIDIA GB 200 NVL72 system with the fifth-generation NVLink enables all 72 GPUs to act as a single GPU and deliver up to 30 times faster inference for LLMs, workloads, and unlocking the ability to run trillion parameter models in real-time. Hopper demand is strong and Blackwell is widely sampling. We executed a change to the Blackwell GPU mass to improve production yields. Blackwell production ramp is scheduled to begin in the fourth quarter and continue into fiscal year '26. In Q4, we expect to ship several billion dollars in Blackwell revenue. Hopper shipments are expected to increase in the second half of fiscal 2025. Hopper supply and availability have improved. Demand for Blackwell platforms is well above supply, and we expect this to continue into next year. Networking revenue increased 16% sequentially. Our Ethernet for AI revenue, which includes our Spectrum-X end-to-end Ethernet platform, doubled sequentially with hundreds of customers adopting our Ethernet offerings. Spectrum-X has broad market support from OEM and ODM partners and is being adopted by CSPs, GPU cloud providers, and enterprise, including X-AI to connect the largest GPU compute cluster in the world. Spectrum-X supercharges Ethernet for AI processing and delivers 1.6 times the performance of traditional Ethernet. We plan to launch new Spectrum-X products every year to support demand for scaling compute clusters from tens of thousands of DPUs today to millions of GPUs in the near future. Spectrum-X is well on-track to begin a multi-billion dollar product line within a year. Our sovereign AI opportunities continue to expand as countries recognize AI expertise and infrastructure at national imperatives for their society and industries. Japan's National Institute of Advanced Industrial Science and Technology is building its AI bridging cloud infrastructure 3.0 supercomputer with NVIDIA. We believe sovereign AI revenue will reach low-double-digit billions this year. The enterprise AI wave has started. Enterprises also drove sequential revenue growth in the quarter. We are working with most of the Fortune 100 companies on AI initiatives across industries and geographies. A range of applications are fueling our growth, including AI-powered chatbots, generative AI copilots, and agents to build new monetizable business applications and enhance employee productivity. Amdocs is using NVIDIA generative AI for their smart agent, transforming the customer experience and reducing customer service costs by 30%. ServiceNow is using NVIDIA for its Now Assist offering, the fastest-growing new product in the company's history. SAP is using NVIDIA to build dual Co-pilots. Cohesity is using NVIDIA to build their generative AI agent and lower generative AI development costs. Snowflake serves over 3 billion queries a day for over 10,000 enterprise customers is working with NVIDIA to build Copilots. And lastly, Wistron is using NVIDIA AI Omniverse to reduce end-to-end cycle times for their factories by 50%. Automotive was a key growth driver for the quarter as every automaker developing autonomous vehicle technology is using NVIDIA in their data centers. Automotive will drive multi-billion dollars in revenue across on-prem and cloud consumption and will grow as next-generation AV models require significantly more compute. Healthcare is also on its way to being a multi-billion dollar business as AI revolutionizes medical imaging, surgical robots, patient care, electronic health record processing, and drug discovery. During the quarter, we announced a new NVIDIA AI foundry service to supercharge generative AI for the world's enterprises with Meta's Llama 3.1, collection of models. This marks a watershed moment for enterprise AI. Companies for the first time can leverage the capabilities of an open-source frontier-level model to develop customized AI applications to encode their institutional knowledge into an AI flywheel to automate and accelerate their business. Accenture is the first to adopt the new service to build custom Llama 3.1 models for both its own use and to assist clients seeking to deploy generative AI applications. Nvidia NIM accelerate and simplify model deployment. Companies across healthcare, energy, financial services, retail, transportation, and telecommunications are adopting NIMs, including Aramco, Lowe's, and Uber. AT&T realized 70% cost savings and 8 times latency reduction after moving into NIMs for generative AI, call transcription, and classification. Over 150 partners are embedding NIMs across every layer of the AI ecosystem. We announced NIM agent Blueprints, a catalog of customizable reference applications that include a full suite of software for building and deploying enterprise generative AI applications. With NIM agent blueprints, enterprises can refine their AI applications overtime, creating a data-driven AI flywheel. The first NIM agent blueprints include workloads for customer service, computer-aided drug discovery, and enterprise retrieval augmented generation. Our system integrators, technology solution providers, and system builders are bringing NVIDIA NIM agent blueprints to enterprises. NVIDIA NIM and NIM agent blueprints are available through the NVIDIA AI enterprise software platform, which has great momentum. We expect our software, SaaS and support revenue to approach a $2 billion annual run rate exiting this year, with NVIDIA AI Enterprise notably contributing to growth. Moving to gaming and AI PCs. Gaming revenue of $2.88 billion increased 9% sequentially and 16% year-on-year. We saw sequential growth in console, notebook, and desktop revenue and demand is strong and growing and channel inventory remains healthy. Every PC with RTX is an AIPC. RTX PCs can deliver up to 1,300 AI tops and there are now over 200 RTX AI laptops designs from leading PC manufacturers. With 600 AI-powered applications and games and an installed base of 100 million devices, RTX is set to revolutionize consumer experiences with generative AI. NVIDIA ACE, a suite of generative AI technologies is available for RTX, AI PCs. Mecha BREAK is the first game to use NVIDIA ACE, including our small large -- small language model, Minitron-4B optimized on device inference. The NVIDIA gaming ecosystem continues to grow, recently added RTX and DLSS titles including Indiana Jones and the Great Circle, Dune Awakening, and Dragon Age: The Veilguard. The GeForce NOW library continues to expand with total catalog size of over 2,000 titles, the most content of any cloud gaming service. Moving to Pro visualization. Revenue of $454 million was up 6% sequentially and 20% year-on-year. Demand is being driven by AI and graphic use cases, including model fine-tuning and Omniverse-related workloads. Automotive and manufacturing were among the key industry verticals driving growth this quarter. Companies are racing to digitalize workflows to drive efficiency across their operations. The world's largest electronics manufacturer, Foxconn is using NVIDIA Omniverse to power digital twins of the physical plants that produce NVIDIA Blackwell systems. And several large global enterprises, including Mercedes-Benz signed multi-year contracts for NVIDIA Omniverse Cloud to build industrial digital twins for factories. We announced new NVIDIA USD NIMs and connectors to open Omniverse to new industries and enable developers to incorporate generative AI Copilots and agents into USD workflows, accelerating their ability to build highly accurate virtual worlds. WPP is implementing USD NIM microservices in its generative AI-enabled content creation pipeline for customers such as the Coca-Cola company. Moving to automotive and robotics, revenue was $346 million, up 5% sequentially and up 37% year-on-year. Year-on-year growth was driven by the new customer ramps in self-driving platforms and increased demand for AI cockpit solutions. At the consumer -- at the Computer Vision and Pattern Recognition conference, NVIDIA won the Autonomous Brand Challenge in the end-to-end driving at-scale category, outperforming more than 400 entries worldwide. Austin Dynamics, BYD Electronics, Figure, Intrinsic, Siemens, Skilled ADI, and Teradyne Robotics are using the NVIDIA Isaac Robotics platform for autonomous robot arms, humanoids, and mobile robots. Now moving to the rest of the P&L. GAAP gross margins were 75.1% and non-GAAP gross margins were 75.7%, down sequentially due to a higher mix of new products within data center and inventory provisions for low-yielding Blackwell material. Sequentially, GAAP and non-GAAP operating expenses were up 12%, primarily reflecting higher compensation-related costs. Cash flow from operations was $14.5 billion. In Q2, we utilized cash of $7.4 billion towards shareholder returns in the form of share repurchases and cash dividends, reflecting the increase in dividend per share. Our Board of Directors recently approved a $50 billion share repurchase authorization to add to our remaining $7.5 billion of authorization at the end of Q2. Let me turn the outlook for the third quarter. Total revenue is expected to be $32.5 billion, plus or minus 2%. Our third-quarter revenue outlook incorporates continued growth of our Hopper architecture and sampling of our Blackwell products. We expect Blackwell production ramp in Q4. GAAP and non-GAAP gross margins are expected to be 74.4% and 75%, respectively, plus or minus 50 basis points. As our data center mix continues to shift to new products, we expect this trend to continue into the fourth quarter of fiscal 2025. For the full-year, we expect gross margins to be in the mid-70% range. GAAP and non-GAAP operating expenses are expected to be approximately $4.3 billion and $3.0 billion, respectively. Full-year operating expenses are expected to grow in the mid-to-upper 40% range as we work on developing our next generation of products. GAAP and non-GAAP other income and expenses are expected to be about $350 million, including gains and losses from non-affiliated investments and publicly-held equity securities. GAAP and non-GAAP tax rates are expected to be 17%, plus or minus 1%, excluding any discrete items. Further financial detail are included in the CFO commentary and other information available on our IR website. We are now going to open the call for questions. Operator, would you please help us poll for questions. Operator: Thank you. [Operator Instructions] And your first question comes from the line of Vivek Arya with Bank of America Securities. Your line is open. Vivek Arya: Thanks for taking my question. Jensen, you mentioned in the prepared comments that there is a change in the Blackwell GPU mask. I'm curious, are there any other incremental changes in back end packaging or anything else? And I think related, you suggested that you could ship several billion dollars of Blackwell in Q4 despite the change in the design. Is it because all these issues will be solved by then? Just help us size what is the overall impact of any changes in Blackwell timing? What that means to your kind of revenue profile and how are customers reacting to it? Jensen Huang: Yes. Thanks, Vivek. The change to the mask is complete. There were no functional changes necessary. And so we're sampling functional samples of Blackwell -- Grace Blackwell in a variety of system configurations as we speak. There are something like 100 different types of Blackwell-based systems that are built that were shown at Computex. And we're enabling our ecosystem to start sampling those. The functionality of Blackwell is as it is, and we expect to start production in Q4. Operator: And your next question comes from the line of Toshiya Hari with Goldman Sachs. Your line is open. Toshiya Hari: Hi, thank you so much for taking the question. Jensen, I had a relatively longer-term question. As you may know, there's a pretty heated debate in the market on your customers and customer's customers return on investment and what that means for the sustainability of CapEx going forward. Internally at NVIDIA, like what are you guys watching? What's on your dashboard as you try to gauge customer return and how that impacts CapEx? And then a quick follow-up maybe for Colette. I think your sovereign AI number for the full-year went up maybe a couple of billion. What's driving the improved outlook? And how should we think about fiscal '26? Thank you. Jensen Huang: Thanks, Toshiya. First of all, when I said ship production in Q4, I mean shipping out. I don't mean starting to ship, but I mean -- I don't mean starting production, but shipping out. On the longer-term question, let's take a step-back and you've heard me say that we're going through two simultaneous platform transitions at the same time. The first one is transitioning from accelerated computing to -- from general-purpose computing to accelerated computing. And the reason for that is because CPU scaling has been known to be slowing for some time. And it is slow to a crawl. And yet the amount of computing demand continues to grow quite significantly. You could maybe even estimate it to be doubling every single year. And so if we don't have a new approach, computing inflation would be driving up the cost for every company, and it would be driving up the energy consumption of data centers around the world. In fact, you're seeing that. And so the answer is accelerated computing. We know that accelerated computing, of course, speeds up applications. It also enables you to do computing at a much larger scale, for example, scientific simulations or database processing. But what that translates directly to is lower cost and lower energy consumed. And in fact, this week, there's a blog that came out that talked about a whole bunch of new libraries that we offer. And that's really the core of the first platform transition going from general purpose computing to accelerated computing. And it's not unusual to see someone save 90% of their computing cost. And the reason for that is, of course, you just sped up an application 50x, you would expect the computing cost to decline quite significantly. The second was enabled by accelerated computing because we drove down the cost of training large language models or training deep learning so incredibly, that it is now possible to have gigantic scale models, multi-trillion parameter models, and train it on -- pre-train it on just about the world's knowledge corpus and let the model go figure out how to understand a human represent -- human language representation and how to codify knowledge into its neural networks and how to learn reasoning, and so -- which caused the generative AI revolution. Now generative AI, taking a step back about why it is that we went so deeply into it is because it's not just a feature, it's not just a capability, it's a fundamental new way of doing software. Instead of human-engineered algorithms, we now have data. We tell the AI, we tell the model, we tell the computer what's the -- what are the expected answers, What are our previous observations. And then for it to figure out what the algorithm is, what's the function. It learns a universal -- AI is a bit of a universal function approximator and it learns the function. And so you could learn the function of almost anything, you know. And anything that you have that's predictable, anything that has structure, anything that you have previous examples of. And so now here we are with generative AI. It's a fundamental new form of computer science. It's affecting how every layer of computing is done from CPU to GPU, from human-engineered algorithms to machine-learned algorithms. And the type of applications you could now develop and produce is fundamentally remarkable. And there are several things that are happening in generative AI. So the first thing that's happening is the frontier models are growing in quite substantial scale. And they're still seeing -- we're still all seeing the benefits of scaling. And whenever you double the size of a model, you also have to more than double the size of the dataset to go train it. And so the amount of flops necessary in order to create that model goes up quadratically. And so it's not unusual -- it's not unexpected to see that the next-generation models could take 20 -- 10, 20, 40 times more compute than last generation. So we have to continue to drive the generational performance up quite significantly, so we can drive down the energy consumed and drive down the cost necessary to do it. So the first one is, there are larger frontier models trained on more modalities and surprisingly, there are more frontier model makers than last year. And so you have more on more on more. That's one of the dynamics going on in generative AI. The second is although it's below the tip of the iceberg. What we see are ChatGPT, image generators, we see coding. We use a generative AI for coding quite extensively here at NVIDIA now. We, of course, have a lot of digital designers and things like that. But those are kind of the tip of the iceberg. What's below the iceberg are the largest systems -- largest computing systems in the world today, which are -- and you've heard me talk about this in the past, which are recommender systems moving from CPUs, it's now moving from CPUs to generative AI. So recommended systems, ad generation, custom ad generation targeting ads at very large scale and quite hyper targeting search and user-generated content. These are all very large-scale applications have now evolved to generative AI. Of course, the number of generative AI startups is generating tens of billions of dollars of cloud renting opportunities for our cloud partners and sovereign AI. Countries that are now realizing that their data is their natural and national resource and they have to use -- they have to use AI, build their own AI infrastructure so that they could have their own digital intelligence. Enterprise AI, as Colette mentioned earlier, is starting and you might have seen our announcement that the world's leading IT companies are joining us to take the NVIDIA AI enterprise platform to the world's enterprises. The companies that we're talking to. So many of them are just so incredibly excited to drive more productivity out of their company. And then I -- and then General Robotics. The big transformation last year as we are able to now learn physical AI from watching video and human demonstration and synthetic data generation from reinforcement learning from systems like Omniverse. We are now able to work with just about every robotics companies now to start thinking about start building on general robotics. And so you can see that there are just so many different directions that generative AI is going. And so we're actually seeing the momentum of generative AI accelerating. Colette Kress: And Toshiya, to answer your question regarding sovereign AI and our goals in terms of growth, in terms of revenue, it certainly is a unique and growing opportunity, something that surfaced with generative AI and the desires of countries around the world to have their own generative AI that would be able to incorporate their own language, incorporate their own culture, incorporate their own data in that country. So more and more excitement around these models and what they can be specific for those countries. So yes, we are seeing some growth opportunity in front of us. Operator: And your next question comes from the line of Joe Moore with Morgan Stanley. Your line is open. Joe Moore: Great. Thank you. Jensen, in the press release, you talked about Blackwell anticipation being incredible, but it seems like Hopper demand is also really strong. I mean, you're guiding for a very strong quarter without Blackwell in October. So how long do you see sort of coexisting strong demand for both? And can you talk about the transition to Blackwell? Do you see people intermixing clusters? Do you think most of the Blackwell activity is new clusters? Just some sense of what that transition looks like? Jensen Huang: Yes. Thanks, Joe. The demand for Hopper is really strong and it's true. The demand for Blackwell is incredible. There's a couple of reasons for that. The first reason is, if you just look at the world's cloud service providers and the amount of GPU capacity they have available, it's basically none. And the reason for that is because they're either being deployed internally for accelerating their own workloads, data processing, for example. Data processing, we hardly ever talk about it because it's mundane. It's not very cool because it doesn't generate a picture or generate words, but almost every single company in the world processes data in the background. And NVIDIA's GPUs are the only accelerators on the planet that process and accelerate data. SQL data, Pandas data science, toolkits like Pandas and the new one, Polars, these are the most popular data processing platforms in the world. And aside from CPUs, which as I've mentioned before, really running out of steam, Nvidia's accelerated computing is really the only way to get boosting performance out of that. And so that's number one is the primary -- the number one use-case long before generative AI came along is the migration of applications one after another to accelerated computing. The second is, of course, the rentals. Their renting capacity to model makers or renting it to startup companies and a generative AI company spends the vast majority of their invested capital into infrastructure so that they could use an AI to help them create products. And so these companies need it now. They just simply can't afford -- you just raise money, you -- they want you to put it to use now. You have processing that you have to do. You can't do it next year, you got to do it today. And so there's a fair -- that's one reason. The second reason for Hopper demand right now is because of the race to the next plateau. So the first person to the next plateau, it gets to be -- gets to introduce a revolutionary level of AI. So the second person who gets there is incrementally better or about the same. And so the ability to systematically and consistently race to the next plateau and be the first one there, is how you establish leadership. NVIDIA is constantly doing that and we show that to the world and the GPUs we make and AI factories that we make, the networking systems that we make, the SOCs we create. I mean, we want to set the pace. We want to be consistently the world's best. And that's the reason why, we drive ourselves so hard. And of course, we also want to see our dreams come true, and all of the capabilities that we imagine in the future, and the benefits that we can bring to society. We want to see all that come true. And so these model makers are the same. They're -- of course, they want to be the world's best, they want to be the world's first. And although Blackwell will start shipping out in billions of dollars at the end of this year, the standing up of the capacity is still probably weeks and a month or so away. And so between now and then is a lot of generative AI market dynamic. And so everybody is just really in a hurry. It's either operational reasons that they need it, they need accelerated computing. They don't want to build any more general-purpose computing infrastructure and even Hopper, you know, of course, H200 state-of-the-art. Hopper, if you have a choice between building CPU infrastructure right now for business or Hopper infrastructure for business right now, that decision is relatively clear. And so I think people are just clamoring to transition the trillion dollars of established installed infrastructure to a modern infrastructure in Hopper's state-of-the-art. Operator: And your next question comes from the line of Matt Ramsay with TD Cowen. Your line is open. Matt Ramsay: Thank you very much. Good afternoon, everybody. I wanted to kind of circle back to an earlier question about the debate that investors are having about, I don't know, the ROI on all of this CapEx, and hopefully this question and the distinction will make some sense. But what I'm having discussions about is with like the percentage of folks that you see that are spending all of this money and looking to sort of push the frontier towards AGI convergence and as you just said, a new plateau and capability. And they're going to spend regardless to get to that level of capability because it opens up so many doors for the industry and for their company versus customers that are really, really focused today on CapEx versus ROI. I don't know if that distinction makes sense. I'm just trying to get a sense of how you're seeing the priorities of people that are putting the dollars in the ground on this new technology and what their priorities are and their time frames are for that investment? Thanks. Jensen Huang: Thanks, Matt. The people who are investing in NVIDIA infrastructure are getting returns on it right away. It's the best ROI infrastructure -- computing infrastructure investment you can make today. And so one way to think through it, probably the most -- the easiest way to think through is just go back to first principles. You have trillion dollars’ worth of general-purpose computing infrastructure and the question is, do you want to build more of that or not? And for every $1 billion worth of general CPU-based infrastructure that you stand up, you probably rent it for less than $1 billion. And so because it's commoditized. There's already a trillion dollars on the ground. What's the point of getting more? And so the people who are clamoring to get this infrastructure, one, when they build-out Hopper-based infrastructure and soon Blackwell-based infrastructure, they start saving money. That's tremendous return on investment. And the reason why they start saving money is because data processing saves money, data processing is pricing, just a giant part of it already. And so recommended system save money, so on and so forth, okay? And so you start saving money. The second thing is everything you stand-up are going to get rented because so many companies are being founded to create generative AI. And so your capacity gets rented right away and the return on investment of that is really good. And then the third reason is your own business. Do you want to either create the next frontier yourself or your own Internet services benefit from a next-generation ad system or a next-generation recommended system or next-generation search system? So for your own services, for your own stores, for your own user-generated content, social media platforms for your own services, generative AI is also a fast ROI. And so there's a lot of ways you could think through it. But at the core, it's because it is the best computing infrastructure you could put in the ground today. The world of general-purpose computing is shifting to accelerated computing. The world of human-engineered software is moving to generative AI software. If you were to build infrastructure to modernize your cloud and your data centers, build it with accelerated computing NVIDIA. That's the best way to do it. Operator: And your next question comes from the line of Timothy Arcuri with UBS. Your line is open. Timothy Arcuri: Thanks a lot. I had a question on the shape of the revenue growth both near and longer-term. I know, Colette, you did increase OpEx for the year. And if I look at the increase in your purchase commitments and your supply obligations, that's also quite bullish. On the other hand, there's some school of thought that not that many customers really seem ready for liquid cooling and I do recognize that some of these racks can be air-cooled. But Jensen, is that something to consider sort of on the shape of how Blackwell is going to ramp? And then I guess when you look beyond next year, which is obviously going to be a great year and you look into '26, do you worry about any other gating factors like, say, the power, supply-chain or at some point, models start to get smaller? I'm just wondering if you could speak to that? Thanks. Jensen Huang: I'm going to work backwards. I really appreciate the question, Tim. So remember, the world is moving from general purpose computing to accelerated computing. And the world builds about $1 trillion dollars’ worth of data centers -- $1 trillion dollars’ worth of data centers in a few years will be all accelerated computing. In the past, no GPUs are in data centers, just CPUs. In the future, every single data center will have GPUs. And the reason for that is very clear is because we need to accelerate workloads so that we can continue to be sustainable, continue to drive down the cost of computing so that when we do more computing our -- we don't experience computing inflation. Second, we need GPUs for a new computing model called generative AI that we can all acknowledge is going to be quite transformative to the future of computing. And so I think working backwards, the way to think about that is the next trillion dollars of the world's infrastructure will clearly be different than the last trillion, and it will be vastly accelerated. With respect to the shape of our ramp, we offer multiple configurations of Blackwell. Blackwell comes in either a Blackwell classic, if you will, that uses the HGX form factor that we pioneered with Volta, and I think it was Volta. And so we've been shipping the HGX form factor for some time. It is air-cooled. The Grace Blackwell is liquid-cooled. However, the number of data centers that want to go liquid-cooled is quite significant. And the reason for that is because we can in a liquid-cooled data center, in any data center -- power limited data center, whatever size data center you choose, you could install and deploy anywhere from 3 times to 5 times, the AI throughput compared to the past. And so liquid cooling is cheaper, liquid cooling TCO is better, and liquid cooling allows you to have the benefit of this capability we call NVLink , which allows us to expand it to 72 Grace Blackwell packages, which has essentially 144 GPUs. And so imagine 144 GPUs connected in NVLink and that is -- we're increasingly showing you the benefits of that. And the next click is obviously a very low-latency, very-high throughput, large language model inference. And the large domain is going to be a game-changer for that. And so I think people are very comfortable deploying both. And so almost every CSP we're working with are deploying some of both. And so I -- I'm pretty confident that we'll ramp it up just fine. Your second question out of the third is that looking-forward, yes, next year is going to be a great year. We expect to grow our data center business quite significantly next year. Blackwell is going to be going to be a complete game-changer for the industry. And Blackwell is going to carry into the following year. And as I mentioned earlier, working backwards from first principles. Remember that computing is going through two platform transitions at the same time and that's just really, really important to keep your head on -- your mind focused on, which is general-purpose computing is shifting to accelerated computing and human engineered software is going to transition to generative AI or artificial intelligence learned software. Operator: And your next question comes from the line of Stacy Rasgon with Bernstein Research. Your line is open. Stacy Rasgon: Hi guys. Thanks for taking my questions. I have two short questions for Colette. The first, several billion dollars of Blackwell revenue in Q4. I guess is that additive? You said you expected Hopper demand to strengthen in the second half. Does that mean Hopper strengthens Q3 to Q4 as well on top of Blackwell adding several billion dollars? And the second question on gross margins, if I have mid-70s for the year, explaining where I want to draw that. If I have 75% for the year, I'd be something like 71% to 72% for Q4 somewhere in that range. Is that the kind of exit rate for gross margins that you're expecting? And how should we think about the drivers of gross margin evolution into next year as Blackwell ramps? And I mean, hopefully, I guess the yields and the inventory reserves and everything come up. Colette Kress: Yes. So Stacy, let's first take your question that you had about Hopper and Blackwell. So we believe our Hopper will continue to grow into the second half. We have many new products for Hopper, our existing products for Hopper that we believe will start continuing to ramp, in the next quarters, including our Q3 and those new products moving to Q4. So let's say Hopper there for versus H1 is a growth opportunity for that. Additionally, we have the Blackwell on top of that, and the Blackwell starting of -- ramping in Q4. So hope that helps you on those two pieces. Your second piece is in terms of on our gross margin. We provided gross margin for our Q3. We provided our gross margin on a non-GAAP at about 75%. We'll work with all the different transitions that we're going through, but we do believe we can do that 75% in Q3. We provided that we're still on track for the full-year also in the mid-70s or approximately the 75%. So we're going to see some slight difference possibly in Q4. Again, with our transitions and the different cost structures that we have on our new product introductions. However, I'm not in the same number that you are there. We don't have exactly guidance, but I do believe you're lower than where we are. Operator: And your next question comes from the line of Ben Reitzes with Melius. Your line is open. Ben Reitzes: Yes, hey, thanks a lot for the question, Jensen and Colette. I wanted to ask about the geographies. There was the 10-Q that came out and the United States was down sequentially while several Asian geographies were up a lot sequentially. Just wondering what the dynamics are there? Obviously, China did very well. You mentioned it in your remarks, what are the puts and takes? And then I just wanted to clarify from Stacy's question, if that means the sequential overall revenue growth rates for the company accelerate in the fourth quarter given all those favorable revenue dynamics? Thanks. Colette Kress: Let me talk about a bit in terms of our disclosure in terms of the 10-Q, a required disclosure, and a choice of geographies. Very challenging sometimes to create that right disclosure as we have to come up with one key piece. Piece is in terms of we have in terms of who we sell to and/or specifically who we invoice to. And so what you're seeing in terms of there is who we invoice. That's not necessarily where the product will eventually be, and where it may even travel to the end-customer. These are just moving to our OEMs, our ODMs, and our system integrators for the most part across our product portfolio. So what you're seeing there is sometimes just a swift shift in terms of who they are using to complete their full configuration before those things are going into the data center, going into notebooks and those pieces of it. And that shift happens from time-to-time. But yes, our China number there are inverse into China, keep in mind that is incorporating both gaming, also data center, also automotive in those numbers that we have. Going back to your statement in regarding gross margin and also what we're seeing in terms of what we're looking at for Hopper and Blackwell in terms of revenue. Hopper will continue to grow in the second half. We'll continue to grow from what we are currently seeing. During -- determining that exact mix in each Q3 and Q4, we don't have here. We are not here to guide yet in terms of Q4. But we do see right now the demand expectations. We do see the visibility that will be a growth opportunity in Q4. On top of that, we will have our Blackwell architecture. Operator: And your next question comes from the line of C.J. Muse with Cantor Fitzgerald. Your line is open. C.J. Muse: Yes, good afternoon. Thank you for taking the question. You've embarked on a remarkable annual product cadence with challenges only likely becoming more and more given rising complexity and a radical limit in advanced package world. So curious, if you take a step back, how does this backdrop alter your thinking around potentially greater vertical integration, supply chain partnerships and then thinking through consequential impact to your margin profile? Thank you. Jensen Huang: Yes, thanks. Let's see. I think the first answer to your -- the answer to your first question is that the reason why our velocity is so high is simultaneously because, the complexity of the model is growing and we want to continue to drive its cost down. It's growing, so we want to continue to increase its scale. And we believe that by continuing to scale the AI models that will reach a level of extraordinary usefulness and that it would open up, I realize the next industrial revolution. We believe it. And so we're going to drive ourselves really hard to continue to go up that scale. We have the ability fairly uniquely to integrate -- to design an AI factory because we have all the parts. It's not possible to come up with a new AI factory every year, unless you have all the parts. And so we have -- next year, we're going to ship a lot more CPUs than we've ever had in the history of our company, more GPUs, of course, but also NVLink switches, CX DPUs, ConnectX EPU for East and West, Bluefield DPUs for North and South and data and storage processing to InfiniBand for supercomputing centers to Ethernet, which is a brand-new product for us, which is well on its way to becoming a multi-billion dollar business to bring AI to Ethernet. And so the fact that we could build -- we have access to all of this. We have one architectural stack, as you know. It allows us to introduce new capabilities to the market as we complete it. Otherwise, what happens is, you ship these parts, you go find customers to sell it to and then you've got to build -- somebody has got to build up an AI factory. And the AI factory has got a mountain of software. And so it's not about who integrates it. We love the fact that our supply chain is disintegrated in the sense that we could service Quanta, Foxconn, HP, Dell, Lenovo, Supermicro. We used to be able to serve as ZT. They were recently purchased and so on and so forth. And so the number of ecosystem partners that we have, Gigabyte, ASUS, the number of ecosystem partners that we have that allows us allows us to -- allows them to take our architecture, which all works, but integrated in a bespoke way into all of the world's cloud service providers, enterprise data centers. The scale and reach necessary from our ODMs and our integrator supply-chain is vast and gigantic because the world is huge. And so that part we don't want to do and we're not good at doing. And I -- but we know how to design the AI infrastructure, provided the way that customers would like it and lets the ecosystem integrate it. Well, yes. So anyways, that's the reason why. Operator: And your final question comes from the line of Aaron Rakers with Wells Fargo. Your line is open. Aaron Rakers: Yes, thanks for taking the questions. I wanted to go back into the Blackwell product cycle. One of the questions that we tend to get asked is how you see the Rack Scale system mix dynamic as you think about leveraging NVLink? You think about GB, NVL72, and how that go-to-market dynamic looks as far as the Blackwell product cycle? I guess put this distinctly, how do you see that mix of Rack Scale systems as we start to think about the Blackwell cycle playing out? Jensen Huang: Yes, Aaron, thanks. The Blackwell Rack system, it's designed and architected as a rack, but it's sold, in a disaggregated system components. We don't sell the whole rack. And the reason for that is because everybody's rack is a little different, surprisingly. You know, some of them are OCP standards, some of them are not, some of them are enterprise, and the power limits for everybody could be a little different. Choice of CDUs, the choice of power bus bars, the configuration and integration into people's data centers, all different. And so the way we designed it, we architected the whole rack. The software is going to work perfectly across the whole rack. And then we provide the system components, like for example, the CPU and GPU compute board is then integrated into an MGX, it's a modular system architecture. MGX is completely ingenious. And we have MGX ODMs and integrators and OEMs all over the plant. And so just about any configuration you would like, where you would like that 3,000 pound rack to be delivered. It's got to be close to -- it has to be integrated and assembled close to the data center because it's fairly heavy. And so everything from the supply chain from the moment that we ship the GPU, CPUs, the switches, the NICs. From that point forward, the integration is done quite close to the location of the CSPs and the locations of the data centers. And so you can imagine how many data centers in the world there are and how many logistics hubs, we've scaled out to with our ODM partners. And so I think that because we show it as one rack and because it's always rendered that way and shown that way, we might have left the impression that we're doing the integration. Our customers hate that we do integration. The supply-chain hates us doing integration. They want to do the integration. That's their value-added. There's a final design in, if you will. It's not quite as simple as shimmy into a data center, but the design fit in is really complicated. And so the install -- the design fit-in, the installation, the bring-up, the repair and replace, that entire cycle is done all over the world. And we have a sprawling network of ODM and OEM partners that does this incredibly well. So integration is not the reason why we're doing racks. It's the anti-reason of doing it. The way we don't want to be an integrator. We want to be a technology provider. Operator: And I will now turn the call back over to Jensen Huang for closing remarks. Jensen Huang: Thank you. Let me make a couple more -- make a couple of comments that I made earlier again. That data center worldwide are in full steam to modernize the entire computing stack with accelerated computing and generative AI. Hopper demand remains strong and the anticipation for Blackwell is incredible. Let me highlight the top-five things, the top-five things of our company. Accelerated computing has reached a tipping point, CPU scaling slows, developers must accelerate everything possible. Accelerated computing starts with CUDA-X libraries. New libraries open new markets for NVIDIA. We released many new libraries, including accelerated Polars, Pandas, and Spark, the leading data science and data processing libraries, QVS for vector product vector databases. This is incredibly hot right now. Aerial and Sionna for 5G wireless base station, a whole suite of -- a whole world of data centers that we can go into now. Parabricks for gene sequencing and Alpha-fold two for protein structure prediction is now CUDA accelerated. We are at the beginning of our journey to modernize a $1 trillion dollars’ worth of data centers from general-purpose computing to accelerated computing. That's number-one. Number two, Blackwell is a step-function leap over Hopper. Blackwell is an AI infrastructure platform, not just a GPU. It also happens to be in the name of our GPU, but it's an AI infrastructure platform. As we reveal more of Blackwell and sample systems to our partners and customers, the extent of Blackwell's lead becomes clear. The Blackwell Vision took nearly five years and seven one-of-a-kind chips to realize. The Grace CPU, the Blackwell dual GPU, and a coVS package, ConnectX DPU for East-West traffic, BlueField DPU for North-South and storage traffic, NVLink switch for all-to-all GPU communications, and Quantum and Spectrum-X for both InfiniBand and Ethernet can support the massive burst traffic of AI. Blackwell AI factories are building-sized computers. NVIDIA designed and optimized the Blackwell platform full stack end-to-end from chips, systems, networking, even structured cables, power and cooling, and mounts of software to make it fast for customers to build AI factories. These are very capital-intensive infrastructures. Customers want to deploy it as soon as they get their hands on the equipment and deliver the best performance and TCO. Blackwell provides 3 times to 5 times more AI throughput in a power-limited data center than Hopper. The third is NVLink. This is a very big deal with its all-to-all GPU switch is game-changing. The Blackwell system lets us connect 144 GPUs in 72-GB 200 packages into one NVLink domain, with an aggregate NVLink bandwidth of 259 terabytes per second in one rack. Just to put that in perspective, that's about 10 times higher than Hopper, 259 terabytes per second, it kind of makes sense because you need to boost the training of multi-trillion parameter models on trillions of tokens. And so that natural amount of data needs to be moved around from GPU to GPU. For inference, NVLink is vital for low-latency, high-throughput, large language model, token generation. We now have three networking platforms, NVLink for GPU scale-up, Quantum InfiniBand for supercomputing and dedicated AI factories, and Spectrum-X for AI on Ethernet. NVIDIA's networking footprint is much bigger than before. Generative AI momentum is accelerating. Generative AI frontier model makers are racing to scale to the next AI plateau to increase model safety and IQ. We're also scaling to understand more modalities from text, images, and video to 3D, physics, chemistry, and biology. Chatbots, coding AIs, and image generators are growing fast, but it's just the tip of the iceberg. Internet services are deploying generative AI for large-scale recommenders, ad targeting, and search systems. AI start-ups are consuming tens of billions of dollars yearly of CSP's cloud capacity and countries are recognizing the importance of AI and investing in sovereign AI infrastructure. And NVIDIA AI and NVIDIA Omniverse is opening up the next era of AI general robotics. And now the enterprise AI wave has started and we're poised to help companies transform their businesses. The NVIDIA AI Enterprise platform consists of NeMo, NIMS, NIM agent blueprints, and AI Foundry, that our ecosystem partners the world-leading IT companies used to help customer -- companies customize AI models and build bespoke AI applications. Enterprises can then deploy on NVIDIA AI Enterprise runtime and at $4,500 per GPU per year, NVIDIA AI Enterprise is an exceptional value for deploying AI anywhere. And for NVIDIA's software TAM can be significant as the CUDA-compatible GPU installed-base grows from millions to tens of millions. And as Colette mentioned, NVIDIA software will exit the year at a $2 billion run rate. Thank you all for joining us today. Operator: Ladies and gentlemen, this concludes today's call and we thank you for your participation. You may now disconnect.

Operator: Good afternoon. My name is Regina and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's First Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question-and-answer session. [Operator Instructions] Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2025. With me today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2025. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 22, 2024, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. Let me highlight some upcoming events. On Sunday, June 2nd, ahead of the Computex Technology Trade Show in Taiwan, Jensen will deliver a keynote which will be held in-person in Taipei as well as streamed live. And on June 5th, we will present at the Bank of America Technology Conference in San Francisco. With that let me turn the call over to Colette. Colette Kress: Thanks, Simona. Q1 was another record quarter. Revenue of $26 billion was up 18% sequentially and up 262% year-on-year and well above our outlook of $24 billion. Starting with Data Center. Data Center revenue of $22.6 billion was a record, up 23% sequentially and up 427% year-on-year, driven by continued strong demand for the NVIDIA Hopper GPU computing platform. Compute revenue grew more than 5x and networking revenue more than 3x from last year. Strong sequential data center growth was driven by all customer types, led by enterprise and consumer internet companies. Large cloud providers continue to drive strong growth as they deploy and ramp NVIDIA AI infrastructure at scale and represented the mid-40s as a percentage of our Data Center revenue. Training and inferencing AI on NVIDIA CUDA is driving meaningful acceleration in cloud rental revenue growth, delivering an immediate and strong return on cloud provider's investment. For every $1 spent on NVIDIA AI infrastructure, cloud providers have an opportunity to earn $5 in GPU instant hosting revenue over four years. NVIDIA's rich software stack and ecosystem and tight integration with cloud providers makes it easy for end customers up and running on NVIDIA GPU instances in the public cloud. For cloud rental customers, NVIDIA GPUs offer the best time to train models, the lowest cost to train models and the lowest cost to inference large language models. For public cloud providers, NVIDIA brings customers to their cloud, driving revenue growth and returns on their infrastructure investments. Leading LLM companies such as OpenAI, Adept, Anthropic, Character.AI, Cohere, Databricks, DeepMind, Meta, Mistral, xAI, and many others are building on NVIDIA AI in the cloud. Enterprises drove strong sequential growth in Data Center this quarter. We supported Tesla's expansion of their training AI cluster to 35,000 H100 GPUs. Their use of NVIDIA AI infrastructure paved the way for the breakthrough performance of FSD Version 12, their latest autonomous driving software based on Vision. Video Transformers, while consuming significantly more computing, are enabling dramatically better autonomous driving capabilities and propelling significant growth for NVIDIA AI infrastructure across the automotive industry. We expect automotive to be our largest enterprise vertical within Data Center this year, driving a multibillion revenue opportunity across on-prem and cloud consumption. Consumer Internet companies are also a strong growth vertical. A big highlight this quarter was Meta's announcement of Llama 3, their latest large language model, which was trained on a cluster of 24,000 H100 GPUs. Llama 3 powers Meta AI, a new AI assistant available on Facebook, Instagram, WhatsApp and Messenger. Llama 3 is openly available and has kickstarted a wave of AI development across industries. As generative AI makes its way into more consumer Internet applications, we expect to see continued growth opportunities as inference scales both with model complexity as well as with the number of users and number of queries per user, driving much more demand for AI compute. In our trailing four quarters, we estimate that inference drove about 40% of our Data Center revenue. Both training and inference are growing significantly. Large clusters like the ones built by Meta and Tesla are examples of the essential infrastructure for AI production, what we refer to as AI factories. These next-generation data centers host advanced full-stack accelerated computing platforms where the data comes in and intelligence comes out. In Q1, we worked with over 100 customers building AI factories ranging in size from hundreds to tens of thousands of GPUs, with some reaching 100,000 GPUs. From a geographic perspective, Data Center revenue continues to diversify as countries around the world invest in Sovereign AI. Sovereign AI refers to a nation's capabilities to produce artificial intelligence using its own infrastructure, data, workforce and business networks. Nations are building up domestic computing capacity through various models. Some are procuring and operating Sovereign AI clouds in collaboration with state-owned telecommunication providers or utilities. Others are sponsoring local cloud partners to provide a shared AI computing platform for public and private sector use. For example, Japan plans to invest more than $740 million in key digital infrastructure providers, including KDDI, Sakura Internet, and SoftBank to build out the nation's Sovereign AI infrastructure. France-based, Scaleway, a subsidiary of the Iliad Group, is building Europe's most powerful cloud native AI supercomputer. In Italy, Swisscom Group will build the nation's first and most powerful NVIDIA DGX-powered supercomputer to develop the first LLM natively trained in the Italian language. And in Singapore, the National Supercomputer Center is getting upgraded with NVIDIA Hopper GPUs, while Singtel is building NVIDIA's accelerated AI factories across Southeast Asia. NVIDIA's ability to offer end-to-end compute to networking technologies, full-stack software, AI expertise, and rich ecosystem of partners and customers allows Sovereign AI and regional cloud providers to jumpstart their country's AI ambitions. From nothing the previous year, we believe Sovereign AI revenue can approach the high single-digit billions this year. The importance of AI has caught the attention of every nation. We ramped new products designed specifically for China that don't require an export control license. Our Data Center revenue in China is down significantly from the level prior to the imposition of the new export control restrictions in October. We expect the market in China to remain very competitive going forward. From a product perspective, the vast majority of compute revenue was driven by our Hopper GPU architecture. Demand for Hopper during the quarter continues to increase. Thanks to CUDA algorithm innovations, we've been able to accelerate LLM inference on H100 by up to 3x, which can translate to a 3x cost reduction for serving popular models like Llama 3. We started sampling the H200 in Q1 and are currently in production with shipments on track for Q2. The first H200 system was delivered by Jensen to Sam Altman and the team at OpenAI and powered their amazing GPT-4o demos last week. H200 nearly doubles the inference performance of H100, delivering significant value for production deployments. For example, using Llama 3 with 700 billion parameters, a single NVIDIA HGX H200 server can deliver 24,000 tokens per second, supporting more than 2,400 users at the same time. That means for every $1 spent on NVIDIA HGX H200 servers at current prices per token, an API provider serving Llama 3 tokens can generate $7 in revenue over four years. With ongoing software optimizations, we continue to improve the performance of NVIDIA AI infrastructure for serving AI models. While supply for H100 prove, we are still constrained on H200. At the same time, Blackwell is in full production. We are working to bring up our system and cloud partners for global availability later this year. Demand for H200 and Blackwell is well ahead of supply and we expect demand may exceed supply well into next year. Grace Hopper Superchip is shipping in volume. Last week at the International Supercomputing Conference, we announced that nine new supercomputers worldwide are using Grace Hopper for a combined 200 exaflops of energy-efficient AI processing power delivered this year. These include the Alps Supercomputer at the Swiss National Supercomputing Center, the fastest AI supercomputer in Europe. Isambard-AI at the University of Bristol in the UK and JUPITER in the Julich Supercomputing Center in Germany. We are seeing an 80% attach rate of Grace Hopper in supercomputing due to its high energy efficiency and performance. We are also proud to see supercomputers powered with Grace Hopper take the number one, the number two, and the number three spots of the most energy-efficient supercomputers in the world. Strong networking year-on-year growth was driven by InfiniBand. We experienced a modest sequential decline, which was largely due to the timing of supply, with demand well ahead of what we were able to ship. We expect networking to return to sequential growth in Q2. In the first quarter, we started shipping our new Spectrum-X Ethernet networking solution optimized for AI from the ground up. It includes our Spectrum-4 switch, BlueField-3 DPU, and new software technologies to overcome the challenges of AI on Ethernet to deliver 1.6x higher networking performance for AI processing compared with traditional Ethernet. Spectrum-X is ramping in volume with multiple customers, including a massive 100,000 GPU cluster. Spectrum-X opens a brand-new market to NVIDIA networking and enables Ethernet only data centers to accommodate large-scale AI. We expect Spectrum-X to jump to a multibillion-dollar product line within a year. At GTC in March, we launched our next-generation AI factory platform, Blackwell. The Blackwell GPU architecture delivers up to 4x faster training and 30x faster inference than the H100 and enables real-time generative AI on trillion-parameter large language models. Blackwell is a giant leap with up to 25x lower TCO and energy consumption than Hopper. The Blackwell platform includes the fifth-generation NVLink with a multi-GPU spine and new InfiniBand and Ethernet switches, the X800 series designed for a trillion parameter scale AI. Blackwell is designed to support data centers universally, from hyperscale to enterprise, training to inference, x86 to Grace CPUs, Ethernet to InfiniBand networking, and air cooling to liquid cooling. Blackwell will be available in over 100 OEM and ODM systems at launch, more than double the number of Hopper's launch and representing every major computer maker in the world. This will support fast and broad adoption across the customer types, workloads and data center environments in the first year shipments. Blackwell time-to-market customers include Amazon, Google, Meta, Microsoft, OpenAI, Oracle, Tesla, and xAI. We announced a new software product with the introduction of NVIDIA Inference Microservices or NIM. NIM provides secure and performance-optimized containers powered by NVIDIA CUDA acceleration in network computing and inference software, including Triton Inference Server and TensorRT LLM with industry-standard APIs for a broad range of use cases, including large language models for text, speech, imaging, vision, robotics, genomics and digital biology. They enable developers to quickly build and deploy generative AI applications using leading models from NVIDIA, AI21, Adept, Cohere, Getty Images, and Shutterstock and open models from Google, Hugging Face, Meta, Microsoft, Mistral AI, Snowflake and Stability AI. NIMs will be offered as part of our NVIDIA AI enterprise software platform for production deployment in the cloud or on-prem. Moving to gaming and AI PCs. Gaming revenue of $2.65 billion was down 8% sequentially and up 18% year-on-year, consistent with our outlook for a seasonal decline. The GeForce RTX Super GPUs market reception is strong and end demand and channel inventory remained healthy across the product range. From the very start of our AI journey, we equipped GeForce RTX GPUs with CUDA Tensor Cores. Now with over 100 million of an installed base, GeForce RTX GPUs are perfect for gamers, creators, AI enthusiasts and offer unmatched performance for running generative AI applications on PCs. NVIDIA has full technology stack for deploying and running fast and efficient generative AI inference on GeForce RTX PCs. TensorRT LLM now accelerates Microsoft's Phi-3-Mini model and Google's Gemma 2B and 7B models as well as popular AI frameworks, including LangChain and LlamaIndex. Yesterday, NVIDIA and Microsoft announced AI performance optimizations for Windows to help run LLMs up to 3x faster on NVIDIA GeForce RTX AI PCs. And top game developers, including NetEase Games, Tencent and Ubisoft are embracing NVIDIA Avatar Character Engine to create lifelike avatars to transform interactions between gamers and nonplayable characters. Moving to ProVis. Revenue of $427 million was down 8% sequentially and up 45% year-on-year. We believe generative AI and Omniverse industrial digitalization will drive the next wave of professional visualization growth. At GTC, we announced new Omniverse Cloud APIs to enable developers to integrate Omniverse industrial digital twin and simulation technologies into their applications. Some of the world's largest industrial software makers are adopting these APIs, including ANSYS, Cadence, 3DEXCITE at Dassault Systemes, Brand and Siemens. And developers can use them to stream industrial digital twins with spatial computing devices such as Apple Vision Pro. Omniverse Cloud APIs will be available on Microsoft Azure later this year. Companies are using Omniverse to digitalize their workflows. Omniverse power digital twins enable Wistron, one of our manufacturing partners to reduce end-to-end production cycle times by 50% and defect rates by 40%. And BYD, the world's largest electric vehicle maker, is adopting Omniverse for virtual factory planning and retail configurations. Moving to automotive. Revenue was $329 million, up 17% sequentially and up 11% year-on-year. Sequential growth was driven by the ramp of AI cockpit solutions with global OEM customers and strength in our self-driving platforms. Year-on-year growth was driven primarily by self-driving. We supported Xiaomi in the successful launch of its first electric vehicle, the SU7 sedan built on the NVIDIA DRIVE Orin, our AI car computer for software-defined AV fleets. We also announced a number of new design wins on NVIDIA DRIVE Thor, the successor to Orin, powered by the new NVIDIA Blackwell architecture with several leading EV makers, including BYD, XPeng, GAC's Aion Hyper and Neuro. DRIVE Thor is slated for production vehicles starting next year. Okay. Moving to the rest of the P&L. GAAP gross margin expanded sequentially to 78.4% and non-GAAP gross margins to 78.9% on lower inventory targets. As noted last quarter, both Q4 and Q1 benefited from favorable component costs. Sequentially, GAAP operating expenses were up 10% and non-GAAP operating expenses were up 13%, primarily reflecting higher compensation-related costs and increased compute and infrastructure investments. In Q1, we returned $7.8 billion to shareholders in the form of share repurchases and cash dividends. Today, we announced a 10-for-1 split of our shares with June 10th as the first day of trading on a split-adjusted basis. We are also increasing our dividend by 150%. Let me turn to the outlook for the second quarter. Total revenue is expected to be $28 billion, plus or minus 2%. We expect sequential growth in all market platforms. GAAP and non-GAAP gross margins are expected to be 74.8% and 75.5%, respectively, plus or minus 50 basis points, consistent with our discussion last quarter. For the full year, we expect gross margins to be in the mid-70s percent range. GAAP and non-GAAP operating expenses are expected to be approximately $4 billion and $2.8 billion, respectively. Full year OpEx is expected to grow in the low 40% range. GAAP and non-GAAP other income and expenses are expected to be an income of approximately, excuse me, approximately $300 million, excluding gains and losses from nonaffiliated investments. GAAP and non-GAAP tax rates are expected to be 17%, plus or minus 1%, excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website. I would like to now turn it over to Jensen as he would like to make a few comments. Jensen Huang: Thanks, Colette. The industry is going through a major change. Before we start Q&A, let me give you some perspective on the importance of the transformation. The next industrial revolution has begun. Companies and countries are partnering with NVIDIA to shift the trillion-dollar installed base of traditional data centers to accelerated computing and build a new type of data center, AI factories, to produce a new commodity, artificial intelligence. AI will bring significant productivity gains to nearly every industry and help companies be more cost and energy efficient while expanding revenue opportunities. CSPs were the first generative AI movers. With NVIDIA, CSPs accelerated workloads to save money and power. The tokens generated by NVIDIA Hopper drive revenues for their AI services. And NVIDIA cloud instances attract rental customers from our rich ecosystem of developers. Strong and accelerated demand -- accelerating demand for generative AI training and inference on Hopper platform propels our Data Center growth. Training continues to scale as models learn to be multimodal, understanding text, speech, images, video and 3D and learn to reason and plan. Our inference workloads are growing incredibly. With generative AI, inference, which is now about fast token generation at massive scale, has become incredibly complex. Generative AI is driving a from-foundation-up full stack computing platform shift that will transform every computer interaction. From today's information retrieval model, we are shifting to an answers and skills generation model of computing. AI will understand context and our intentions, be knowledgeable, reason, plan and perform tasks. We are fundamentally changing how computing works and what computers can do, from general purpose CPU to GPU accelerated computing, from instruction-driven software to intention-understanding models, from retrieving information to performing skills, and at the industrial level, from producing software to generating tokens, manufacturing digital intelligence. Token generation will drive a multiyear build-out of AI factories. Beyond cloud service providers, generative AI has expanded to consumer Internet companies and enterprise, Sovereign AI, automotive, and health care customers, creating multiple multibillion-dollar vertical markets. The Blackwell platform is in full production and forms the foundation for trillion-parameter scale generative AI. The combination of Grace CPU, Blackwell GPUs, NVLink, Quantum, Spectrum, mix and switches, high-speed interconnects and a rich ecosystem of software and partners let us expand and offer a richer and more complete solution for AI factories than previous generations. Spectrum-X opens a brand-new market for us to bring large-scale AI to Ethernet-only data centers. And NVIDIA NIMs is our new software offering that delivers enterprise-grade optimized generative AI to run on CUDA everywhere, from the cloud to on-prem data centers to RTX AI PCs through our expansive network of ecosystem partners. From Blackwell to Spectrum-X to NIMs, we are poised for the next wave of growth. Thank you. Simona Jankowski: Thank you, Jensen. We will now open the call for questions. Operator, could you please poll for questions? Operator: [Operator Instructions] Your first question comes from the line of Stacy Rasgon with Bernstein. Please go ahead. Stacy Rasgon: Hi, guys. Thanks for taking my questions. My first one, I wanted to drill a little bit into the Blackwell comment that it's in full production now. What does that suggest with regard to shipments and delivery timing if that product is -- doesn't sound like it's sampling anymore. What does that mean when that's actually in customers' hands if it's in production now? Jensen Huang: We will be shipping. Well, we've been in production for a little bit of time. But our production shipments will start in Q2 and ramp in Q3, and customers should have data centers stood up in Q4. Stacy Rasgon: Got it. So this year, we will see Blackwell revenue, it sounds like? Jensen Huang: We will see a lot of Blackwell revenue this year. Operator: Our next question will come from the line of Timothy Arcuri with UBS. Please go ahead. Timothy Arcuri: Thanks a lot. I wanted to ask, Jensen, about the deployment of Blackwell versus Hopper just between the systems nature and all the demand for GB that you have. How does the deployment of this stuff differ from Hopper? I guess I ask because liquid cooling at scale hasn't been done before, and there's some engineering challenges both at the node level and within the data center. So do these complexities sort of elongate the transition? And how do you sort of think about how that's all going? Thanks. Jensen Huang: Yes. Blackwell comes in many configurations. Blackwell is a platform, not a GPU. And the platform includes support for air cooled, liquid cooled, x86 and Grace, InfiniBand, now Spectrum-X and very large NVLink domain that I demonstrated at GTC, that I showed at GTC. And so for some customers, they will ramp into their existing installed base of data centers that are already shipping Hoppers. They will easily transition from H100 to H200 to B100. And so Blackwell systems have been designed to be backwards compatible, if you will, electrically, mechanically. And of course, the software stack that runs on Hopper will run fantastically on Blackwell. We also have been priming the pump, if you will, with the entire ecosystem, getting them ready for liquid cooling. We've been talking to the ecosystem about Blackwell for quite some time. And the CSPs, the data centers, the ODMs, the system makers, our supply chain beyond them, the cooling supply chain base, liquid cooling supply chain base, data center supply chain base, no one is going to be surprised with Blackwell coming and the capabilities that we would like to deliver with Grace Blackwell 200. GB200 is going to be exceptional. Operator: Our next question will come from the line of Vivek Arya with Bank of America Securities. Please go ahead. Vivek Arya: Thanks for taking my question. Jensen, how are you ensuring that there is enough utilization of your products and that there isn't a pull-ahead or holding behavior because of tight supply, competition or other factors? Basically, what checks have you built in the system to give us confidence that monetization is keeping pace with your really very strong shipment growth? Jensen Huang: Well, I guess, there's the big picture view that I'll come to, and then, but I'll answer your question directly. The demand for GPUs in all the data centers is incredible. We're racing every single day. And the reason for that is because applications like ChatGPT and GPT-4o, and now it's going to be multi-modality and Gemini and its ramp and Anthropic and all of the work that's being done at all the CSPs are consuming every GPU that's out there. There's also a long line of generative AI startups, some 15,000, 20,000 startups that in all different fields from multimedia to digital characters, of course, all kinds of design tool application -- productivity applications, digital biology, the moving of the AV industry to video, so that they can train end-to-end models, to expand the operating domain of self-driving cars. The list is just quite extraordinary. We're racing actually. Customers are putting a lot of pressure on us to deliver the systems and stand it up as quickly as possible. And of course, I haven't even mentioned all of the Sovereign AIs who would like to train all of their regional natural resource of their country, which is their data to train their regional models. And there's a lot of pressure to stand those systems up. So anyhow, the demand, I think, is really, really high and it outstrips our supply. Longer term, that's what -- that's the reason why I jumped in to make a few comments. Longer term, we're completely redesigning how computers work. And this is a platform shift. Of course, it's been compared to other platform shifts in the past. But time will clearly tell that this is much, much more profound than previous platform shifts. And the reason for that is because the computer is no longer an instruction-driven only computer. It's an intention-understanding computer. And it understands, of course, the way we interact with it, but it also understands our meaning, what we intend that we asked it to do and it has the ability to reason, inference iteratively to process a plan and come back with a solution. And so every aspect of the computer is changing in such a way that instead of retrieving prerecorded files, it is now generating contextually relevant intelligent answers. And so that's going to change computing stacks all over the world. And you saw a build that, in fact, even the PC computing stack is going to get revolutionized. And this is just the beginning of all the things that -- what people see today are the beginning of the things that we're working in our labs and the things that we're doing with all the startups and large companies and developers all over the world. It's going to be quite extraordinary. Operator: Our next question will come from the line of Joe Moore with Morgan Stanley. Please go ahead. Joseph Moore: Great. Thank you. I understand what you just said about how strong demand is. You have a lot of demand for H200 and for Blackwell products. Do you anticipate any kind of pause with Hopper and H100 as you sort of migrate to those products? Will people wait for those new products, which would be a good product to have? Or do you think there's enough demand for H100 to sustain growth? Jensen Huang: We see increasing demand of Hopper through this quarter. And we expect to be -- we expect demand to outstrip supply for some time as we now transition to H200, as we transition to Blackwell. Everybody is anxious to get their infrastructure online. And the reason for that is because they're saving money and making money, and they would like to do that as soon as possible. Operator: Our next question will come from the line of Toshiya Hari with Goldman Sachs. Please go ahead. Toshiya Hari: Hi. Thank you so much for taking the question. Jensen, I wanted to ask about competition. I think many of your cloud customers have announced new or updates to their existing internal programs, right, in parallel to what they're working on with you guys. To what extent did you consider them as competitors, medium to long term? And in your view, do you think they're limited to addressing most internal workloads or could they be broader in what they address going forward? Thank you. Jensen Huang: We're different in several ways. First, NVIDIA's accelerated computing architecture allows customers to process every aspect of their pipeline from unstructured data processing to prepare it for training, to structured data processing, data frame processing like SQL to prepare for training, to training to inference. And as I was mentioning in my remarks, that inference has really fundamentally changed, it's now generation. It's not trying to just detect the cat, which was plenty hard in itself, but it has to generate every pixel of a cat. And so the generation process is a fundamentally different processing architecture. And it's one of the reasons why TensorRT LLM was so well received. We improved the performance in using the same chips on our architecture by a factor of three. That kind of tells you something about the richness of our architecture and the richness of our software. So one, you could use NVIDIA for everything, from computer vision to image processing, the computer graphics to all modalities of computing. And as the world is now suffering from computing cost and computing energy inflation because general-purpose computing has run its course, accelerated computing is really the sustainable way of going forward. So accelerated computing is how you're going to save money in computing, is how you're going to save energy in computing. And so the versatility of our platform results in the lowest TCO for their data center. Second, we're in every cloud. And so for developers that are looking for a platform to develop on, starting with NVIDIA is always a great choice. And we're on-prem, we're in the cloud. We're in computers of any size and shape. We're practically everywhere. And so that's the second reason. The third reason has to do with the fact that we build AI factories. And this is becoming more an apparent to people that AI is not a chip problem only. It starts, of course, with very good chips and we build a whole bunch of chips for our AI factories, but it's a systems problem. In fact, even AI is now a systems problem. It's not just one large language model. It's a complex system of a whole bunch of large language models that are working together. And so the fact that NVIDIA builds this system causes us to optimize all of our chips to work together as a system, to be able to have software that operates as a system, and to be able to optimize across the system. And just to put it in perspective in simple numbers, if you had a $5 billion infrastructure and you improved the performance by a factor of two, which we routinely do, when you improve the infrastructure by a factor of two, the value too is $5 billion. All the chips in that data center doesn't pay for it. And so the value of it is really quite extraordinary. And this is the reason why today, performance matters everything. This is at a time when the highest performance is also the lowest cost because the infrastructure cost of carrying all of these chips cost a lot of money. And it takes a lot of money to fund the data center, to operate the data center, the people that goes along with it, the power that goes along with it, the real estate that goes along with it, and all of it adds up. And so the highest performance is also the lowest TCO. Operator: Our next question will come from the line of Matt Ramsay with TD Cowen. Please go ahead. Matthew Ramsay: Thank you very much. Good afternoon, everyone. Jensen, I've been in the data center industry my whole career. I've never seen the velocity that you guys are introducing new platforms at the same combination of the performance jumps that you're getting, I mean, 5x in training. Some of the stuff you talked about at GTC up to 30x in inference. And it's an amazing thing to watch but, it also creates an interesting juxtaposition where the current generation of product that your customers are spending billions of dollars on, it's going to be not as competitive with your new stuff, very, very much more quickly than the depreciation cycle of that product. So I'd like you to -- if you wouldn't mind speak a little bit about how you're seeing that situation evolve itself with customers. As you move to Blackwell, you're going to have very large installed bases, obviously software compatible, but large installed bases of product that's not nearly as performant as your new generation stuff. And it'd be interesting to hear what you see happening with customers along that path. Thank you. Jensen Huang: Yes. I really appreciate it. Three points that I'd like to make. If you're 5% into the build-out versus if you're 95% into the build out, you're going to feel very differently. And because you're only 5% into the build-out anyhow, you build as fast as you can. And when Blackwell comes, it's going to be terrific. And then after Blackwell, as you mentioned, we have other Blackwells coming. And then there's a short -- we're in a one-year rhythm as we've explained to the world. And we want our customers to see our road map for as far as they like, but they're early in their build-out anyways and so they had to just keep on building, okay. And so there's going to be a whole bunch of chips coming at them, and they just got to keep on building and just, if you will, performance average your way into it. So that's the smart thing to do. They need to make money today. They want to save money today. And time is really, really valuable to them. Let me give you an example of time being really valuable, why this idea of standing up a data center instantaneously is so valuable and getting this thing called time to train is so valuable. The reason for that is because the next company who reaches the next major plateau gets to announce a groundbreaking AI. And the second one after that gets to announce something that's 0.3% better. And so the question is, do you want to be repeatedly the company delivering groundbreaking AI or the company delivering 0.3% better? And that's the reason why this race, as in all technology races, the race is so important. And you're seeing this race across multiple companies because this is so vital to have technology leadership, for companies to trust the leadership and want to build on your platform and know that the platform that they're building on is going to get better and better. And so leadership matters a great deal. Time to train matters a great deal. The difference between time to train that is three months earlier just to get it done, in order to get time to train on three-months project, getting started three months earlier is everything. And so it's the reason why we're standing up Hopper systems like mad right now because the next plateau is just around the corner. And so that's the second reason. The first comment that you made is really a great comment, which is how is it that we're doing -- we're moving so fast and advancing them quickly? Because we have all the stacks here. We literally build the entire data center and we can monitor everything, measure everything, optimize across everything. We know where all the bottlenecks are. We're not guessing about it. We're not putting up PowerPoint slides that look good. We're actually -- we also like our PowerPoint slides look good, but we're delivering systems that perform at scale. And the reason why we know they perform at scale is because we built it all here. Now one of the things that we do that's a bit of a miracle is that we build entire AI infrastructure here, but then we disaggregated and integrated into our customers' data centers however they liked. But we know how it's going to perform and we know where the bottlenecks are. We know where we need to optimize with them and we know where we have to help them improve their infrastructure to achieve the most performance. This deep intimate knowledge at the entire data center scale is fundamentally what sets us apart today. We build every single chip from the ground up. We know exactly how processing is done across the entire system. And so we understand exactly how it's going to perform and how to get the most out of it with every single generation. So I appreciate. Those are the three points. Operator: Your next question will come from the line of Mark Lipacis with Evercore ISI. Please go ahead. Mark Lipacis: Hi. Thanks for taking my question. Jensen, in the past, you've made the observation that general-purpose computing ecosystems typically dominated each computing era. And I believe the argument was that they could adapt to different workloads, get higher utilization, drive cost of compute cycle down. And this is a motivation for why you were driving to a general-purpose GPU CUDA ecosystem for accelerated computing. And if I mischaracterized that observation, please do let me know. So the question is, given that the workloads that are driving demand for your solutions are being driven by neural network training and inferencing, which on the surface seem like a limited number of workloads, then it might also seem to lend themselves to custom solutions. And so then the question is about does the general purpose computing framework become more at risk or is there enough variability or a rapid enough evolution on these workloads that support that historical general purpose framework? Thank you. Jensen Huang: Yes. NVIDIA's accelerated computing is versatile, but I wouldn't call it general-purpose. Like for example, we wouldn't be very good at running the spreadsheet. That was really designed for general-purpose computing. And so there is a -- the control loop of an operating system code probably isn't fantastic for general-purpose compute, not for accelerated computing. And so I would say that we're versatile, and that's usually the way I describe it. There's a rich domain of applications that we're able to accelerate over the years, but they all have a lot of commonalities. Maybe some deep differences, but commonalities. They're all things that I can run in parallel, they're all heavily threaded. 5% of the code represents 99% of the run-time, for example. Those are all properties of accelerated computing. The versatility of our platform and the fact that we design entire systems is the reason why over the course of the last 10 years or so, the number of start-ups that you guys have asked me about in these conference calls is fairly large. And every single one of them, because of the brittleness of their architecture, the moment generative AI came along or the moment the fusion models came along, the moment the next models are coming along now. And now all of a sudden, look at this, large language models with memory because the large language model needs to have memory so they can carry on a conversation with you, understand the context. All of a sudden, the versatility of the Grace memory became super important. And so each one of these advances in generative AI and the advancement of AI really begs for not having a widget that's designed for one model. But to have something that is really good for this entire domain, properties of this entire domain, but obeys the first principles of software, that software is going to continue to evolve, that software is going to keep getting better and bigger. We believe in the scaling of these models. There's a lot of reasons why we're going to scale by easily a million times in the coming few years for good reasons, and we're looking forward to it and we're ready for it. And so the versatility of our platform is really quite key. And it's not -- if you're too brittle and too specific, you might as well just build an FPGA or you build an ASIC or something like that, but that's hardly a computer. Operator: Our next question will come from the line of Blayne Curtis with Jefferies. Please go ahead. Blayne Curtis: Thanks for taking my question. Actually kind of curious, I mean, being supply constrained, how do you think about , I mean, you came out with a product for China, H20. I'm assuming there'd be a ton of demand for it, but obviously, you're trying to serve your customers with the other Hopper products. Just kind of curious how you're thinking about that in the second half. You could elaborate any impact, what you're thinking for sales as well as gross margin. Jensen Huang: I didn't hear your questions. Something bleeped out. Simona Jankowski: H20 and how you're thinking about allocating supply between the different Hopper products. Jensen Huang: Well, we have customers that we honor and we do our best for every customer. It is the case that our business in China is substantially lower than the levels of the past. And it's a lot more competitive in China now because of the limitations on our technology. And so those matters are true. However, we continue to do our best to serve the customers in the markets there and to the best of our ability, we'll do our best. But I think overall, the comments that we made about demand outstripping supply is for the entire market and particularly so for H200 and Blackwell towards the end of the year. Operator: Our next question will come from the line of Srini Pajjuri with Raymond James. Please go ahead. Srini Pajjuri: Thank you. Jensen, actually more of a clarification on what you said. GB 200 systems, it looks like there is a significant demand for systems. Historically, I think you've sold a lot of HGX boards and some GPUs and the systems business was relatively small. So I'm just curious, why is it that now you are seeing such a strong demand for systems going forward? Is it just the TCO or is it something else or is it just the architecture? Thank you. Jensen Huang: Yes. I appreciate that. In fact, the way we sell GB200 is the same. We disaggregate all of the components that make sense and we integrate it into computer makers. We have 100 different computer system configurations that are coming this year for Blackwell. And that is off the charts. Hopper, frankly, had only half, but that's at its peak. It started out with way less than that even. And so you're going to see liquid cooled version, air cooled version, x86 visions, Grace versions, so on and so forth. There's a whole bunch of systems that are being designed. And they're offered from all of our ecosystem of great partners. Nothing has really changed. Now of course, the Blackwell platform has expanded our offering tremendously. The integration of CPUs and the much more compressed density of computing, liquid cooling is going to save data centers a lot of money in provisioning power and not to mention to be more energy efficient. And so it's a much better solution. It's more expansive, meaning that we offer a lot more components of a data center and everybody wins. The data center gets much higher performance, networking from networking switches, networking. Of course, NICs, we have Ethernet now so that we can bring NVIDIA AI to a large-scale NVIDIA AI to customers who only operate only know how to operate Ethernet because of the ecosystem that they have. And so Blackwell is much more expansive. We have a lot more to offer our customers this generation around. Operator: Our next question will come from the line William Stein with Truist Securities. Please go ahead. William Stein: Great. Thanks for taking my question. Jensen, at some point, NVIDIA decided that while there are reasonably good CPUs available for data center operations, your ARM-based Grace CPU provides some real advantage that made that technology worth delivering to customers, perhaps related to cost or power consumption or technical synergies between Grace and Hopper, Grace and Blackwell. Can you address whether there could be a similar dynamic that might emerge on the client side, whereby while there are very good solutions, you've highlighted that Intel and AMD are very good partners and deliver great products in x86, but there might be some, especially in emerging AI workloads, some advantage that NVIDIA can deliver that others have more of a challenge? Jensen Huang: Well, you mentioned some really good reasons. It is true that for many of the applications, our partnership with x86 partners are really terrific and we build excellent systems together. But Grace allows us to do something that isn't possible with the configuration, the system configuration today. The memory system between Grace and Hopper are coherent and connected. The interconnect between the two chips, calling it two chips is almost weird because it's like a superchip. The two of them are connected with this interface that's like a terabytes per second. It's off the charts. And the memory that's used by Grace is LPDDR. It's the first data center-grade low-power memory. And so we save a lot of power on every single node. And then finally, because of the architecture, because we can create our own architecture with the entire system now, we could create something that has a really large NVLink domain, which is vitally important to the next-generation large language models for inferencing. And so you saw that GB200 has a 72-node NVLink domain. That's like 72 Blackwells connected together into one giant GPU. And so we needed Grace Blackwells to be able to do that. And so there are architectural reasons, there are software programming reasons and then there are system reasons that are essential for us to build them that way. And so if we see opportunities like that, we'll explore it. And today, as you saw at the build yesterday, which I thought was really excellent, Satya announced the next-generation PCs, Copilot+ PC, which runs fantastically on NVIDIA's RTX GPUs that are shipping in laptops. But it also supports ARM beautifully. And so it opens up opportunities for system innovation even for PCs. Operator: Our last question comes from the line of C.J. Muse with Cantor Fitzgerald. Please go ahead. C.J. Muse: Good afternoon. Thank you for taking the question. I guess, Jensen, a bit of a longer-term question. I know Blackwell hasn't even launched yet, but obviously, investors are forward-looking and amidst rising potential competition from GPUs and custom ASICs, how are you thinking about NVIDIA's pace of innovation and your million-fold scaling over the last decade, truly impressive. CUDA, Varsity, Precision, Grace, Cohere and Connectivity. When you look forward, what frictions need to be solved in the coming decade? And I guess, maybe more importantly, what are you willing to share with us today? Jensen Huang: Well, I can announce that after Blackwell, there's another chip. And we are on a one-year rhythm. And so and you can also count that -- count on us having new networking technology on a very fast rhythm. We're announcing Spectrum-X for Ethernet. But we're all in on Ethernet, and we have a really exciting road map coming for Ethernet. We have a rich ecosystem of partners. Dell announced that they're taking Spectrum-X to market. We have a rich ecosystem of customers and partners who are going to announce taking our entire AI factory architecture to market. And so for companies that want the ultimate performance, we have InfiniBand computing fabric. InfiniBand is a computing fabric, Ethernet is a network. And InfiniBand, over the years, started out as a computing fabric, became a better and better network. Ethernet is a network and with Spectrum-X, we're going to make it a much better computing fabric. And we're committed -- fully committed to all three links, NVLink computing fabric for single computing domain to InfiniBand computing fabric, to Ethernet networking computing fabric. And so we're going to take all three of them forward at a very fast clip. And so you're going to see new switches coming, new NICs coming, new capability, new software stacks that run on all three of them. New CPUs, new GPUs, new networking NICs, new switches, a mound of chips that are coming. And all of it, the beautiful thing is all of it runs CUDA. And all of it runs our entire software stack. So you invest today on our software stack, without doing anything at all, it's just going to get faster and faster and faster and faster. And if you invest in our architecture today, without doing anything, it will go to more and more clouds and more and more data centers and everything just runs. And so I think the pace of innovation that we're bringing will drive up the capability, on the one hand, and drive down the TCO on the other hand. And so we should be able to scale out with the NVIDIA architecture for this new era of computing and start this new industrial revolution where we manufacture not just software anymore, but we manufacture artificial intelligence tokens and we're going to do that at scale. Thank you. Operator: That will conclude our question-and-answer session and our call for today. We thank you all for joining and you may now disconnect.

Operator: Good afternoon. My name is Rob and I'll be your conference operator today. At this time, I would like to welcome everyone to the NVIDIA's Fourth Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question-and-answer session. [Operator Instructions] Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the fourth quarter and fiscal 2024. With me today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the first quarter of fiscal 2025. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, February 21, 2024, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that let me turn the call over to Colette. Colette Kress: Thanks, Simona. Q4 was another record quarter. Revenue of $22.1 billion was up 22% sequentially and up to 265% year-on-year and well above our outlook of $20 billion. For fiscal 2024, revenue was $60.9 billion and up 126% from the prior year. Starting with data center. Data center revenue for the fiscal 2024 year was $47.5 billion, more than tripling from the prior year. The world has reached the tipping point of new computing era. The $1 trillion installed base of data center infrastructure is rapidly transitioning from general purpose to accelerated computing. As Moore's Law slows while computing demand continues to skyrocket, companies may accelerate every workload possible to drive future improvement in performance, TCO and energy efficiency. At the same time, companies have started to build the next generation of modern data centers, what we refer to as AI factories, purpose built to refine raw data and produce valuable intelligence in the era of generative AI. In the fourth quarter, data center revenue of $18.4 billion was a record, up 27% sequentially and up 409% year-over-year, driven by the NVIDIA Hopper GPU computing platform along with InfiniBand end-to-end networking. Compute revenue grew more than 5x and networking revenue tripled from last year. We are delighted that supply of Hopper architecture products is improving. Demand for Hopper remains very strong. We expect our next-generation products to be supply constrained as demand far exceeds supply. Fourth quarter data center growth was driven by both training and inference of generative AI and large language models across a broad set of industries, use cases and regions. The versatility and leading performance of our data center platform enables a high return on investment for many use cases, including AI training and inference, data processing and a broad range of CUDA accelerated workloads. We estimate in the past year approximately 40% of data center revenue was for AI inference. Building and deploying AI solutions has reached virtually every industry. Many companies across industries are training and operating their AI models and services at scale, enterprises across NVIDIA AI infrastructure through cloud providers, including hyperscales, GPU specialized and private clouds or on-premise. NVIDIA's computing stack extends seamlessly across cloud and on-premise environments, allowing customers to deploy with a multi-cloud or hybrid-cloud strategy. In the fourth quarter, large cloud providers represented more than half of our data center revenue, supporting both internal workloads and external public cloud customers. Microsoft recently noted that more than 50,000 organizations use GitHub Copilot business to supercharge the productivity of their developers, contributing to GitHub revenue growth accelerating to 40% year-over-year. And Copilot for Microsoft 365 adoption grew faster in its first two months than the two previous major Microsoft 365 enterprise suite releases did. Consumer internet companies have been early adopters of AI and represent one of our largest customer categories. Companies from search to e-commerce, social media, news and video services and entertainment are using AI for deep learning-based recommendation systems. These AI investments are generating a strong return by improving customer engagement, ad conversation and click-throughs rates. Meta in its latest quarter cited more accurate predictions and improved advertiser performance as contributing to the significant acceleration in its revenue. In addition, consumer internet companies are investing in generative AI to support content creators, advertisers and customers through automation tools for content and ad creation, online product descriptions and AI shopping assistance. Enterprise software companies are applying generative AI to help customers realize productivity gains. Early customers we've partnered with for both training and inference of generative AI are already seeing notable commercial success. ServiceNow's generative AI products in their latest quarter drove their largest ever net new annual contract value contribution of any new product family release. We are working with many other leading AI and enterprise software platforms as well, including Adobe, Databricks, Getty Images, SAP and Snowflake. The field of foundation of large-language models is thriving. Anthropic, Google, Inflection, Microsoft, OpenAI and xAI are leading with continued amazing breakthrough in generative AI. Exciting companies like Adept, AI21, Character.ai, Cohere, Mistral, Perplexity and Runway are building platforms to serve enterprises and creators. New startups are creating LLMs to serve the specific languages, cultures and customs of the world many regions. And others are creating foundation models to address entirely different industries like Recursion Pharmaceuticals and Generate:Biomedicines for biology. These companies are driving demand for NVIDIA AI infrastructure through hyperscale or GPU specialized cloud providers. Just this morning, we announced that we've collaborated with Google to optimize its state-of-the art new Gemma language models to accelerate their inference performance on NVIDIA GPUs in the cloud data center and PC. One of the most notable trends over the past year is the significant adoption of AI by enterprises across the industry verticals such as automotive, healthcare and financial services. NVIDIA offers multiple application frameworks to help companies adopt AI in vertical domains such as autonomous driving, drug discovery, low latency machine learning for fraud detection or robotics, leveraging our full stack accelerated computing platform. We estimate the data center revenue contribution of the automotive vertical through the cloud or on-prem exceeded $1 billion last year. NVIDIA DRIVE infrastructure solutions includes systems and software for the development of autonomous driving, including data ingestion, creation, labeling and AI training, plus validation through simulation. Almost 80 vehicle manufacturers across global OEMs, new energy vehicles, trucking, robotaxi and Tier 1 suppliers are using NVIDIA's AI infrastructure to train LLMs and other AI models for automated driving and AI cockpit applications. And in fact, nearly every automotive company working on AI is working with NVIDIA. As AV algorithms move to video transformers and more cars are equipped with cameras, we expect NVIDIA's automotive data center processing demand to grow significantly. In healthcare, digital biology and generative AI are helping to reinvent drug discovery, surgery, medical imaging and wearable devices. We have built deep domain expertise in healthcare over the past decade, creating the NVIDIA Clara healthcare platform and NVIDIA BioNeMo, a generative AI service to develop, customize and deploy AI foundation models for computer-aided drug discovery. BioNeMo features a growing collection of pre-trained Biomolecular AI models that can be applied to the end-to-end drug discovery processes. We announced Recursion is making available for their proprietary AI model through BioNeMo for the drug discovery ecosystem. In financial services, customers are using AI for a growing set of use cases from trading and risk management to customer service and fraud detection. For example, American Express improved fraud detection accuracy by 6% using NVIDIA AI. Shifting to our data center revenue by geography. Growth was strong across all regions, except for China where our data center revenue declined significantly following the U.S. government export control regulations imposed in October. Although we have not received licenses from the U.S. government to ship restricted products to China, we have started shipping alternatives that don't require a license for the China market. China represented a mid-single digit percentage of our data center revenue in Q4. And we expect it to stay in a similar range in the first-quarter. In regions outside of the U.S. and China, sovereign AI has become an additional demand driver. Countries around the world are investing in AI infrastructure to support the building of large-language models in their own language, on domestic data and in support of their local research and enterprise ecosystems. From a product perspective, the vast majority of revenue was driven by our Hopper architecture along with InfiniBand networking. Together, they have emerged as the de-facto standard for accelerated computing and AI infrastructure. We are on track to ramp H200 with initial shipments in the second quarter. Demand is strong as H200 nearly doubles the inference performance of H100. Networking exceeded a $13 billion annualized revenue run rate. Our end-to-end networking solutions define modern AI data centers. Our Quantum InfiniBand solutions grew more than 5x year on year. NVIDIA Quantum InfiniBand is the standard for the highest performance AI-dedicated infrastructures. We are now entering the ethernet networking space with the launch of our new Spectrum-X end-to-end offering designed for an AI-optimized networking for the data center. Spectrum-X introduces new technologies over ethernet, that are purpose built for AI. Technologies incorporated in our Spectrum switch, BlueField DPU and software stack deliver 1.6x higher networking performance for AI processing compared with traditional ethernet. Leading OEMs, including Dell, HPE, Lenovo and Super Micro, with their global sales channels, are partnering with us to expand our AI solution to enterprises worldwide. We are on track to ship Spectrum-X this quarter. We also made great progress with our software and services offerings, which reached an annualized revenue run rate of $1 billion in Q4. We announced that NVIDIA DGX Cloud will expand its list of partners to include Amazon's AWS, joining Microsoft Azure, Google Cloud and Oracle Cloud. DGX Cloud is used for NVIDIA's own AI R&D and custom model development as well as NVIDIA developers. It brings the CUDA ecosystem to NVIDIA CSP partners. Okay, moving to gaming. Gaming revenue was $2.87 billion, was flat sequentially and up 56% year on year, better than our outlook on solid consumer demand for NVIDIA GeForce RTX GPUs during the holidays. Fiscal year revenue of $10.45 billion was up 15%. At CES, we announced our GeForce RTX 40 Super Series family of GPUs. Starting at $599, they deliver incredible gaming performance and generative AI capabilities. Sales are off to a great start. NVIDIA AI Tensor cores and the GPUs deliver up to 836 AI tops, perfect for powering AI for gaming, creating an everyday productivity. The rich software stack we offer with our RTX GPUs further accelerates AI. With our DLSS technologies, seven out of eight pixels can be AI generated, resulting up to 4x faster ray tracing and better image quality. And with the Tensor RT LLM for Windows, our open-source library that accelerates inference performance for the latest large-language models generative AI can run up to 5X faster on RTX AI PCs. At CES, we also announced a wave of new RTX 40 Series AI laptops from every major OEMs. These bring high-performance gaming and AI capabilities to a wide range of form factors, including 14 inch and thin and light laptops. With up to 686 tops of AI performance, these next-generation AI PCs increase generative AI performance by up to 60x, making them the best-performing AI PC platforms. At CES, we announced NVIDIA Avatar Cloud Engine microservices, which allowed developers to integrate state-of-the-art generative AI models into digital avatars. ACE won several Best of CES 2024 awards. NVIDIA has an end-to-end platform for building and deploying generative AI applications for RTX PCs and workstations. This includes libraries, SDKs, tools and services developers can incorporate into their generative AI workloads. NVIDIA is fueling the next wave of generative AI applications coming to the PC. With over 100 million RTX PCs in the installed-base and over 500 AI-enabled PC applications and games, we are on our way. Moving to Pro Visualization. Revenue of $463 million was up 11% sequentially and up 105% year on year. Fiscal year revenue of $1.55 billion was up 1%. Sequential growth in the quarter was driven by a rich mix of RTX Ada architecture GPUs continuing to ramp. Enterprises are refreshing their workstations to support generative AI-related workloads, such as data preparation, LLM fine-tuning and retrieval augmented generation. These key industrial verticals driving demand include manufacturing, automotive and robotics. The automotive industry has also been an early adopter of NVIDIA Omniverse as it seeks to digitize work flows from design to build, simulate, operate and experience their factories and cars. At CES, we announced that creative partners and developers including Brickland, WPP and ZeroLight are building Omniverse-powered car configurators. Leading automakers like LOTUS are adopting the technology to bring new levels of personalization, realism and interactivity to the car buying experience. Moving to Automotive. Revenue was $281 million, up 8% sequentially and down 4% year on year. Fiscal year revenue of $1.09 billion was up 21%, crossing the $1 billion mark for the first time on continued adoption of the NVIDIA DRIVE platform by automakers. NVIDIA DRIVE Orin is the AI car computer of choice for software-defined AV fleets. Its successor, NVIDIA DRIVE Thor, designed for vision transformers often -- offers more AI performance and integrates a wide range of intelligent capabilities into a single AI compute platform, including autonomous driving and parking, driver and passenger monitoring and AI cockpit functionality and will be available next year. There were several automotive customer announcements this quarter, Li Auto, Great Wall Motor, ZEEKR, the premium EV subsidiary of Geely and Jeremy Xiaomi EV all announced new vehicles built on NVIDIA. Moving to the rest of the P&L. GAAP gross margins expanded sequentially to 76% and non-GAAP gross margins to 76.7% on strong data center growth and mix. Our gross margins in Q4 benefited from favorable component costs. Sequentially, GAAP operating expenses were up 6% and non-GAAP operating expenses were up 9%, primarily reflecting higher compute and infrastructure investments and employee growth. In Q4, we returned $2.8 billion to shareholders in the form of share repurchases and cash dividends. During fiscal year '24, we utilized cash of $9.9 billion towards shareholder returns, including $9.5 billion in share repurchases. Let me turn to the outlook for the first quarter. Total revenue is expected to be $24 billion, plus or minus 2%. We expect sequential growth in data center and proviz, partially offset by seasonal decline in gaming. GAAP and non-GAAP gross margins are expected to be 76.3% and 77% respectively, plus or minus 50 basis-points. Similar to Q4, Q1 gross margins are benefiting from favorable component costs. Beyond Q1, for the remainder of the year, we expect gross margins to return to the mid-70s percent range. GAAP and non-GAAP operating expenses are expected to be approximately $3.5 billion and $2.5 billion respectively. Fiscal year 2025 GAAP and non-GAAP operating expenses are expected to grow in the mid-30% range as we continue to invest in the large opportunities ahead of us. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $250 million, excluding gains and losses from non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 17%, plus or minus 1% excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight some upcoming events for the financial community. We will attend the Morgan Stanley Technology and Media and Telecom Conference in San Francisco on March 4 and the TD Cowen's 44th Annual Healthcare Conference in Boston on March 5. And of course, please join us for our Annual DTC conference starting Monday March 18 in San Jose, California, to be held in-person for the first time in five years. DTC will kick off with Jen-Hsun's keynote and we will host a Q&A session for financial analysts the next day, March 19. At this time, we will now open the call for questions. Operator, would you please poll for questions? Operator: [Operator Instructions] Your first question comes from the line of Toshiya Hari from Goldman Sachs. Your line is open. Toshiya Hari: Hi. Thank you so much for taking the question and congratulations on the really strong results. My question is for Jen-Hsun on the data center business. Clearly, you're doing extremely well in the business. I'm curious how your expectations for calendar '24 and '25 have evolved over the past 90 days. And as you answer the question, I was hoping you can touch on some of the newer buckets within data center, things like software. Sovereign AI, I think you've been pretty vocal about how to think about that medium-to-long term. And recently, there was an article about NVIDIA potentially participating in the ASIC market. Is there any credence to that, and if so, how should we think about you guys playing in that market over the next several years? Thank you. Jensen Huang: Thanks, Toshiya. Let's see. There were three questions, one more time. First question was -- can you -- well? Toshiya Hari: I guess your expectations for data center, how they've evolved. Thank you. Jensen Huang: Okay. Yeah. Well, we guide one quarter at a time. But fundamentally, the conditions are excellent for continued growth calendar '24, to calendar '25 and beyond. And let me tell you why? We're at the beginning of two industry-wide transitions and both of them are industry wide. The first one is a transition from general to accelerated computing. General-purpose computing, as you know, is starting to run out of steam. And you can tell by the CSPs extending and many data centers, including our own for general-purpose computing, extending the depreciation from four to six years. There's just no reason to update with more CPUs when you can't fundamentally and dramatically enhance its throughput like you used to. And so you have to accelerate everything. This is what NVIDIA has been pioneering for some time. And with accelerated computing, you can dramatically improve your energy efficiency. You can dramatically improve your cost in data processing by 20 to 1. Huge numbers. And of course, the speed. That speed is so incredible that we enabled a second industry-wide transition called generative AI. Generative AI, I'm sure we're going to talk plenty -- plenty about it during the call. But remember, generative AI is a new application. It is enabling a new way of doing software, new types of software are being created. It is a new way of computing. You can't do generative AI on traditional general-purpose computing. You have to accelerate it. And the third is it is enabling a whole new industry, and this is something worthwhile to take a step back and look at and it connects to your last question about sovereign AI. A whole new industry in the sense that for the very first time a data center is not just about computing data and storing data and serving the employees of a company. We now have a new type of data center that is about AI generation, an AI generation factory. And you've heard me describe it as AI factories. But basically, it takes raw material, which is data, it transforms it with these AI supercomputers that NVIDIA builds, and it turns them into incredibly valuable tokens. These tokens are what people experience on the amazing ChatGPT or Midjourney or, search these days are augmented by that. All of your recommender systems are now augmented by that, the hyper-personalization that goes along with it. All of these incredible startups in digital biology, generating proteins and generating chemicals and the list goes on. And so all of these tokens are generated in a very specialized type of data center. And this data center we call AI supercomputers and AI generation factories. But we're seeing diversity -- one of the other reasons -- so at the foundation is that. The way it manifests into new markets is in all of the diversity that you're seeing us in. One, the amount of inference that we do is just off the charts now. Almost every single time you interact with ChatGPT, that we're inferencing. Every time you use Midjourney, we're inferencing. Every time you see amazing -- these Sora videos that are being generated or Runway, the videos that they're editing, Firefly, NVIDIA is doing inferencing. The inference part of our business has grown tremendously. We estimate about 40%. The amount of training is continuing, because these models are getting larger and larger, the amount of inference is increasing. But we're also diversifying into new industries. The large CSPs are still continuing to build out. You can see from their CapEx and their discussions, but there's a whole new category called GPU specialized CSPs. They specialize in NVIDIA AI infrastructure, GPU specialized CSPs. You're seeing enterprise software platforms deploying AI. ServiceNow is just a really, really great example. You see Adobe. There's the others, SAP and others. You see consumer Internet services that are now augmenting all of their services of the past with generative AI. So they can have even more hyper-personalized content to be created. You see us talking about industrial generative AI. Now our industries represent multi-billion dollar businesses, auto, health, financial services. In total, our vertical industries are multi-billion dollar businesses now. And of course sovereign AI. The reason for sovereign AI has to do with the fact that the language, the knowledge, the history, the culture of each region are different and they own their own data. They would like to use their data, train it with to create their own digital intelligence and provision it to harness that raw material themselves. It belongs to them, each one of the regions around the world. The data belongs to them. The data is most useful to their society. And so they want to protect the data. They want to transform it themselves, value-added transformation, into AI and provision those services themselves. So we're seeing sovereign AI infrastructure is being built in Japan, in Canada, in France, so many other regions. And so my expectation is that what is being experienced here in the United States, in the West, will surely be replicated around the world, and these AI generation factories are going to be in every industry, every company, every region. And so I think the last -- this last year, we've seen a generative AI really becoming a whole new application space, a whole new way of doing computing, a whole new industry is being formed and that's driving our growth. Operator: Your next question comes from the line of Joe Moore from Morgan Stanley. Your line is open. Joe Moore: Great. Thank you. I wanted to follow up on the 40% of revenues coming from inference. That's a bigger number than I expected. Can you give us some sense of where that number was maybe a year before, how much you're seeing growth around LLMs from inference? And how are you measuring that? Is that -- I assume it's in some cases the same GPUs you use for training and inference. How solid is that measurement? Thank you. Jensen Huang: I'll go backwards. The estimate is probably understated. And -- but we estimated it. And let me tell you why. Whenever -- a year ago, the recommender systems that people are -- when you run the internet, the news, the videos, the music, the products that are being recommended to you because as you know, the internet has trillions -- I don't know how many trillions, but trillions of things out there and your phone is 3-inches square. And so the ability for them to fit all of that information down to something, such a small real estate, is through a system, an amazing system called recommender systems. These recommender systems used to be all based on CPU approaches. But the recent migration to deep learning and now generative AI has really put these recommender systems now directly into the path of GPU acceleration. It needs GPU acceleration for the embeddings. It needs GPU acceleration for the nearest neighbor search. It needs GPU acceleration for the re-ranking and it needs GPU acceleration to generate the augmented information for you. So GPUs are in every single step of a recommender system now. And as you know, recommender system is the single largest software engine on the planet. Almost every major company in the world has to run these large recommender systems. Whenever you use ChatGPT, it's being inferenced. Whenever you hear about Midjourney and just the number of things that they're generating for consumers, when you when you see Getty, the work that we do with Getty and Firefly from Adobe. These are all generative models. The list goes on. And none of these, as I mentioned, existed a year ago, 100% new. Operator: Your next question comes from the line of Stacy Rasgon from Bernstein Research. Your line is open. Stacy Rasgon: Hi, guys. Thanks for taking my question. I wanted Colette -- I wanted to touch on your comment that you expected the next generation of products -- I assume that meant Blackwell, to be supply constrained. Could you dig into that a little bit, what is the driver of that? Why does that get constrained as Hopper is easing up? And how long do you expect that to be constrained, like do you expect the next generation to be constrained like all the way through calendar '25, like when do those start to ease? Jensen Huang: Yeah. The first thing is overall, our supply is improving, overall. Our supply chain is just doing an incredible job for us, everything from of course the wafers, the packaging, the memories, all of the power regulators, to transceivers and networking and cables and you name it. The list of components that we ship -- as you know, people think that NVIDIA GPUs is like a chip. But the NVIDIA Hopper GPU has 35,000 parts. It weighs 70 pounds. These things are really complicated things we've built. People call it an AI supercomputer for good reason. If you ever look in the back of the data center, the systems, the cabling system is mind boggling. It is the most dense complex cabling system for networking the world's ever seen. Our InfiniBand business grew 5x year over year. The supply chain is really doing fantastic supporting us. And so overall, the supply is improving. We expect the demand will continue to be stronger than our supply provides and -- through the year and we'll do our best. The cycle times are improving and we're going to continue to do our best. However, whenever we have new products, as you know, it ramps from zero to a very large number. And you can't do that overnight. Everything is ramped up. It doesn't step up. And so whenever we have a new generation of products -- and right now, we are ramping H200's. There is no way we can reasonably keep up on demand in the short term as we ramp. We're ramping Spectrum-X. We're doing incredibly well with Spectrum-X. It's our brand-new product into the world of ethernet. InfiniBand is the standard for AI-dedicated systems. Ethernet with Spectrum-X --ethernet is just not a very good scale-out system. But with Spectrum-X, we've augmented, layered on top of ethernet, fundamental new capabilities like adaptive routing, congestion control, noise isolation or traffic isolation, so that we could optimize ethernet for AI. And so InfiniBand will be our AI-dedicated infrastructure. Spectrum-X will be our AI-optimized networking and that is ramping, and so we'll -- with all of the new products, demand is greater than supply. And that's just kind of the nature of new products and so we work as fast as we can to capture the demand. But overall, overall net-net, overall, our supply is increasing very nicely. Operator: Your next question comes from the line of Matt Ramsay from TD Cowen. Your line is open. Matt Ramsay: Good afternoon, Jensen, Colette. Congrats on the results. I wanted to ask I guess a two-part question, and it comes at what Stacy was just getting out on your demand being significantly more than your supply, even though supply is improving. And I guess the two sides of the question are, I guess, first for Colette, like how are you guys thinking about allocation of product in terms of customer readiness to deploy and sort of monitoring if there's any kind of build-up of product that might not yet be turned on? And then I guess Jen-Hsun, for you, I'd be really interested to hear you speak a bit about the thought that you and your company are putting into the allocation of your product across customers, many of which compete with each other, across industries to smaller startup companies, to things in the healthcare arena to government. It's a very, very unique technology that you're enabling and I'd be really interested to hear you speak a bit about how you think about quote/unquote fairly allocating sort of for the good of your company, but also for the good of the industry. Thanks. Colette Kress: Let me first start with your question, thanks, about how we are working with our customers as they look into how they are building out their GPU instances and our allocation process. The folks that we work with, our customers that we work with, have been partners with us for many years as we have been assisting them both in what they set up in the cloud, as well as what they are setting up internally. Many of these providers have multiple products going at one time to serve so many different needs across their end customers but also what they need internally. So they are working in advance, of course, thinking about those new clusters that they will need. And our discussions with them continue not only on our Hopper architecture, but helping them understand the next wave and getting their interest and getting their outlook for the demand that they want. So it's always a moving process in terms of what they will purchase, what is still being built and what is in use for our end customers. But the relationships that we've built and their understanding of the sophistication of the build has really helped us with that allocation and both helped us with our communications with them. Jensen Huang: First, our CSPs have a very clear view of our product road map and transitions. And that transparency with our CSPs gives them the confidence of which products to place and where and when. And so they know their -- they know the timing to the best of our ability. And they know quantities and of course allocation. We allocate fairly. We allocate fairly. We do the best of our -- do the best we can to allocate fairly and to avoid allocating unnecessarily. As you mentioned earlier, why allocate something when the data center's not ready. Nothing is more difficult then to have anything sit around. And so, allocate fairly, and to avoid allocating unnecessarily. And where we do -- the question that you asked about the end markets, that we have an excellent ecosystem with OEMs, ODMs, CSPs and, very importantly, end markets. What NVIDIA is really unique about is that we bring our customers, we bring our partners, CSPs and OEMs, we bring them customers. The biology companies, the healthcare companies, financial services companies, AI developers, large-language model developers, autonomous vehicle companies, robotics companies. There's just a giant suite of robotics companies that are emerging. There are warehouse robotics to surgical robotics to humanoid robotics, all kinds of really interesting robotics companies, agriculture robotics companies. All of these startups, large companies, healthcare, financial services and auto and such are working on NVIDIA's platform. We support them directly. And oftentimes, we can have a twofer by allocating to a CSP and bringing the customer to the CSP at the same time. And so this ecosystem, you're absolutely right that it's vibrant. But at the core of it, we want to allocate fairly with avoiding waste and looking for opportunities to connect partners and end users. We're looking for those opportunities all the time. Operator: Your next question comes from the line of Timothy Arcuri from UBS. Your line is open. Timothy Arcuri: Thanks a lot. I wanted to ask about how you're converting backlog into revenue. Obviously, lead times for your products have come down quite a bit. Colette, you didn't talk about the inventory purchase commitments. But if I sort of add up your inventory plus the purchase commits and your prepaid supply, sort of the aggregate of your supply, it was actually down a touch. How should we read that? Is that just you saying that you don't need to make as much of a financial commitment to your suppliers because the lead times are lower or is that maybe you're reaching some sort of steady state where you're closer to filling your order book and your backlog? Thanks. Colette Kress: Yeah. So let me, highlight on those three different areas of how we look at our suppliers. You're correct. Our inventory on hand given our allocation that we're on, we're trying to, as things come into inventory, immediately work to ship them to our customers. I think our customer appreciates our ability to meet the schedules that we've looked for. The second piece of it is our purchase commitments. Our purchase commitments have many different components into it, components that we need for manufacturing. But also, often we are procuring capacity that we may need. The length of that need for capacity or the length for the components are all different. Some of them may be for the next two quarters, but some of them may be for multiple years. I can say the same regarding our prepaids. Our prepaids are pre-designed to make sure that we have the reserve capacity that we need at several of our manufacturing suppliers as we look forward. So wouldn't read into anything regarding approximately about the same numbers as we are increasing our supply. All of them just have different lengths as we have sometimes had to buy things in long-lead times or things that needed capacity to be built for us. Operator: Your next question comes from the line of Ben Reitzes from Melius Research. Your line is open. Ben Reitzes: Yeah. Thanks. Congratulations on the results. Colette, I wanted to talk about your comment regarding gross margins and that they should go back to the mid-70s. If you don't mind unpacking that. And also, is that due to the HBM content in the new products and what do you think are the drivers of that comment? Thanks so much. Colette Kress: Yeah. Thanks for the question. We highlighted in our opening remarks really about our Q4 results and our outlook for Q1. Both of those quarters are unique. Those two quarters are unique in their gross margin as they include some benefit from favorable component cost in the supply chain kind of across both our compute and networking and also in several different stages of our manufacturing process. So looking forward, we have visibility into a mid-70s gross margin for the rest of the fiscal year, taking us back to where we were before this Q4 and Q1 peak that we've had here. So we're really looking at just a balance of our mix. Mix is always going to be our largest driver of what we will be shipping for the rest of the year. And those are really just the drivers. Operator: Your next question comes from the line of C.J. Muse from Cantor Fitzgerald. Your line is open. C.J. Muse: Yeah. Good afternoon, and thank you for taking the question. Bigger picture question for you, Jen-Hsun. When you think about the million-x improvement in GPU compute over the last decade and expectations for similar improvements in the next, how do your customers think about the long-term usability of their NVIDIA investments that they're making today? Do today's training clusters become tomorrow's inference clusters? How do you see this playing out? Thank you. Jensen Huang: Hey, CJ. Thanks for the question. Yeah, that's the really cool part. If you look at the reason why we're able to improve performance so much, it's because we have two characteristics about our platform. One, is that it's accelerated. And two, it's programmable. It's not brittle. NVIDIA is the only architecture that has gone from the very, very beginning, literally the very beginning when CNN's and Alex Krizhevsky and Ilya Sutskever and Geoff Hinton first revealed AlexNet, all the way through to RNNs to LSTMs to every -- RLs to deep learning RLs to transformers to every single version. Every single version and every species that have come along, vision transformers, multi-modality transformers, every single -- and now time sequence stuff, and every single variation, every single species of AI that has come along, we've been able to support it, optimize our stack for it and deploy it into our installed base. This is really the great amazing part. On the one hand, we can invent new architectures and new technologies like our Tensor cores, like our transformer engine for Tensor cores, improved new numerical formats and structures of processing like we've done with the different generations of Tensor cores, meanwhile, supporting the installed base at the same time. And so, as a result, we take all of our new software algorithm invest -- inventions, all of the inventions, new inventions of models of the industry, and it runs on our installed base on the one hand. On the other hand, whenever we see something revolutionary we can -- like transformers, we can create something brand new like the Hopper transformer engine and implement it into future. And so we simultaneously have this ability to bring software to the installed base and keep making it better and better and better, so our customers installed base is enriched over time with our new software. On the other hand, for new technologies, create revolutionary capabilities. Don't be surprised if in our future generation, all of a sudden amazing breakthroughs in large-language models were made possible And those breakthroughs, some of which will be in software because they run CUDA, will be made available to the installed base. And so we carry everybody with us on the one hand. We make giant breakthroughs on the other hand. Operator: Your next question comes from the line of Aaron Rakers from Wells Fargo. Your line is open. Aaron Rakers: Yeah. Thanks for taking the question. I wanted to ask about the China business. I know that in your prepared comments you said that you started shipping some alternative solutions into China. You also put it out that you expect that contribution to continue to be about a mid-single digit percent of your total data center business. So I guess the question is what is the extent of products that you're shipping today into the China market and why should we not expect that maybe other alternative solutions come to the market and expand your breadth to participate in that in that opportunity again? Thank you. Jensen Huang: Think of, at the core, remember the US government wants to limit the latest capabilities of NVIDIA's accelerated computing and AI to the Chinese market. And the U.S. government would like to see us be as successful in China as possible. Within those two constraints, within those two pillars if you will, are the restrictions, and so we had to pause when the new restrictions came out. We immediately paused. So that we understood what the restrictions are, reconfigured our products in a way that is not software hackable in any way. And that took some time. And so we reset -- we reset our product offering to China and now we're sampling to customers in China. And we're going to do our best to compete in that marketplace and succeed in that marketplace within the -- within the specifications of the restriction. And so that's it. We -- this last quarter, we -- our business significantly declined as we -- as we paused in the marketplace. We stopped shipping in the marketplace. We expect this quarter to be about the same. But after, that hopefully we can go compete for our business and do our best, and we'll see how it turns out. Operator: Your next question comes from the line of Harsh Kumar from Piper Sandler. Your line is open. Harsh Kumar: Yeah. Hey, Jen-Hsun, Colette and NVIDIA team. First of all, congratulations on a stunning quarter and guide. I wanted to talk about, a little bit about your software business and it's pleasing to hear that it's over a $1 billion but I was hoping Jen-Hsun or Colette if you could just help us understand what the different parts and pieces are for the software business? In other words, just help us unpack it a little bit, so we can get a better understanding of where that growth is coming from. Jensen Huang: Let me take a step back and explain the fundamental reason why NVIDIA will be very successful in software. So first, as you know, accelerated computing really grew in the cloud. In the cloud, the cloud service providers have really large engineering teams and we work with them in a way that allows them to operate and manage their own business. And whenever there are any issues, we have large teams assigned to them. And their engineering teams are working directly with our engineering teams and we enhance, we fix, we maintain, we patch the complicated stack of software that's involved in accelerated computing. As you know, accelerated computing is very different than general-purpose computing. You're not starting from a program like C++. You compile it and things run on all your CPUs. The stacks of software necessary for every domain from data processing SQL versus -- SQL structure data versus all the images and text and PDF, which is unstructured, to classical machine-learning to computer vision to speech to large-language models, all --recommender systems. All of these things require different software stacks. That's the reason why NVIDIA has hundreds of libraries. If you don't have software, you can't open new markets. If you don't have software, you can't open and enable new applications. Software is fundamentally necessary for accelerated computing. This is the fundamental difference between accelerated computing and general-purpose computing that most people took a long time to understand. And now, people understand that the software is really key. And the way that we work with CSPs, that's really easy. We have large teams that are working with their large teams. However, now that generative AI is enabling every enterprise and every enterprise software company to embrace accelerated computing -- and when -- it is now essential to embrace accelerated computing because it is no longer possible, no longer likely anyhow to sustain improved throughput through just general-purpose computing. All of these enterprise software companies and enterprise companies don't have large engineering teams to be able to maintain and optimize their software stack to run across all of the world's clouds and private clouds and on-prem. So we are going to do the management, the optimization, the patching, the tuning, the installed-base optimization for all of their software stacks. And we containerize them into our stack. We call it NVIDIA AI Enterprise. And the way we go to market with it is that think of that NVIDIA AI Enterprise now as a run time like an operating system, it's an operating system for artificial intelligence. And we charge $4,500 per GPU per year. And my guess is that every enterprise in the world, every software enterprise company that are deploying software in all the clouds and private clouds and on-prem, will run on NVIDIA AI Enterprise, especially obviously for our GPUs. And so this is going to likely be a very significant business over time. We're off to a great start. And Colette mentioned that it's already at $1 billion run rate and we're really just getting started. Operator: Thank you. I will now turn the call back over to Jen-Hsun Huang, CEO, for closing remarks. Jensen Huang: The computer industry is making two simultaneous platform shifts at the same time. The trillion-dollar installed base of data centers is transitioning from general purpose to accelerated computing. Every data center will be accelerated so the world can keep up with the computing demand, with increasing throughput, while managing costs and energy. The incredible speed up of NVIDIA enabled -- that NVIDIA enabled, a whole new computing paradigm, generative AI, where software can learn, understand and generate any information from human language to the structure of biology and the 3D world. We are now at the beginning of a new industry where AI-dedicated data centers process massive raw data to refine it into digital intelligence. Like AC power generation plants of the last industrial revolution, NVIDIA AI supercomputers are essentially AI generation factories of this Industrial Revolution. Every company in every industry is fundamentally built on their proprietary business intelligence, and in the future, their proprietary generative AI. Generative AI has kicked off a whole new investment cycle to build the next trillion dollars of infrastructure of AI generation factories. We believe these two trends will drive a doubling of the world's data center infrastructure installed base in the next five years and will represent an annual market opportunity in the hundreds of billions. This new AI infrastructure will open up a whole new world of applications not possible today. We started the AI journey with the hyperscale cloud providers and consumer internet companies. And now, every industry is on board, from automotive to healthcare to financial services, to industrial to telecom, media and entertainment. NVIDIA's full stack computing platform with industry-specific applications frameworks and a huge developer and partner ecosystem, gives us the speed, scale and reach to help every company -- to help companies in every industry become an AI company. We have so much to share with you at next month's GTC in San Jose. So be sure to join us. We look forward to updating you on our progress next quarter. Operator: This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is JL, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's Third Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speakers’ remarks, there will be a question-and-answer session. [Operator Instructions] Simona Jankowski, you may now begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the third quarter of fiscal 2024. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the fourth quarter and fiscal 2024. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All statements are made as of today, November 21, 2023, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. Q3 was another record quarter. Revenue of $18.1 billion was up 34% sequentially and up more than 200% year-on-year and well above our outlook for $16 billion. Starting with Data Center. The continued ramp of the NVIDIA HGX platform based on our Hopper Tensor Core GPU architecture, along with InfiniBand end-to-end networking, drove record revenue of $14.5 billion, up 41% sequentially and up 279% year-on-year. NVIDIA HGX with InfiniBand together are essentially the reference architecture for AI supercomputers and data center infrastructures. Some of the most exciting generative AI applications are built and run on NVIDIA, including Adobe Firefly, ChatGPT, Microsoft 365 Copilot, CoAssist, now assist with ServiceNow and Zoom AI Companion. Our Data Center compute revenue quadrupled from last year and networking revenue nearly tripled. Investments in infrastructure for training and inferencing large language models, deep learning, recommender systems and generative AI applications is fueling strong broad-based demand for NVIDIA accelerated computing. Inferencing is now a major workload for NVIDIA AI computing. Consumer Internet companies and enterprises drove exceptional sequential growth in Q3, comprising approximately half of our Data Center revenue and outpacing total growth. Companies like Meta are in full production with deep learning, recommender systems and also investing in generative AI to help advertisers optimize images and text. Most major consumer Internet companies are racing to ramp up generative AI deployment. The enterprise wave of AI adoption is now beginning. Enterprise software companies such as Adobe, Databricks, Snowflake and ServiceNow are adding AI copilots and the systems to their platforms. And broader enterprises are developing custom AI for vertical industry applications such as Tesla in autonomous driving. Cloud service providers drove roughly the other half of our Data Center revenue in the quarter. Demand was strong from all hyperscale CSPs, as well as from a broadening set of GPU-specialized CSPs globally that are rapidly growing to address the new market opportunities in AI. NVIDIA H100 Tensor Core GPU instances are now generally available in virtually every cloud with instances in high demand. We have significantly increased supply every quarter this year to meet strong demand and expect to continue to do so next year. We will also have a broader and faster product launch cadence to meet the growing and diverse set of AI opportunities. Towards the end of the quarter, the U.S. government announced a new set of export control regulations for China and other markets, including Vietnam and certain countries in the Middle East. These regulations require licenses for the export of a number of our products, including our Hopper and Ampere 100 and 800 series and several others. Our sales to China and other affected destinations derived from products that are now subject to licensing requirements have consistently contributed approximately 20% to 25% of Data Center revenue over the past few quarters. We expect that our sales to these destinations will decline significantly in the fourth quarter. So we believe will be more than offset by strong growth in other regions. The U.S. government designed the regulation to allow the U.S. industry to provide data center compute products to markets worldwide, including China. Continuing to compete worldwide as the regulations encourage, promotes U.S. technology leadership, spurs economic growth and supports U.S. jobs. For the highest performance levels, the government requires licenses. For lower performance levels, the government requires a streamlined prior notification process. And for products even lower performance levels, the government does not require any notice at all. Following the government's clear guidelines, we are working to expand our Data Center product portfolio to offer compliance solutions for each regulatory category, including products for which the U.S. government does not wish to have advance notice before each shipment. We are working with some customers in China and the Middle East to pursue licenses from the U.S. government. It is too early to know whether these will be granted for any significant amount of revenue. Many countries are awakening to the need to invest in sovereign AI infrastructure to support economic growth and industrial innovation. With investments in domestic compute capacity, nations can use their own data to train LLMs and support their local generative AI ecosystems. For example, we are working with India's government and largest tech companies including Infosys, Reliance and Tata to boost their sovereign AI infrastructure. And French private cloud provider, Scaleway, is building a regional AI cloud based on NVIDIA H100 InfiniBand and NVIDIA's AI Enterprise software to fuel advancement across France and Europe. National investment in compute capacity is a new economic imperative and serving the sovereign AI infrastructure market represents a multi-billion dollar opportunity over the next few years. From a product perspective, the vast majority of revenue in Q3 was driven by the NVIDIA HGX platform based on our Hopper GPU architecture with lower contribution from the prior generation Ampere GPU architecture. The new L40S GPU built for industry standard servers began to ship, supporting training and inference workloads across a variety of consumers. This was also the first revenue quarter of our GH200 Grace Hopper Superchip, which combines our ARM-based Grace CPU with a Hopper GPU. Grace and Grace Hopper are ramping into a new multi-billion dollar product line. Grace Hopper instances are now available at GPU specialized cloud providers, and coming soon to Oracle Cloud. Grace Hopper is also getting significant traction with supercomputing customers. Initial shipments to Los Alamos National Lab and the Swiss National Supercomputing Center took place in the third quarter. The UK government announced it will build one of the world's fastest AI supercomputers called Isambard-AI with almost 5,500 Grace Hopper Superchips. German supercomputing center, Julich, also announced that it will build its next-generation AI supercomputer with close to 24,000 Grace Hopper Superchips and Quantum-2 InfiniBand, making it the world's most powerful AI supercomputer with over 90 exaflops of AI performance. All-in, we estimate that the combined AI compute capacity of all the supercomputers built on Grace Hopper across the U.S., Europe and Japan next year will exceed 200 exaflops with more wins to come. Inference is contributing significantly to our data center demand, as AI is now in full production for deep learning, recommenders, chatbots, copilots and text to image generation and this is just the beginning. NVIDIA AI offers the best inference performance and versatility, and thus the lower power and cost of ownership. We are also driving a fast cost reduction curve. With the release of TensorRT-LLM, we now achieved more than 2x the inference performance for half the cost of inferencing LLMs on NVIDIA GPUs. We also announced the latest member of the Hopper family, the H200, which will be the first GPU to offer HBM3e, faster, larger memory to further accelerate generative AI and LLMs. It moves inference speed up to another 2x compared to H100 GPUs for running LLMs like Norma2 (ph). Combined, TensorRT-LLM and H200, increased performance or reduced cost by 4x in just one year. With our customers changing their stack, this is a benefit of CUDA and our architecture compatibility. Compared to the A100, H200 delivers an 18x performance increase for inferencing models like GPT-3, allowing customers to move to larger models and with no increase in latency. Amazon Web Services, Google Cloud, Microsoft Azure and Oracle Cloud will be among the first CSPs to offer H200-based instances starting next year. At last week's Microsoft Ignite, we deepened and expanded our collaboration with Microsoft across the entire stock. We introduced an AI foundry service for the development and tuning of custom generative AI enterprise applications running on Azure. Customers can bring their domain knowledge and proprietary data and we help them build their AI models using our AI expertise and software stock in our DGX cloud, all with enterprise grade security and support. SAP and Amdocs are the first customers of the NVIDIA AI foundry service on Microsoft Azure. In addition, Microsoft will launch new confidential computing instances based on the H100. The H100 remains the top performing and most versatile platform for AI training and by a wide margin, as shown in the latest MLPerf industry benchmark results. Our training cluster included more than 10,000 H100 GPUs or 3x more than in June, reflecting very efficient scaling. Efficient scaling is a key requirement in generative AI, because LLMs are growing by an order of magnitude every year. Microsoft Azure achieved similar results on a nearly identical cluster, demonstrating the efficiency of NVIDIA AI in public cloud deployments. Networking now exceeds a $10 billion annualized revenue run rate. Strong growth was driven by exceptional demand for InfiniBand, which grew fivefold year-on-year. InfiniBand is critical to gaining the scale and performance needed for training LLMs. Microsoft made this very point last week, highlighting that Azure uses over 29,000 miles of InfiniBand cabling, enough to circle the globe. We are expanding NVIDIA networking into the Ethernet space. Our new Spectrum-X end-to-end Ethernet offering with technologies, purpose built for AI, will be available in Q1 next year. With support from leading OEMs, including Dell, HPE and Lenovo. Spectrum-X can achieve 1.6x higher networking performance for AI communication compared to traditional Ethernet offerings. Let me also provide an update on our software and services offerings, where we are starting to see excellent adoption. We are on track to exit the year at an annualized revenue run rate of $1 billion for our recurring software, support and services offerings. We see two primary opportunities for growth over the intermediate term with our DGX cloud service and with our NVIDIA AI Enterprise software, each reflects the growth of enterprise AI training and enterprise AI inference, respectively. Our latest DGX cloud customer announcement was this morning as part of an AI research collaboration with Gentech, the biotechnology pioneer also plans to use our BioNeMo LLM framework to help accelerate and optimize their AI drug discovery platform. We now have enterprise AI partnership with Adobe, Dropbox, Getty, SAP, ServiceNow, Snowflake and others to come. Okay. Moving to Gaming. Gaming revenue of $2.86 billion was up 15% sequentially and up more than 80% year-on-year with strong demand in the important back-to-school shopping season with NVIDIA RTX ray tracing and AI technology now available at price points as low as $299. We entered the holidays with the best-ever line-up for gamers and creators. Gaming has doubled relative to pre-COVID levels even against the backdrop of lackluster PC market performance. This reflects the significant value we've brought to the gaming ecosystem with innovations like RTX and DLSS. The number of games and applications supporting these technologies has exploded in that period, driving upgrades and attracting new buyers. The RTX ecosystem continues to grow. There are now over 475 RTX-enabled games and applications. Generative AI is quickly emerging as the new pillar app for high performance PCs. NVIDIA RTX GPUs to find the most performance AI PCs and workstations. We just released TensorRT-LLM for Windows, which speeds on-device LLM inference up by 4x. With an installed base of over 100 million, NVIDIA RTX is the natural platform for AI application developers. Finally, our GeForce NOW cloud gaming service continues to build momentum. Its library of PC games surpassed 1,700 titles, including the launches of Alan Wake 2, Baldur's Gate 3, Cyberpunk 2077: Phantom Liberty and Starfield. Moving to the Pro Vis. Revenue of $416 million was up 10% sequentially and up 108% year-on year. NVIDIA RTX is the workstation platform of choice for professional design, engineering and simulation use cases and AI is emerging as a powerful demand driver. Early applications include inference for AI imaging in healthcare and edge AI in smart spaces and the public sector. We launched a new line of desktop workstations based on NVIDIA RTX Ada Lovelace generation GPUs and ConnectX, SmartNICs offering up to 2x the AI processing ray tracing and graphics performance of the previous generations. These powerful new workstations are optimized for AI workloads such as fine tune AI models, training smaller models and running inference locally. We continue to make progress on Omniverse, our software platform for designing, building and operating 3D virtual worlds. Mercedes-Benz is using Omniverse powered digital twins to plan, design, build and operate its manufacturing and assembly facilities, helping it increase efficiency and reduce defects. Oxxon (ph) is also incorporating Omniverse into its manufacturing process, including end-to-end simulation for the entire robotics and automation pipeline, saving time and cost. We announced two new Omniverse Cloud services for automotive digitalization available on Microsoft Azure, a virtual factory simulation engine and autonomous vehicle simulation engine. Moving to Automotive. Revenue was $261 million, up 3% sequentially and up 4% year-on year, primarily driven by continued growth in self-driving platforms based on NVIDIA DRIVE Orin SOC and the ramp of AI cockpit solutions with global OEM customers. We extended our automotive partnership of Foxconn to include NVIDIA DRIVE for our next-generation automotive SOC. Foxconn has become the ODM for EVs. Our partnership provides Foxconn with a standard AV sensor and computing platform for their customers to easily build a state-of-an-art safe and secure software defined car. Now we're going to move to the rest of the P&L. GAAP gross margin expanded to 74% and non-GAAP gross margin to 75%, driven by higher Data Center sales and lower net inventory reserve, including a 1 percentage point benefit from the release of previously reserved inventory related to the Ampere GPU architecture products. Sequentially, GAAP operating expenses were up 12% and non-GAAP operating expenses were up 10%, primarily reflecting increased compensation and benefits. Let me turn to the fourth quarter of fiscal 2024. Total revenue is expected to be $20 billion, plus or minus 2%. We expect strong sequential growth to be driven by Data Center, with continued strong demand for both compute and networking. Gaming will likely decline sequentially as it is now more aligned with notebook seasonality. GAAP and non-GAAP gross margins are expected to be 74.5% and 75.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $3.17 billion and $2.2 billion, respectively. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $200 million, excluding gains and losses from non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 15%, plus or minus 1% excluding any discrete items. Further financial information are included in the CFO commentary and other information available on our IR website. In closing, let me highlight some upcoming events for the financial community. We will attend the UBS Global Technology Conference in Scottsdale, Arizona, on November 28; the Wells Fargo TMT Summit in Rancho Palos Verdes, California on November 29; the Arete Virtual Tech Conference on December 7; and the J.P. Morgan Health Care Conference in San Francisco on January 8. Our earnings call to discuss the results of our fourth quarter and fiscal 2024 is scheduled for Wednesday, February 21. We will now open the call for questions. Operator, will you please poll for questions. Operator: [Operator Instructions] Your first question comes from the line of Vivek Arya of Bank of America. Your line is open. Vivek Arya: Thanks for taking my question. Just, Colette, wanted to clarify what China contributions are you expecting in Q4. And then, Jensen, the main question is for you, where do you think we are in the adoption curve in terms of your shipments into the generative AI market? Because when I just look at the trajectory of your data center, is growth -- it will be close to nearly 30% of all the spending in data center next year. So what metrics are you keeping an eye on to inform you that you can continue to grow? Just where are we in the adoption curve of your products into the generative AI market? Thank you. Colette Kress: So, first let me start with your question, Vivek, on export controls and the impacts that we are seeing in our Q4 outlook and guidance that we provided. We had seen historically over the last several quarters that China and some of the other impacted destinations to be about 20% to 25% of our Data Center revenue. We are expecting in our guidance for that to decrease substantially as we move into Q4. The export controls will have a negative effect on our China business. And we do not have good visibility into the magnitude of that impact even over the long-term. We are though working to expand our Data Center product portfolio to possibly offer new regulation compliance solutions that do not require a license, these products, they may become available in the next coming months. However, we don't expect their contribution to be material or meaningful as a percentage of the revenue in Q4. Jensen Huang: Generative AI is the largest TAM expansion of software and hardware that we've seen in several decades. At the core of it, what's really exciting is that, what was largely a retrieval based computing approach, almost everything that you do is retrieved off of storage somewhere, has been augmented now, added with a generative method. And it's changed almost everything. You could see that text-to-text, text-to-image, text-to-video, text-to-3D, text-to-protein, text-to-chemicals, these were things that were processed and typed in by humans in the past. And these are now generative approaches. The way that we access data is changed. It used to be based on explicit queries. It is now based on natural language queries, intention queries, semantic queries. And so, we're excited about the work that we're doing with SAP and Dropbox and many others that you're going to hear about. And one of the areas that is really impactful is the software industry, which is about $1 trillion or so, has been building tools that are manually used over the last couple of decades. And now there's a whole new segment of software called copilots and assistants. Instead of manually used, these tools will have copilots to help you use it. And so, instead of licensing software, we will continue to do that, of course, but we will also hire copilots and assistants to help us use these -- use the software. We'll connect all of these copilots and assistants into teams of AIs, which is going to be the modern version of software, modern version of enterprise business software. And so the transformation of software and the way that software has done is driving the hardware underneath. And you can see that it's transforming hardware in two ways. One is something that's largely independent of generative AI. There's two trends: one is related to accelerated computing, general purpose computing is too wasteful of energy and cost. And now that we have much, much better approaches, call it, accelerated computing, you could save an order of magnitude of energy, you can save an order of magnitude of time or you can save an order of magnitudes of cost by using acceleration. And so, accelerated computing is transitioning, if you will, general purpose computing into this new approach. And that's been augmented by a new class of data centers. This is the traditional data centers that you were just talking about where we represent about a third of that. But there is a new class of data centers and this new class of data centers, unlike the data centers of the past, where you have a lot of applications running used by a great many people that are different tenants that are using the same infrastructure and that data center stores a lot of files. These new data centers are very few applications, if not one application, used by basically one tenant and it processes data, it trains models and then generates tokens and generates AI. And we call these new data centers AI factories. We're seeing AI factories being built out everywhere, and just by every country. And so if you look at the way where we are in the expansion, the transition into this new computing approach, the first wave you saw with large language model start-ups, generative AI start-ups and consumer Internet companies, and weren't in the process of ramping that. Meanwhile, while that's being ramped, you see that we're starting to partner with enterprise software companies who would like to build chatbots and copilots and assistants to augment the tools that they have on their platforms. You're seeing GPU specialized CSPs cropping up all over the world and they are dedicated to do really one thing, which is processing AI. You're seeing sovereign AI infrastructures, people -- countries that now recognize that they have to utilize their own data, keep their own data, keep their own culture, process that data and develop their own AI. You see that in India. Several -- about a year ago in Sweden, you are seeing in Japan. Last week, a big announcement in France. But the number of sovereign AI clouds that are being built is really quite significant. And my guess is that almost every major region will have and surely every major country will have their own AI clouds. And so I think you're seeing just new developments as the generative AI wave propagates through every industry, every company, every region. And so we're at the beginning of this inflection, this computing transition. Operator: Your next question comes from the line of Aaron Rakers of Wells Fargo. Your line is open. Aaron Rakers: Yeah. Thanks for taking the question. I wanted to ask about kind of the networking side of the business. Given the growth rates that you've now cited, I think, it's 155% year-over-year and strong growth sequentially, it looks like that business is like almost approaching $2.5 billion to $3 billion quarterly level. I'm curious of how you see Ethernet involved evolving and maybe how you would characterize your differentiation of Spectrum-X relative to the traditional Ethernet stack as we start to think about that becoming part of the networking narrative above and maybe beyond just InfiniBand as we look into next year? Thank you. Jensen Huang: Yeah. Thanks for the question. Our networking business is already on a $10 billion plus run rate and it's going to get much larger. And as you mentioned, we added a new networking platform to our networking business recently. The vast majority of the dedicated large scale AI factories standardize on InfiniBand. And the reason for that is not only because of its data rate and not only just the latency, but the way that it moves traffic around the network is really important. The way that you process AI and a multi-tenant hyperscale Ethernet environment, the traffic pattern is just radically different. And with InfiniBand and with software defined networks, we could do congestion control, adaptive routing, performance isolation and noise isolation, not to mention, of course, the day rate and the low latency that -- and a very low overhead of InfiniBand that's natural part of InfiniBand. And so, InfiniBand is not so much just the network, it's also a computing fabric. We've put a lot of software-defined capabilities into the fabric including computation. We will do 40-point calculations and computation right on the switch, and right in the fabric itself. And so that's the reason why that difference in Ethernet versus InfiniBand or InfiniBand versus Ethernet for AI factories is so dramatic. And the difference is profound. And the reason for that is because you've just invested in a $2 billion infrastructure for AI factories. A 20%, 25%, 30% difference in overall effectiveness, especially as you scale up is measured in hundreds of millions of dollars of value. And if you will, renting that infrastructure over the course of four to five years, it really, really adds up. And so InfiniBand's value proposition is undeniable for AI factories. However, as we move AI into enterprise. This is enterprise computing what we'd like to enable every company to be able to build their own custom AIs. We're building customer AIs in our company based on our proprietary data, our proprietary type of skills. For example, recently we spoke about one of the models that we're creating, it's called ChipNeMo; we're building many others. There'll be tens, hundreds of custom AI models that we create inside our company. And our company is -- for all of our employee use, doesn't have to be as high performance as the AI factories we used to train the models. And so we would like the AI to be able to run in Ethernet environment. And so what we've done is we invented this new platform that extends Ethernet; doesn't replace Ethernet, it's 100% compliant with Ethernet. And it's optimized for East-West traffic, which is where the computing fabric is. It adds to Ethernet with an end-to-end solution with Bluefield, as well as our Spectrum switch that allows us to perform some of the capabilities that we have in InfiniBand, not all but some. And we achieved excellent results. And the way we go to market is we go to market with our large enterprise partners who already offer our computing solution. And so, HP, Dell and Lenovo has the NVIDIA AI stack, the NVIDIA AI Enterprise software stack and now they integrate with Bluefield, as well as bundle -- take a market there, Spectrum switch, and they'll be able to offer enterprise customers all over the world with their vast sales force and vast network of resellers a fully integrated, if you will, fully optimized, at least end-to-end AI solution. And so that's basically it, bringing AI to Ethernet for the world's enterprise. Operator: Thank you. Your next question comes from the line of Joe Moore of Morgan Stanley. Your line is open. Joseph Moore: Great. Thank you. I'm wondering if you could talk a little bit more about Grace Hopper and how you see the ability to leverage kind of the microprocessor, how you see that as a TAM expander. And what applications do you see using Grace Hopper versus more traditional H100 applications? Jensen Huang: Yeah. Thanks for the question. Grace Hopper is in production -- in high volume production now. We're expecting next year just with all of the design wins that we have in high performance computing and AI infrastructures, we are on a very, very fast ramp with our first data center CPU to a multi-billion dollar product line. This is going to be a very large product line for us. The capability of Grace Hopper is really quite spectacular. It has the ability to create computing nodes that simultaneously has very fast memory, as well as very large memory. In the areas of vector databases or semantic surge, what is called RAG, retrieval augmented generation. So that you could have a generative AI model be able to refer to proprietary data or a factual data before it generates a response, that data is quite large. And you can also have applications or generative models where the context length is very high. You basically store it in entire book into end-to-end system memory before you ask your questions. And so the context length can be quite large this way. The generative models has the ability to still be able to naturally interact with you on one hand. On the other hand, be able to refer to factual data, proprietary data or domain-specific data, you data and be contextually relevant and reduce hallucination. And so that particular use case for example is really quite fantastic for Grace Hopper. It also serves the customers that really care to have a different CPU than x86. Maybe it's a European supercomputing centers or European companies who would like to build up their own ARM ecosystem and like to build up a full stack or CSPs that have decided that they would like to pivot to ARM, because their own custom CPUs are based on ARM. There are variety of different reasons that drives the success of Grace Hopper, but we're off to a just an extraordinary start. This is a home run product. Operator: Your next question comes from the line of Tim Arcuri of UBS. Your line is open. Tim Arcuri: Hi. Thanks. I wanted to ask a little bit about the visibility that you have on revenue. I know there's a few moving parts. I guess, on one hand, the purchase commitments went up a lot again. But on the other hand, China bans would arguably pull in when you can fill the demand beyond China. So I know we're not even into 2024 yet and it doesn't sound like, Jensen, you think that next year would be a peak in your Data Center revenue, but I just wanted to sort of explicitly ask you that. Do you think that Data Center can grow even in 2025? Thanks. Jensen Huang: Absolutely believe the Data Center can grow through 2025. And there are, of course, several reasons for that. We are expanding our supply quite significantly. We have already one of the broadest and largest and most capable supply chain in the world. Now, remember, people think that the GPU is a chip. But the HGX H100, the Hopper HGX has 35,000 parts, it weighs 70 pounds. Eight of the chips are Hopper. The other 35,000 are not. It is -- even its passive components are incredible. High voltage parts. High frequency parts. High current parts. It is a supercomputer, and therefore, the only way to test a supercomputer is with another supercomputer. Even the manufacturing of it is complicated, the testing of it is complicated, the shipping of it complicated and installation is complicated. And so, every aspect of our HGX supply chain is complicated. And the remarkable team that we have here has really scaled out the supply chain incredibly. Not to mention, all of our HGXs are connected with NVIDIA networking. And the networking, the transceivers, the mix, the cables, the switches, the amount of complexity there is just incredible. And so, I'm just -- first of all, I'm just super proud of the team for scaling up this incredible supply chain. We are absolutely world class. But meanwhile, we're adding new customers and new products. So we have new supply. We have new customers, as I was mentioning earlier. Different regions are standing up GPU specialist clouds, sovereign AI clouds coming out from all over the world, as people realize that they can't afford to export their country's knowledge, their country's culture for somebody else to then resell AI back to them, they have to -- they should, they have the skills and surely with us in combination, we can help them to do that build up their national AI. And so, the first thing that they have to do is, create their AI cloud, national AI cloud. You're also seeing us now growing into enterprise. The enterprise market has two paths. One path -- or if I could say three paths. The first path, of course, just off-the-shelf AI. And there are of course Chat GPT, a fabulous off-the-shelf AI, there'll be others. There's also a proprietary AI, because software companies like ServiceNow and SAP, there are many, many others that can't afford to have their company's intelligence be outsourced to somebody else. And they are about building tools and on top of their tools they should build custom and proprietary and domain-specific copilots and assistants that they can then rent to their customer base. This is -- they're sitting on a goldmine, almost every major tools company in the world is sitting on a goldmine, and they recognize that they have to go build their own custom AIs. We have a new service called an AI foundry, where we leverage NVS (ph) capabilities to be able to serve them in that. And then the next one is enterprises building their own custom AIs, their own custom chatbots, their own custom RAGs. And this capability is spreading all over the world. And the way that we're going to serve that marketplace is with the entire stacks of systems, which includes our compute, our networking and our switches, running our software stack called NVIDIA AI Enterprise, taking it through our market partners, HP, Dell, Lenovo, so on and so forth. And so we're just -- we're seeing the waves of generative AI starting from the start-ups and CSPs, moving to consumer Internet companies, moving to enterprise software platforms, moving to enterprise companies. And then ultimately, one of the areas that you guys have seen us spend a lot of energy on has to do with industrial generative AI. This is where NVIDIA AI and NVIDIA Omniverse comes together and that is a really, really exciting work. And so I think the -- we're at the beginning of a basically across-the-board industrial transition to generative AI to accelerated computing. This is going to affect every company, every industry, every country. Operator: Your next question comes from the line of Toshiya Hari of Goldman Sachs. Your line is open. Toshiya Hari: Hi. Thank you. I wanted to clarify something with Colette real quick, and then I had a question for Jensen as well. Colette, you mentioned that you'll be introducing regulation-compliant products over the next couple of months. Yet, the contribution to Q4 revenue should be relatively limited. Is that a timing issue and could it be a source of reacceleration in growth for Data Center in April and beyond or are the price points such that the contribution to revenue going forward should be relatively limited? And then the question for Jensen, the AI foundry service announcement from last week. I just wanted to ask about that, and hopefully, have you expand on it. How is the monetization model going to work? Is it primarily services and software revenue? How should we think about the long term opportunity set? And is this going to be exclusive to Microsoft or do you have plans to expand to other partners as well? Thank you. Colette Kress: Thanks, Toshiya. On the question regarding potentially new products that we could provide to our China customers. It's a significant process to both design and develop these new products. As we discussed, we're going to make sure that we are in full discussions with the U.S. government of our intent to move products as well. Given our state about where we are in the quarter, we're already several weeks into the quarter. So it's just going to take some time for us to go through and discussing with our customers the needs and desires of these new products that we have. And moving forward, whether that's medium-term or long-term, it's just hard to say both the [Technical Difficulty] of what we can produce with the U.S. government and what the interest of our China customers in this. So we stay still focused on finding that right balance for our China customers, but it's hard to say at this time. Jensen Huang: Toshiya, thanks for the question. There is a glaring opportunity in the world for AI foundry, and it makes so much sense. First, every company has its core intelligence. It makes up our company. Our data, our domain expertise, in the case of many companies, we create tools, and most of the software companies in the world are tool platforms, and those tools are used by people today. And in the future, it's going to be used by people augmented with a whole bunch of AIs that we hire. And these platforms just got to go across the world and you'll see and we've only announced a few; SAP, ServiceNow, Dropbox, Getty, many others are coming. And the reason for that is because they have their own proprietary AI. They want their own proprietary AI. They can't afford to outsource their intelligence and handout their data, and handout their flywheel for other companies to build the AI for them. And so, they come to us. We have several things that are really essential in a foundry. Just as TSMC as a foundry, you have to have AI technology. And as you know, we have just an incredible depth of AI capability -- AI technology capability. And then second, you have to have the best practice known practice, the skills of processing data through the invention of AI models to create AIs that are guardrails, fine-tuned, so on and so forth, that are safe, so on and so forth. And the third thing is you need factories. And that's what DGX Cloud is. Our AI models are called AI Foundations. Our process, if you will, our CAD system for creating AIs are called NeMo and they run on NVIDIA's factories we call DGX Cloud. Our monetization model is that with each one of our partners they rent a sandbox on DGX Cloud, where we work together, they bring their data, they bring their domain expertise, we bring our researchers and engineers, we help them build their custom AI. We help them make that custom AI incredible. Then that custom AI becomes theirs. And they deploy it on the runtime that is enterprise grade, enterprise optimized or outperformance optimized, runs across everything NVIDIA. We have a giant installed base in the cloud, on-prem, anywhere. And it's secure, securely patched, constantly patched and optimized and supported. And we call that NVIDIA AI Enterprise. NVIDIA AI Enterprise is $4,500 per GP per year, that's our business model. Our business model is basically a license. Our customers then with that basic license can build their monetization model on top of. In a lot of ways we're wholesale, they become retail. They could have a per -- they could have subscription license base, they could per instance or they could do per usage, there is a lot of different ways that they could take a -- create their own business model, but ours is basically like a software license, like an operating system. And so our business model is help you create your custom models, you run those custom models on NVIDIA AI Enterprise. And it's off to a great start. NVIDIA AI Enterprise is going to be a very large business for us. Operator: Your next question comes from the line of Stacy Rasgon of Bernstein Research. Your line is open. Stacy Rasgon: Hi, guys. Thanks for taking my questions. Colette, I wanted to know if it weren't for the China restrictions would the Q4 guide has been higher or are you supply-constrained in just reshipping stuff that would have gone to China elsewhere? And I guess along those lines you give us a feeling for where your lead times are right now in data center and just the China redirection such as-is, is it lowering those lead times, because you've got parts that are sort of immediately available to ship? Colette Kress: Yeah. Stacy, let me see if I can help you understand. Yes, there are still situations where we are working on both improving our supply each and every quarter. We've done a really solid job of ramping every quarter, which has defined our revenue. But with the absence of China for our outlook for Q4, sure, there could have been some things that we are not supply-constrained that we could have sold, but kind of we would no longer can. So could our guidance had been a little higher in our Q4? Yes. We are still working on improving our supply on plan, on continuing growing all throughout next year as well towards that. Operator: Your next question comes from the line of Matt Ramsay of TD Cowen. Your line is open. Matt Ramsay: Thank you very much. Congrats, everybody, on the results. Jensen, I had a two-part question for you, and it comes off of sort of one premise. And the premise is, I still get a lot of questions from investors thinking about AI training as being NVIDIA's dominant domain and somehow inference, even large model inference takes more and more of the TAM that the market will become more competitive. You'll be less differentiated et cetera., et cetera. So I guess the two parts of the question are: number one, maybe you could spend a little bit of time talking about the evolution of the inference workload as we move to LLMs and how your company is positioned for that rather than smaller model inference. And second, up until a month or two ago, I never really got any questions at all about the data processing piece of the AI workloads. So the pieces of manipulating the data before training, between training and inference, after inference and I think that's a large part of the workload now. Maybe you could talk about how CUDA is enabling acceleration of those pieces of the workload. Thanks. Jensen Huang: Sure. Inference is complicated. It's actually incredibly complicated. If you -- we this quarter announced one of the most exciting new engines, optimizing compilers called TensorRT-LLM. The reception has been incredible. You got to GitHub, it's been downloaded a ton, a whole lot of stars, integrated into stacks and frameworks all over the world, almost instantaneously. And there are several reasons for that, obviously. We could create TensorRT-LLM, because CUDA is programmable. If CUDA and our GPUs were not so programmable, it would really be hard for us to improve software stacks at the pace that we do. TensorRT-LLM, on the same GPU, without anybody touching anything, improves the performance by a factor of two. And then on top of that, of course, the pace of our innovation is so high. H200 increases it by another factor of two. And so, our inference performance, another way of saying inference cost, just reduced by a factor of four within about a year's time. And so, that's really, really hard to keep up with. The reason why everybody likes our inference engine is because our installed base. We've been dedicated to our installed base for 20 years, 20-plus years. We have an installed base that is not only largest in every single cloud, it's in every available from every enterprise system maker, it's used by companies of just about every industry. And every -- anytime you see a NVIDIA GPU, it runs our stack. It's architecturally compatible, something we've been dedicated to for a very long time. We're very disciplined about it. We make it our, if you will, architecture compatibility is job one. And that has conveyed to the world, the certainty of our platform stability. NVIDIA's platform stability certainty is the reason why everybody builds on us first and the reason why everybody optimizes on us first. All the engineering and all the work that you do, all the invention of technologies that you build on top of NVIDIA accrues to the -- and benefits everybody that uses our GPUs. And we have such a large installed base, large -- millions and millions of GPUs in cloud, 100 million GPUs from people’s PCs just about every workstation in the world, and they all architecturally compatible. And so, if you are an inference platform and you're deploying an inference application, you are basically an application provider. And as a software application provider, you're looking for large installed base. Data processing, before you could train a model, you have to curate the data, you have to dedupe the data, maybe you have to augment the data with synthetic data. So, process the data, clean the data, align the data, normalize the data, all of that data is measured not in bytes or megabytes, it's measured in terabytes and petabytes. And the amount of data processing that you do before data engineering, before that you do training is quite significant. It could represent 30%, 40%, 50% of the amount of work that you ultimately do. And what you -- and ultimately creating a data driven machine learning service. And so data processing is just a massive part. We accelerate Spark, we accelerate Python. One of the coolest things that we just did is called cuDF Pandas. Without one line of code, Pandas, which is the single most successful data science framework in the world. Pandas now is accelerated by NVIDIA CUDA. And just out-of-the box, without the line of code and so the acceleration is really quite terrific and people are just incredibly excited about it. And Pandas was designed for one purpose and one purpose only, really data processing, it's for data science. And so NVIDIA CUDA gives you all of that. Operator: Your final question comes from the line of Harlan Sur of J.P. Morgan. Your line is open. Harlan Sur: Good afternoon. Thanks for taking my question. If you look at the history of the tech industry like those companies that have been successful have always been focused on ecosystem; silicon, hardware, software, strong partnerships and just as importantly, right, an aggressive cadence of new products, more segmentation over time. The team recently announced a more aggressive new product cadence in data center from two years to now every year with higher levels of segmentation, training, optimization in printing CPU, GPU, DPU networking. How do we think about your R&D OpEx growth outlook to support a more aggressive and expanding forward roadmap, but more importantly, what is the team doing to manage and drive execution through all of this complexity? Jensen Huang: Gosh. Boy, that's just really excellent. You just wrote NVIDIA's business plan, and so you described our strategy. First of all, there is a fundamental reason why we accelerate our execution. And the reason for that is because it fundamentally drives down cost. When the combination of TensorRT-LLM and H200 reduce the cost for our customers for large model inference by a factor of four, and so that includes, of course, our speeds and feeds, but mostly it's because of our software, mostly the software benefits because of the architecture. And so we want to accelerate our roadmap for that reason. The second reason is to expand the reach of generative AI, the world's number of data center configurations -- this is kind of the amazing thing. NVIDIA is in every cloud, but not one cloud is the same. NVIDIA is working with every single cloud service provider and not one of the networking control plane, security posture is the same. Everybody's platform is different and yet we're integrated into all of their stacks, all of their data centers and we work incredibly well with all of them. And not to mention, we then take the whole thing and we create AI factories that are standalone. We take our platform, we can put them into supercomputers, we can put them into enterprise. Bringing AI to enterprise is something generative AI Enterprise something nobody's ever done before. And we're right now in the process of going to market with all of that. And so the complexity includes, of course, all the technologies and segments and the pace. It includes the fact that we are architecturally compatible across every single one of those. It includes all of the domain specific libraries that we create. The reason why every computer company, without thinking, can integrate NVIDIA into their roadmap and take it to market. And the reason for that is, because there is market demand for it. There is market demand in healthcare, there is market demand in manufacturing, there is market demand, of course, in AI, including financial services, in supercomputing and quantum computing. The list of markets and segments that we have domain specific libraries is incredibly broaden. And then finally, we have an end-to-end solution for data centers; InfiniBand networking, Ethernet networking, x86, ARM, just about every permutation combination of solutions -- technology solutions and software stacks provided. And that translates to having the largest number of ecosystem software developers; the largest ecosystem of system makers; the largest and broadest distribution partnership network; and ultimately, the greatest reach. And that takes -- surely that takes a lot of energy. But the thing that really holds it together, and this is a great decision that we made decades ago, which is everything is architecturally compatible. When we develop a domain specific language that runs on one GPU, it runs on every GPU. When we optimize TensorRT for the cloud, we optimized it for enterprise. When we do something that brings in a new feature, a new library, a new feature or a new developer, they instantly get the benefit of all of our reach. And so that discipline, that architecture compatible discipline that has lasted more than a couple of decades now, is one of the reasons why NVIDIA is still really, really efficient. I mean, we're 28,000 people large and serving just about every single company, every single industry, every single market around the world. Operator: Thank you. I will now turn the call back over to Jensen Huang for closing remarks. Jensen Huang: Our strong growth reflects the broad industry platform transition from general purpose to accelerated computing and generative AI. Large language models start-ups consumer Internet companies and global cloud service providers are the first movers. The next waves are starting to build. Nations and regional CSPs are building AI clouds to serve local demand. Enterprise software companies like Adobe and Dropbox, SAP and ServiceNow are adding AI copilots and assistants to their platforms. Enterprises in the world's largest industries are creating custom AIs to automate and boost productivity. The generative AI era is in full steam and has created the need for a new type of data center, an AI factory; optimized for refining data and training, and inference, and generating AI. AI factory workloads are different and incremental to legacy data center workloads supporting IT tasks. AI factories run copilots and AI assistants, which are significant software TAM expansion and are driving significant new investment. Expanding the $1 trillion traditional data center infrastructure installed base, empowering the AI Industrial Revolution. NVIDIA H100 HGX with InfiniBand and the NVIDIA AI software stack define an AI factory today. As we expand our supply chain to meet the world's demand, we are also building new growth drivers for the next wave of AI. We highlighted three elements to our new growth strategy that are hitting their stride: CPU, networking, and software and services. Grace is NVIDIA's first data center CPU. Grace and Grace Hopper are in full production and ramping into a new multi-billion dollar product line next year. Irrespective of the CPU choice, we can help customers build an AI factory. NVIDIA networking now exceeds $10 billion annualized revenue run rate. InfiniBand grew five-fold year-over-year, and is positioned for excellent growth ahead as the networking of AI factories. Enterprises are also racing to adopt AI and Ethernet is the standard networking. This week we announced an Ethernet for AI platform for enterprises. NVIDIA Spectrum-X is an end-to-end solution of Bluefield SuperNIC, Spectrum-4 Ethernet switch and software that boosts Ethernet performance by up to 1.6x for AI workloads. Dell, HPE and Lenovo have joined us to bring a full generative AI solution of NVIDIA AI computing, networking and software to the world's enterprises. NVIDIA software and services is on track to exit the year at an annualized run rate of $1 billion. Enterprise software platforms like ServiceNow and SAP need to build and operate proprietary AI. Enterprises need to build and deploy custom AI copilots. We have the AI technology, expertise and scale to help customers build custom models with their proprietary data on NVIDIA DGX Cloud and deploy the AI applications on enterprise grade NVIDIA AI Enterprise. NVIDIA is essentially an AI foundry. NVIDIA's GPUs, CPUs, networking, AI foundry services and NVIDIA AI Enterprise software are all growth engines in full throttle. Thanks for joining us today. We look forward to updating you on our progress next quarter. Operator: This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is David, and I'll be your conference operator today. At this time, I'd like to welcome everyone to NVIDIA's Second Quarter Earnings Call. Today's conference is being recorded. All lines have been placed on mute to prevent any background noise. After the speakers’ remarks, there will be a question-and-answer session. [Operator Instructions] Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone and welcome to NVIDIA's conference call for the second quarter of fiscal 2024. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the third quarter of fiscal 2024. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, August 23, 2023, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. And with that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. We had an exceptional quarter. Record Q2 revenue of $13.51 billion was up 88% sequentially and up 101% year-on-year, and above our outlook of $11 billion. Let me first start with Data Center. Record revenue of $10.32 billion was up 141% sequentially and up 171% year-on-year. Data Center compute revenue nearly tripled year-on-year, driven primarily by accelerating demand from cloud service providers and large consumer Internet companies for HGX platform, the engine of generative AI and large language models. Major companies, including AWS, Google Cloud, Meta, Microsoft Azure and Oracle Cloud as well as a growing number of GPU cloud providers are deploying, in volume, HGX systems based on our Hopper and Ampere architecture Tensor Core GPUs. Networking revenue almost doubled year-on-year, driven by our end-to-end InfiniBand networking platform, the gold standard for AI. There is tremendous demand for NVIDIA accelerated computing and AI platforms. Our supply partners have been exceptional in ramping capacity to support our needs. Our data center supply chain, including HGX with 35,000 parts and highly complex networking has been built up over the past decade. We have also developed and qualified additional capacity and suppliers for key steps in the manufacturing process such as [indiscernible] packaging. We expect supply to increase each quarter through next year. By geography, data center growth was strongest in the U.S. as customers direct their capital investments to AI and accelerated computing. China demand was within the historical range of 20% to 25% of our Data Center revenue, including compute and networking solutions. At this time, let me take a moment to address recent reports on the potential for increased regulations on our exports to China. We believe the current regulation is achieving the intended results. Given the strength of demand for our products worldwide, we do not anticipate that additional export restrictions on our Data Center GPUs, if adopted, would have an immediate material impact to our financial results. However, over the long term, restrictions prohibiting the sale of our Data Center GPUs to China, if implemented, will result in a permanent loss and opportunity for the U.S. industry to compete and lead in one of the world's largest markets. Our cloud service providers drove exceptional strong demand for HGX systems in the quarter, as they undertake a generational transition to upgrade their data center infrastructure for the new era of accelerated computing and AI. The NVIDIA HGX platform is culminating of nearly two decades of full stack innovation across silicon, systems, interconnects, networking, software and algorithms. Instances powered by the NVIDIA H100 Tensor Core GPUs are now generally available at AWS, Microsoft Azure and several GPU cloud providers, with others on the way shortly. Consumer Internet companies also drove the very strong demand. Their investments in data center infrastructure purpose-built for AI are already generating significant returns. For example, Meta, recently highlighted that since launching Reels, AI recommendations have driven a more than 24% increase in time spent on Instagram. Enterprises are also racing to deploy generative AI, driving strong consumption of NVIDIA powered instances in the cloud as well as demand for on-premise infrastructure. Whether we serve customers in the cloud or on-prem through partners or direct, their applications can run seamlessly on NVIDIA AI enterprise software with access to our acceleration libraries, pre-trained models and APIs. We announced a partnership with Snowflake to provide enterprises with accelerated path to create customized generative AI applications using their own proprietary data, all securely within the Snowflake Data Cloud. With the NVIDIA NeMo platform for developing large language models, enterprises will be able to make custom LLMs for advanced AI services, including chatbot, search and summarization, right from the Snowflake Data Cloud. Virtually, every industry can benefit from generative AI. For example, AI Copilot such as those just announced by Microsoft can boost the productivity of over 1 billion office workers and tens of millions of software engineers. Billions of professionals in legal services, sales, customer support and education will be available to leverage AI systems trained in their field. AI Copilot and assistants are set to create new multi-hundred billion dollar market opportunities for our customers. We are seeing some of the earliest applications of generative AI in marketing, media and entertainment. WPP, the world's largest marketing and communication services organization, is developing a content engine using NVIDIA Omniverse to enable artists and designers to integrate generative AI into 3D content creation. WPP designers can create images from text prompts while responsibly trained generative AI tools and content from NVIDIA partners such as Adobe and Getty Images using NVIDIA Picasso, a foundry for custom generative AI models for visual design. Visual content provider Shutterstock is also using NVIDIA Picasso to build tools and services that enables users to create 3D scene background with the help of generative AI. We've partnered with ServiceNow and Accenture to launch the AI Lighthouse program, fast tracking the development of enterprise AI capabilities. AI Lighthouse unites the ServiceNow enterprise automation platform and engine with NVIDIA accelerated computing and with Accenture consulting and deployment services. We are collaborating also with Hugging Face to simplify the creation of new and custom AI models for enterprises. Hugging Face will offer a new service for enterprises to train and tune advanced AI models powered by NVIDIA HGX cloud. And just yesterday, VMware and NVIDIA announced a major new enterprise offering called VMware Private AI Foundation with NVIDIA, a fully integrated platform featuring AI software and accelerated computing from NVIDIA with multi-cloud software for enterprises running VMware. VMware's hundreds of thousands of enterprise customers will have access to the infrastructure, AI and cloud management software needed to customize models and run generative AI applications such as intelligent chatbot, assistants, search and summarization. We also announced new NVIDIA AI enterprise-ready servers featuring the new NVIDIA L40S GPU built for the industry standard data center server ecosystem and BlueField-3 DPU data center infrastructure processor. L40S is not limited by [indiscernible] supply and is shipping to the world's leading server system makers (ph). L40S is a universal data center processor designed for high volume data center standing out to accelerate the most compute-intensive applications, including AI training and inventing through the designing, visualization, video processing and NVIDIA Omniverse industrial digitalization. NVIDIA AI enterprise ready servers are fully optimized for VMware, Cloud Foundation and Private AI Foundation. Nearly 100 configurations of NVIDIA AI enterprise ready servers will soon be available from the world's leading enterprise IT computing companies, including Dell, HP and Lenovo. The GH200 Grace Hopper Superchip which combines our ARM-based Grace CPU with Hopper GPU entered full production and will be available this quarter in OEM servers. It is also shipping to multiple supercomputing customers, including Atmos (ph), National Labs and the Swiss National Computing Center. And NVIDIA and SoftBank are collaborating on a platform based on GH200 for generative AI and 5G/6G applications. The second generation version of our Grace Hopper Superchip with the latest HBM3e memory will be available in Q2 of calendar 2024. We announced the DGX GH200, a new class of large memory AI supercomputer for giant AI language model, recommendator systems and data analytics. This is the first use of the new NVIDIA [indiscernible] switch system, enabling all of its 256 Grace Hopper Superchips to work together as one, a huge jump compared to our prior generation connecting just eight GPUs over [indiscernible]. DGX GH200 systems are expected to be available by the end of the year, Google Cloud, Meta and Microsoft among the first to gain access. Strong networking growth was driven primarily by InfiniBand infrastructure to connect HGX GPU systems. Thanks to its end-to-end optimization and in-network computing capabilities, InfiniBand delivers more than double the performance of traditional Ethernet for AI. For billions of dollar AI infrastructures, the value from the increased throughput of InfiniBand is worth hundreds of [indiscernible] and pays for the network. In addition, only InfiniBand can scale to hundreds of thousands of GPUs. It is the network of choice for leading AI practitioners. For Ethernet-based cloud data centers that seek to optimize their AI performance, we announced NVIDIA Spectrum-X, an accelerated networking platform designed to optimize Ethernet for AI workloads. Spectrum-X couples the Spectrum or Ethernet switch with the BlueField-3 DPU, achieving 1.5x better overall AI performance and power efficiency versus traditional Ethernet. BlueField-3 DPU is a major success. It is in qualification with major OEMs and ramping across multiple CSPs and consumer Internet companies. Now moving to gaming. Gaming revenue of $2.49 billion was up 11% sequentially and 22% year-on-year. Growth was fueled by GeForce RTX 40 Series GPUs for laptops and desktop. End customer demand was solid and consistent with seasonality. We believe global end demand has returned to growth after last year's slowdown. We have a large upgrade opportunity ahead of us. Just 47% of our installed base have upgraded to RTX and about 20% of the GPU with an RTX 3060 or higher performance. Laptop GPUs posted strong growth in the key back-to-school season, led by RTX 4060 GPUs. NVIDIA's GPU-powered laptops have gained in popularity, and their shipments are now outpacing desktop GPUs from several regions around the world. This is likely to shift the reality of our overall gaming revenue a bit, with Q2 and Q3 as the stronger quarters of the year, reflecting the back-to-school and holiday build schedules for laptops. In desktop, we launched the GeForce RTX 4060 and the GeForce RTX 4060 TI GPUs, bringing the Ada Lovelace architecture down to price points as low as $299. The ecosystem of RTX and DLSS games continue to expand. 35 new games added to DLSS support, including blockbusters such as Diablo IV and Baldur’s Gate 3. There's now over 330 RTX accelerated games and apps. We are bringing generative AI to gaming. At COMPUTEX, we announced NVIDIA Avatar Cloud Engine or ACE for games, a custom AI model foundry service. Developers can use this service to bring intelligence to non-player characters. And it harnesses a number of NVIDIA Omniverse and AI technologies, including NeMo, Riva and Audio2Face. Now moving to Professional Visualization. Revenue of $375 million was up 28% sequentially and down 24% year-on-year. The Ada architecture ramp drove strong growth in Q2, rolling out initially in laptop workstations with a refresh of desktop workstations coming in Q3. These will include powerful new RTX systems with up to 4 NVIDIA RTX 6000 GPUs, providing more than 5,800 teraflops of AI performance and 192 gigabytes of GPU memory. They can be configured with NVIDIA AI enterprise or NVIDIA Omniverse inside. We also announced three new desktop workstation GPUs based on the Ada generation. The NVIDIA RTX 5000, 4500 and 4000, offering up to 2x the RT core throughput and up to 2x faster AI training performance compared to the previous generation. In addition to traditional workloads such as 3D design and content creation, new workloads in generative AI, large language model development and data science are expanding the opportunity in pro visualization for our RTX technology. One of the key themes in Jensen's keynote [indiscernible] earlier this month was the conversion of graphics and AI. This is where NVIDIA Omniverse is positioned. Omniverse is OpenUSD's native platform. OpenUSD is a universal interchange that is quickly becoming the standard for the 3D world, much like HTML is the universal language for the 2D [indiscernible]. Together, Adobe, Apple, Autodesk, Pixar and NVIDIA form the Alliance for OpenUSD. Our mission is to accelerate OpenUSD's development and adoption. We announced new and upcoming Omniverse cloud APIs, including RunUSD and ChatUSD to bring generative AI to OpenUSD workload. Moving to automotive. Revenue was $253 million, down 15% sequentially and up 15% year-on-year. Solid year-on-year growth was driven by the ramp of self-driving platforms based on [indiscernible] or associated with a number of new energy vehicle makers. The sequential decline reflects lower overall automotive demand, particularly in China. We announced a partnership with MediaTek to bring drivers and passengers new experiences inside the car. MediaTek will develop automotive SoCs and integrate a new product line of NVIDIA's GPU chiplet. The partnership covers a wide range of vehicle segments from luxury to entry level. Moving to the rest of the P&L. GAAP gross margins expanded to 70.1% and non-GAAP gross margin to 71.2%, driven by higher data center sales. Our Data Center products include a significant amount of software and complexity, which is also helping drive our gross margin. Sequential GAAP operating expenses were up 6% and non-GAAP operating expenses were up 5%, primarily reflecting increased compensation and benefits. We returned approximately $3.4 billion to shareholders in the form of share repurchases and cash dividends. Our Board of Directors has just approved an additional $25 billion in stock repurchases to add to our remaining $4 billion of authorization as of the end of Q2. Let me turn to the outlook for the third quarter of fiscal 2024. Demand for our Data Center platform where AI is tremendous and broad-based across industries on customers. Our demand visibility extends into next year. Our supply over the next several quarters will continue to ramp as we lower cycle times and work with our supply partners to add capacity. Additionally, the new L40S GPU will help address the growing demand for many types of workloads from cloud to enterprise. For Q3, total revenue is expected to be $16 billion, plus or minus 2%. We expect sequential growth to be driven largely by Data Center with gaming and ProViz also contributing. GAAP and non-GAAP gross margins are expected to be 71.5% and 72.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $2.95 billion and $2 billion, respectively. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $100 million, excluding gains and losses from non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 14.5%, plus or minus 1%, excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight some upcoming events for the financial community. We will attend the Jefferies Tech Summit on August 30 in Chicago, the Goldman Sachs Conference on September 5 in San Francisco, the Evercore Semiconductor Conference on September 6 as well as the Citi Tech Conference on September 7, both in New York. And the BofA Virtual AI conference on September 11. Our earnings call to discuss the results of our third quarter of fiscal 2024 is scheduled for Tuesday, November 21. Operator, we will now open the call for questions. Could you please poll for questions for us? Thank you. Operator: Thank you. [Operator Instructions] We'll take our first question from Matt Ramsay with TD Cowen. Your line is now open. Matt Ramsay: Yes. Thank you very much. Good afternoon. Obviously, remarkable results. Jensen, I wanted to ask a question of you regarding the really quickly emerging application of large model inference. So I think it's pretty well understood by the majority of investors that you guys have very much a lockdown share of the training market. A lot of the smaller market -- smaller model inference workloads have been done on ASICs or CPUs in the past. And with many of these GPT and other really large models, there's this new workload that's accelerating super-duper quickly on large model inference. And I think your Grace Hopper Superchip products and others are pretty well aligned for that. But could you maybe talk to us about how you're seeing the inference market segment between small model inference and large model inference and how your product portfolio is positioned for that? Thanks. Jensen Huang: Yeah. Thanks a lot. So let's take a quick step back. These large language models are fairly -- are pretty phenomenal. It does several things, of course. It has the ability to understand unstructured language. But at its core, what it has learned is the structure of human language. And it has encoded or within it -- compressed within it a large amount of human knowledge that it has learned by the corpuses that it studied. What happens is, you create these large language models and you create as large as you can, and then you derive from it smaller versions of the model, essentially teacher-student models. It's a process called distillation. And so when you see these smaller models, it's very likely the case that they were derived from or distilled from or learned from larger models, just as you have professors and teachers and students and so on and so forth. And you're going to see this going forward. And so you start from a very large model and it has a large amount of generality and generalization and what's called zero-shot capability. And so for a lot of applications and questions or skills that you haven't trained it specifically on, these large language models miraculously has the capability to perform them. That's what makes it so magical. On the other hand, you would like to have these capabilities in all kinds of computing devices, and so what you do is you distill them down. These smaller models might have excellent capabilities on a particular skill, but they don't generalize as well. They don't have what is called as good zero-shot capabilities. And so they all have their own unique capabilities, but you start from very large models. Operator: Okay. Next, we'll go to Vivek Arya with BofA Securities. Your line is now open. Vivek Arya: Thank you. Just had a quick clarification and a question. Colette, if you could please clarify how much incremental supply do you expect to come online in the next year? You think it's up 20%, 30%, 40%, 50%? So just any sense of how much supply because you said it's growing every quarter. And then Jensen, the question for you is, when we look at the overall hyperscaler spending, that buy is not really growing that much. So what is giving you the confidence that they can continue to carve out more of that pie for generative AI? Just give us your sense of how sustainable is this demand as we look over the next one to two years? So if I take your implied Q3 outlook of Data Center, $12 billion, $13 billion, what does that say about how many servers are already AI accelerated? Where is that going? So just give some confidence that the growth that you are seeing is sustainable into the next one to two years. Colette Kress: So thanks for that question regarding our supply. Yes, we do expect to continue increasing ramping our supply over the next quarters as well as into next fiscal year. In terms of percent, it's not something that we have here. It is a work across so many different suppliers, so many different parts of building an HGX and many of our other new products that are coming to market. But we are very pleased with both the support that we have with our suppliers and the long time that we have spent with them improving their supply. Jensen Huang: The world has something along the lines of about $1 trillion worth of data centers installed, in the cloud, in enterprise and otherwise. And that $1 trillion of data centers is in the process of transitioning into accelerated computing and generative AI. We're seeing two simultaneous platform shifts at the same time. One is accelerated computing. And the reason for that is because it's the most cost-effective, most energy effective and the most performant way of doing computing now. So what you're seeing, and then all of a sudden, enabled by generative AI, enabled by accelerated compute and generative AI came along. And this incredible application now gives everyone two reasons to transition to do a platform shift from general purpose computing, the classical way of doing computing, to this new way of doing computing, accelerated computing. It's about $1 trillion worth of data centers, call it, $0.25 trillion of capital spend each year. You're seeing the data centers around the world are taking that capital spend and focusing it on the two most important trends of computing today, accelerated computing and generative AI. And so I think this is not a near-term thing. This is a long-term industry transition and we're seeing these two platform shifts happening at the same time. Operator: Next, we go to Stacy Rasgon with Bernstein Research. Your line is open. Stacy Rasgon: Hi, guys. Thanks for taking my question. I was wondering, Colette, if you could tell me like how much of Data Center in the quarter, maybe even the guide is like systems versus GPU, like DGX versus just the H100? What I'm really trying to get at is, how much is like pricing or content or however you want to define that [indiscernible] versus units actually driving the growth going forward. Can you give us any color around that? Colette Kress: Sure, Stacy. Let me help. Within the quarter, our HGX systems were a very significant part of our Data Center as well as our Data Center growth that we had seen. Those systems include our HGX of our Hopper architecture, but also our Ampere architecture. Yes, we are still selling both of these architectures in the market. Now when you think about that, what does that mean from both the systems as a unit, of course, is growing quite substantially, and that is driving in terms of the revenue increases. So both of these things are the drivers of the revenue inside Data Center. Our DGXs are always a portion of additional systems that we will sell. Those are great opportunities for enterprise customers and many other different types of customers that we're seeing even in our consumer Internet companies. The importance there is also coming together with software that we sell with our DGXs, but that's a portion of our sales that we're doing. The rest of the GPUs, we have new GPUs coming to market that we talk about the L40S, and they will add continued growth going forward. But again, the largest driver of our revenue within this last quarter was definitely the HGX system. Jensen Huang: And Stacy, if I could just add something. You say it’s H100 and I know you know what your mental image in your mind. But the H100 is 35,000 parts, 70 pounds, nearly 1 trillion transistors in combination. Takes a robot to build – well, many robots to build because it’s 70 pounds to lift. And it takes a supercomputer to test a supercomputer. And so these things are technology marvels, and the manufacturing of them is really intensive. And so I think we call it H100 as if it’s a chip that comes off of a fab, but H100s go out really as HGXs sent to the world’s hyperscalers and they’re really, really quite large system components, if you will. Operator: Next, we go to Mark Lipacis with Jefferies. Your line is now open. Mark Lipacis: Hi. Thanks for taking my question and congrats on the success. Jensen, it seems like a key part of the success -- your success in the market is delivering the software ecosystem along with the chip and the hardware platform. And I had a two-part question on this. I was wondering if you could just help us understand the evolution of your software ecosystem, the critical elements. And is there a way to quantify your lead on this dimension like how many person years you've invested in building it? And then part two, I was wondering if you would care to share with us your view on the -- what percentage of the value of the NVIDIA platform is hardware differentiation versus software differentiation? Thank you. A – Jensen Huang: Yeah, Mark, I really appreciate the question. Let me see if I could use some metrics, so we have a run time called AI Enterprise. This is one part of our software stack. And this is, if you will, the run time that just about every company uses for the end-to-end of machine learning from data processing, the training of any model that you like to do on any framework you'd like to do, the inference and the deployment, the scaling it out into a data center. It could be a scale-out for a hyperscale data center. It could be a scale-out for enterprise data center, for example, on VMware. You can do this on any of our GPUs. We have hundreds of millions of GPUs in the field and millions of GPUs in the cloud and just about every single cloud. And it runs in a single GPU configuration as well as multi-GPU per compute or multi-node. It also has multiple sessions or multiple computing instances per GPU. So from multiple instances per GPU to multiple GPUs, multiple nodes to entire data center scale. So this run time called NVIDIA AI enterprise has something like 4,500 software packages, software libraries and has something like 10,000 dependencies among each other. And that run time is, as I mentioned, continuously updated and optimized for our installed base for our stack. And that's just one example of what it would take to get accelerated computing to work. The number of code combinations and type of application combinations is really quite insane. And it's taken us two decades to get here. But what I would characterize as probably our -- the elements of our company, if you will, are several. I would say number 1 is architecture. The flexibility, the versatility and the performance of our architecture makes it possible for us to do all the things that I just said, from data processing to training to inference, for preprocessing of the data before you do the inference to the post processing of the data, tokenizing of languages so that you could then train with it. The amount of -- the workflow is much more intense than just training or inference. But anyways, that's where we'll focus and it's fine. But when people actually use these computing systems, it's quite -- requires a lot of applications. And so the combination of our architecture makes it possible for us to deliver the lowest cost ownership. And the reason for that is because we accelerate so many different things. The second characteristic of our company is the installed base. You have to ask yourself, why is it that all the software developers come to our platform? And the reason for that is because software developers seek a large installed base so that they can reach the largest number of end users, so that they could build a business or get a return on the investments that they make. And then the third characteristic is reach. We're in the cloud today, both for public cloud, public-facing cloud because we have so many customers that use -- so many developers and customers that use our platform. CSPs are delighted to put it up in the cloud. They use it for internal consumption to develop and train and to operate recommender systems or search or data processing engines and whatnot all the way to training and inference. And so we're in the cloud, we're in enterprise. Yesterday, we had a very big announcement. It's really worthwhile to take a look at that. VMware is the operating system of the world's enterprise. And we've been working together for several years now, and we're going to bring together -- together, we're going to bring generative AI to the world's enterprises all the way out to the edge. And so reach is another reason. And because of reach, all of the world's system makers are anxious to put NVIDIA's platform in their systems. And so we have a very broad distribution from all of the world's OEMs and ODMs and so on and so forth because of our reach. And then lastly, because of our scale and velocity, we were able to sustain this really complex stack of software and hardware, networking and compute and across all of these different usage models and different computing environments. And we're able to do all this while accelerating the velocity of our engineering. It seems like we're introducing a new architecture every two years. Now we're introducing a new architecture, a new product just about every six months. And so these properties make it possible for the ecosystem to build their company and their business on top of us. And so those in combination makes us special. Operator: Next, we'll go to Atif Malik with Citi. Your line is open. Atif Malik: Hi. Thank you for taking my question. Great job on results and outlook. Colette, I have a question on the core L40S that you guys talked about. Any idea how much of the supply tightness can L40S help with? And if you can talk about the incremental profitability or gross margin contribution from this product? Thank you. Jensen Huang: Yeah, Atif. Let me take that for you. The L40S is really designed for a different type of application. H100 is designed for large-scale language models and processing just very large models and a great deal of data. And so that's not L40S' focus. L40S' focus is to be able to fine-tune models, fine-tune pretrained models, and it'll do that incredibly well. It has a transform engine. It's got a lot of performance. You can get multiple GPUs in a server. It's designed for hyperscale scale-out, meaning it's easy to install L40S servers into the world's hyperscale data centers. It comes in a standard rack, standard server, and everything about it is standard and so it's easy to install. L40S also is with the software stack around it and along with BlueField-3 and all the work that we did with VMware and the work that we did with Snowflakes and ServiceNow and so many other enterprise partners. L40S is designed for the world's enterprise IT systems. And that's the reason why HPE, Dell, and Lenovo and some 20 other system makers building about 100 different configurations of enterprise servers are going to work with us to take generative AI to the world's enterprise. And so L40S is really designed for a different type of scale-out, if you will. It's, of course, large language models. It's, of course, generative AI, but it's a different use case. And so the L40S is going to -- is off to a great start and the world's enterprise and hyperscalers are really clamoring to get L40S deployed. Operator: Next, we'll go to Joe Moore with Morgan Stanley. Your line is open. Joseph Moore: Great. Thank you. I guess the thing about these numbers that's so remarkable to me is the amount of demand that remains unfulfilled, talking to some of your customers. As good as these numbers are, you sort of more than tripled your revenue in a couple of quarters. There's a demand, in some cases, for multiples of what people are getting. So can you talk about that? How much unfulfilled demand do you think there is? And you talked about visibility extending into next year. Do you have line of sight into when you get to see supply-demand equilibrium here? Jensen Huang: Yeah. We have excellent visibility through the year and into next year. And we're already planning the next-generation infrastructure with the leading CSPs and data center builders. The demand – easiest way to think about the demand, the world is transitioning from general-purpose computing to accelerated computing. That's the easiest way to think about the demand. The best way for companies to increase their throughput, improve their energy efficiency, improve their cost efficiency is to divert their capital budget to accelerated computing and generative AI. Because by doing that, you're going to offload so much workload off of the CPUs, but the available CPUs is -- in your data center will get boosted. And so what you're seeing companies do now is recognizing this -- the tipping point here, recognizing the beginning of this transition and diverting their capital investment to accelerated computing and generative AI. And so that's probably the easiest way to think about the opportunity ahead of us. This isn't a singular application that is driving the demand, but this is a new computing platform, if you will, a new computing transition that's happening. And data centers all over the world are responding to this and shifting in a broad-based way. Operator: Next, we go to Toshiya Hari with Goldman Sachs. Your line is now open. Toshiya Hari: Hi. Thank you for taking the question. I had one quick clarification question for Colette and then another one for Jensen. Colette, I think last quarter, you had said CSPs were about 40% of your Data Center revenue, consumer Internet at 30%, enterprise 30%. Based on your remarks, it sounded like CSPs and consumer Internet may have been a larger percentage of your business. If you can kind of clarify that or confirm that, that would be super helpful. And then Jensen, a question for you. Given your position as the key enabler of AI, the breadth of engagements and the visibility you have into customer projects, I'm curious how confident you are that there will be enough applications or use cases for your customers to generate a reasonable return on their investments. I guess I ask the question because there is a concern out there that there could be a bit of a pause in your demand profile in the out years. Curious if there's enough breadth and depth there to support a sustained increase in your Data Center business going forward. Thank you. Colette Kress: Okay. So thank you, Toshiya, on the question regarding our types of customers that we have in our Data Center business. And we look at it in terms of combining our compute as well as our networking together. Our CSPs, our large CSPs are contributing a little bit more than 50% of our revenue within Q2. And the next largest category will be our consumer Internet companies. And then the last piece of that will be our enterprise and high performance computing. Jensen Huang: Toshi, I'm reluctant to guess about the future and so I'll answer the question from the first principle of computer science perspective. It is recognized for some time now that general purpose computing is just not and brute forcing general purpose computing. Using general purpose computing at scale is no longer the best way to go forward. It's too energy costly, it's too expensive, and the performance of the applications are too slow. And finally, the world has a new way of doing it. It's called accelerated computing and what kicked it into turbocharge is generative AI. But accelerated computing could be used for all kinds of different applications that's already in the data center. And by using it, you offload the CPUs. You save a ton of money in order of magnitude, in cost and order of magnitude and energy and the throughput is higher and that's what the industry is really responding to. Going forward, the best way to invest in the data center is to divert the capital investment from general purpose computing and focus it on generative AI and accelerated computing. Generative AI provides a new way of generating productivity, a new way of generating new services to offer to your customers, and accelerated computing helps you save money and save power. And the number of applications is, well, tons. Lots of developers, lots of applications, lots of libraries. It's ready to be deployed. And so I think the data centers around the world recognize this, that this is the best way to deploy resources, deploy capital going forward for data centers. This is true for the world's clouds and you're seeing a whole crop of new GPU specialty -- GPU specialized cloud service providers. One of the famous ones is CoreWeave and they're doing incredibly well. But you're seeing the regional GPU specialist service providers all over the world now. And it's because they all recognize the same thing, that the best way to invest their capital going forward is to put it into accelerated computing and generative AI. We're also seeing that enterprises want to do that. But in order for enterprises to do it, you have to support the management system, the operating system, the security and software-defined data center approach of enterprises, and that's all VMware. And we've been working several years with VMware to make it possible for VMware to support not just the virtualization of CPUs but a virtualization of GPUs as well as the distributed computing capabilities of GPUs, supporting NVIDIA's BlueField for high-performance networking. And all of the generative AI libraries that we've been working on is now going to be offered as a special SKU by VMware's sales force, which is, as we all know, quite large because they reach some several hundred thousand VMware customers around the world. And this new SKU is going to be called VMware Private AI Foundation. And this will be a new SKU that makes it possible for enterprises. And in combination with HP, Dell, and Lenovo's new server offerings based on L40S, any enterprise could have a state-of-the-art AI data center and be able to engage generative AI. And so I think the answer to that question is hard to predict exactly what's going to happen quarter-to-quarter. But I think the trend is very, very clear now that we're seeing a platform shift. Operator: Next, we'll go to Timothy Arcuri with UBS. Your line is now open. Timothy Arcuri: Thanks a lot. Can you talk about the attach rate of your networking solutions to your -- to the compute that you're shipping? In other words, is like half of your compute shipping with your networking solutions more than half, less than half? And is this something that maybe you can use to prioritize allocation of the GPUs? Thank you. Jensen Huang: Well, working backwards, we don't use that to prioritize the allocation of our GPUs. We let customers decide what networking they would like to use. And for the customers that are building very large infrastructure, InfiniBand is, I hate to say it, kind of a no-brainer. And the reason for that because the efficiency of InfiniBand is so significant, some 10%, 15%, 20% higher throughput for $1 billion infrastructure translates to enormous savings. Basically, the networking is free. And so, if you have a single application, if you will, infrastructure or it’s largely dedicated to large language models or large AI systems, InfiniBand is really a terrific choice. However, if you’re hosting for a lot of different users and Ethernet is really core to the way you manage your data center, we have an excellent solution there that we had just recently announced and it’s called Spectrum-X. Well, we’re going to bring the capabilities, if you will, not all of it, but some of it, of the capabilities of InfiniBand to Ethernet so that we can also, within the environment of Ethernet, allow you to – enable you to get excellent generative AI capabilities. So Spectrum-X is just ramping now. It requires BlueField-3 and it supports both our Spectrum-2 and Spectrum-3 Ethernet switches. And the additional performance is really spectacular. BlueField-3 makes it possible and a whole bunch of software that goes along with it. BlueField, as all of you know, is a project really dear to my heart, and it’s off to just a tremendous start. I think it’s a home run. This is the concept of in-network computing and putting a lot of software in the computing fabric is being realized with BlueField-3, and it is going to be a home run. Operator: Our final question comes from the line of Ben Reitzes with Melius. Your line is now open. Benjamin Reitzes: Hi. Good afternoon. Good evening. Thank you for the question, putting me in here. My question is with regard to DGX Cloud. Can you talk about the reception that you're seeing and how the momentum is going? And then Colette, can you also talk about your software business? What is the run rate right now and the materiality of that business? And it does seem like it's already helping margins a bit. Thank you very much. Jensen Huang: DGX Cloud's strategy, let me start there. DGX Cloud's strategy is to achieve several things: number one, to enable a really close partnership between us and the world's CSPs. We recognize that many of our -- we work with some 30,000 companies around the world. 15,000 of them are startups. Thousands of them are generative AI companies and the fastest-growing segment, of course, is generative AI. We're working with all of the world's AI start-ups. And ultimately, they would like to be able to land in one of the world's leading clouds. And so we built DGX Cloud as a footprint inside the world's leading clouds so that we could simultaneously work with all of our AI partners and help blend them easily in one of our cloud partners. The second benefit is that it allows our CSPs and ourselves to work really closely together to improve the performance of hyperscale clouds, which is historically designed for multi-tenancy and not designed for high-performance distributed computing like generative AI. And so to be able to work closely architecturally to have our engineers work hand in hand to improve the networking performance and the computing performance has been really powerful, really terrific. And then thirdly, of course, NVIDIA uses very large infrastructures ourselves. And our self-driving car team, our NVIDIA research team, our generative AI team, our language model team, the amount of infrastructure that we need is quite significant. And none of our optimizing compilers are possible without our DGX systems. Even compilers these days require AI, and optimizing software and infrastructure software requires AI to even develop. It's been well publicized that our engineering uses AI to design our chips. And so the internal -- our own consumption of AI, our robotics team, so on and so forth, Omniverse teams, so on and so forth, all needs AI. And so our internal consumption is quite large as well, and we land that in DGX Cloud. And so DGX Cloud has multiple use cases, multiple drivers, and it's been off to just an enormous success. And our CSPs love it, the developers love it and our own internal engineers are clamoring to have more of it. And it's a great way for us to engage and work closely with all of the AI ecosystem around the world. Colette Kress: And let's see if I can answer your question regarding our software revenue. In part of our opening remarks that we made as well, remember, software is a part of almost all of our products, whether they're our Data Center products, GPU systems or any of our products within gaming and our future automotive products. You're correct, we're also selling it in a standalone business. And that stand-alone software continues to grow where we are providing both the software services, upgrades across there as well. Now we're seeing, at this point, probably hundreds of millions of dollars annually for our software business, and we are looking at NVIDIA AI enterprise to be included with many of the products that we're selling, such as our DGX, such as our PCIe versions of our H100. And I think we're going to see more availability even with our CSP marketplaces. So we're off to a great start, and I do believe we'll see this continue to grow going forward. Operator: And that does conclude today's question-and-answer session. I'll turn the call back over to Jensen Huang for any additional or closing remarks. Jensen Huang: A new computing era has begun. The industry is simultaneously going through 2 platform transitions, accelerated computing and generative AI. Data centers are making a platform shift from general purpose to accelerated computing. The $1 trillion of global data centers will transition to accelerated computing to achieve an order of magnitude better performance, energy efficiency and cost. Accelerated computing enabled generative AI, which is now driving a platform shift in software and enabling new, never-before possible applications. Together, accelerated computing and generative AI are driving a broad-based computer industry platform shift. Our demand is tremendous. We are significantly expanding our production capacity. Supply will substantially increase for the rest of this year and next year. NVIDIA has been preparing for this for over two decades and has created a new computing platform that the world’s industry -- world’s industries can build upon. What makes NVIDIA special are: one, architecture. NVIDIA accelerates everything from data processing, training, inference, every AI model, real-time speech to computer vision, and giant recommenders to vector databases. The performance and versatility of our architecture translates to the lowest data center TCO and best energy efficiency. Two, installed base. NVIDIA has hundreds of millions of CUDA-compatible GPUs worldwide. Developers need a large installed base to reach end users and grow their business. NVIDIA is the developer’s preferred platform. More developers create more applications that make NVIDIA more valuable for customers. Three, reach. NVIDIA is in clouds, enterprise data centers, industrial edge, PCs, workstations, instruments and robotics. Each has fundamentally unique computing models and ecosystems. System suppliers like OEMs, computer OEMs can confidently invest in NVIDIA because we offer significant market demand and reach. Scale and velocity. NVIDIA has achieved significant scale and is 100% invested in accelerated computing and generative AI. Our ecosystem partners can trust that we have the expertise, focus and scale to deliver a strong road map and reach to help them grow. We are accelerating because of the additive results of these capabilities. We’re upgrading and adding new products about every six months versus every two years to address the expanding universe of generative AI. While we increased the output of H100 for training and inference of large language models, we’re ramping up our new L40S universal GPU for scale, for cloud scale-out and enterprise servers. Spectrum-X, which consists of our Ethernet switch, BlueField-3 Super NIC and software helps customers who want the best possible AI performance on Ethernet infrastructures. Customers are already working on next-generation accelerated computing and generative AI with our Grace Hopper. We’re extending NVIDIA AI to the world’s enterprises that demand generative AI but with the model privacy, security and sovereignty. Together with the world’s leading enterprise IT companies, Accenture, Adobe, Getty, Hugging Face, Snowflake, ServiceNow, VMware and WPP and our enterprise system partners, Dell, HPE, and Lenovo, we are bringing generative AI to the world’s enterprise. We’re building NVIDIA Omniverse to digitalize and enable the world’s multi-trillion dollar heavy industries to use generative AI to automate how they build and operate physical assets and achieve greater productivity. Generative AI starts in the cloud, but the most significant opportunities are in the world’s largest industries, where companies can realize trillions of dollars of productivity gains. It is an exciting time for NVIDIA, our customers, partners and the entire ecosystem to drive this generational shift in computing. We look forward to updating you on our progress next quarter. Operator: This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is David, and I'll be your conference operator today. At this time, I'd like to welcome everyone to NVIDIA's First Quarter Earnings Call. Today's conference is being recorded. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there'll be a question-and-answer session. [Operator Instructions] Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2024. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2024. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 24, 2023, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. And with that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. Q1 revenue was $7.19 billion, up 19% sequentially and down 13% year-on-year. Strong sequential growth was driven by record data center revenue, with our gaming and professional visualization platforms emerging from channel inventory corrections. Starting with data center, record revenue of $4.28 billion was up 18% sequentially and up 14% year-on-year, on strong growth by accelerated computing platform worldwide. Generative AI is driving exponential growth in compute requirements and a fast transition to NVIDIA accelerated computing, which is the most versatile, most energy-efficient, and the lowest TCO approach to train and deploy AI. Generative AI drove significant upside in demand for our products, creating opportunities and broad-based global growth across our markets. Let me give you some color across our three major customer categories, cloud service providers or CSPs, consumer Internet companies, and enterprises. First, CSPs around the world are racing to deploy our flagship Hopper and Ampere architecture GPUs to meet the surge in interest from both enterprise and consumer AI applications for training and inference. Multiple CSPs announced the availability of H100 on their platforms, including private previews at Microsoft Azure, Google Cloud, and Oracle Cloud Infrastructure, upcoming offerings at AWS, and general availability at emerging GPU specialized cloud providers like CoreWeave and Lambda. In addition to enterprise AI adoption, these CSPs are serving strong demand for H100 from Generative AI pioneers. Second, consumer Internet companies are also at the forefront of adopting Generative AI and deep learning-based recommendation systems, driving strong growth. For example, Meta has now deployed it's H100 powered Grand Teton AI supercomputer for its AI production and research teams. Third, enterprise demand for AI and accelerated computing is strong. We are seeing momentum in verticals such as automotive, financial services, healthcare, and telecom, where AI and accelerated computing are quickly becoming integral to customers' innovation roadmaps and competitive positioning. For example, Bloomberg announced it has a 50 billion parameter model, BloombergGPT, to help with financial natural language processing tasks such as sentiment analysis, named entity recognition, news classification, and question-answering. Auto Insurance company, CCC Intelligent Solutions is using AI for estimating repairs. And AT&T is working with us on AI to improve fleet dispatches so their field technicians can better serve customers. Among other enterprise customers using NVIDIA AI are Deloitte for logistics and customer service and Amgen for drug discovery and protein engineering. This quarter, we started shipping DGX H100, our Hopper generation AI system, which customers can deploy on-prem. And with the launch of DGX Cloud through our partnership with Microsoft Azure, Google Cloud, and Oracle Cloud Infrastructure, we deliver the promise of NVIDIA DGX to customers from the cloud. Whether the customers deploy DGX on-prem or via DGX Cloud, they get access to NVIDIA AI software, including NVIDIA Base Command, and AI frameworks, and pre-trained models. We provide them with the blueprint for building and operating AI, spanning our expertise across systems, algorithms, data processing, and training methods. We also announced NVIDIA AI Foundations, which are model foundry services available on DGX Cloud, that enable businesses to build, refine, and operate custom large language models and generative AI models, trained with our own proprietary data, created for unique domain-specific tasks. They include NVIDIA NeMo for large language models, NVIDIA Picasso for images, video, and 3D, and NVIDIA BioNeMo for life sciences. Each service has six elements, pre-trained models, frameworks for data processing and curation, proprietary knowledge-based sector databases, systems for fine-tuning, aligning, and guardrailing, optimized inference engines, and support from NVIDIA experts to help enterprises fine-tune models for their custom use cases. ServiceNow, a leading enterprise services platform is an early adopter of DGX Cloud and NeMo. They are developing custom large language models trained on data specifically for the ServiceNow platform. Our collaboration will let ServiceNow create new enterprise-grade generative AI offerings, with the 1,000s of enterprises worldwide running on the ServiceNow platform, including for IT departments, customer service teams, employees, and developers. Generative AI is also driving a step-function increase in inference workloads. Because of their size and complexities, these workloads require acceleration. The latest MLPerf industry benchmark released in April showed NVIDIA's inference platform deliver performance that is orders of magnitude ahead of the industry, with unmatched versatility across diverse workloads. To help customers deploy generative AI applications at scale, at GTC, we announced four major new inference platforms that leverage the NVIDIA AI software stack. These include L4 Tensor Core GPU for AI video, L40 for Omniverse, and graphics rendering, H100 NVL for large language models, and the Grace Hopper Superchip for LLMs and also, recommendation systems and vector databases. Google Cloud is the first CSP to adopt our L4 inference platform with the launch of its G2 virtual machines for generative AI inference and other workloads such as Google Cloud Dataproc, Google AlphaFold, and Google Cloud's Immersive Stream, which render 3D and AR experiences. In addition, Google is integrating our Triton inference server with Google Kubernetes engine and its cloud-based Vertex AI platform. In networking, we saw strong demand at both CSPs and enterprise customers for generative AI and accelerated computing, which require high-performance networking like NVIDIA's Mellanox networking platforms. Demand relating to general purpose CPU infrastructure remain soft. As generative AI applications grow in size and complexity, high performance networks become essential for delivering accelerated computing at data center scale to meet the enormous demand of all training and inferencing. Our 400 gig Quantum-2 InfiniBand platform is the gold standard for AI dedicated infrastructure, with broad adoption across major cloud and consumer Internet platforms such as Microsoft Azure. With the combination of in-network computing technology and the industry's only end-to-end data center scale, optimized software stack, customers routinely enjoy a 20% increase in throughput for their sizable infrastructure investment. For multi-tenant cloud transitioning to support generative AI our high-speed Ethernet platform with BlueField-3 DPUs and Spectrum-4 Ethernet switching, offers the highest available Ethernet network performance. BlueField-3 is in production and has been adopted by multiple hyperscale and CSP customers, including Microsoft Azure, Oracle Cloud, CoreWeave, Baidu, and others. We look forward to sharing more about our 400 gig Spectrum-4 accelerated AI networking platform next week at the COMPUTEX Conference in Taiwan. Lastly, our Grace data center CPU is sampling with customers. At this week's International Supercomputing Conference in Germany, the University of Bristol announced a new supercomputer based on the NVIDIA Grace CPU Superchip, which is 6x more energy-efficient than the previous supercomputer. This adds to the growing momentum for Grace with both CPU only and CPU/GPU opportunities across AI and cloud and supercomputing applications. The coming wave of BlueField-3, Grace and Grace Hopper Superchips will enable a new generation of super energy efficient accelerated data centers. Now, let's move to gaming. Gaming revenue of $2.24 billion was up 22% sequentially, and down 38% year-on-year. Strong sequential growth was driven by sales of the 40 Series GeForce RTX GPUs for both notebooks and desktops. Overall, end demand was solid, and consistent with seasonality, demonstrating resilience against a challenging consumer spending backdrop. The GeForce RTX 40 Series GPU laptops are off to a great start, featuring four NVIDIA inventions, RTX Path Tracing, DLSS 3 AI rendering, Reflex Ultra-Low Latency rendering, and Max-Q, energy efficient technologies. They deliver tremendous gains in industrial design, performance and battery life for gamers and creators. And like our desktop offerings, 40 Series laptops support the NVIDIA Studio platform or software technologies, including acceleration for creative data science and AI workflows, and Omniverse, giving content creators unmatched tools and capabilities. In desktop, we ramped the RTX 4070, which joined the previously launched RTX 4090, 4080, and 4070 Ti GPUs. The RTX 4070 is nearly 3x faster than the RTX 2070 and offers our large installed-base a spectacular upgrade. Last week, we launched the 60 family, RTX 4060, and 4060 Ti, bringing our newest architecture to the world's core gamers starting at just $299. These GPUs for the first time provide 2x the performance of the latest gaming console at mainstream price points. The 4060 Ti is available starting today, while the 4060 will be available in July. Generative AI will be transformative to gaming and content creation from development to run time. At the Microsoft Build Developer Conference earlier this week, we showcased how Windows PCs and workstations with NVIDIA RTX GPUs will be AI-powered at their core. NVIDIA and Microsoft have collaborated on end-to-end software engineering, spanning from the Windows operating system to the NVIDIA graphics drivers, and NeMo's LLM framework to help make Windows on NVIDIA RTX Tensor Core GPUs, a supercharged platform for generative AI. Last quarter, we announced a partnership with Microsoft to bring Xbox PC games to GeForce NOW. The first game from this partnership, Gears 5 is now available with more set to be released in the coming months. There are now over 1,600 games on GeForce NOW, the richest content available on any cloud gaming service. Moving to Pro Visualization. Revenue of $295 million was up 31% sequentially, and down 53% year-on-year. Sequential growth was driven by stronger workstation demand across both mobile and desktop form factors, with strength in key verticals such as Public Sector, Healthcare, and Automotive. We believe the channel inventory correction is behind us. The ramp of our Ada Lovelace GPU architecture in workstations kicked-off a major product cycle. At GTC, we announced six new RTX GPUs for laptops and desktop workstations, with further rollout planned in the coming quarters. Generative AI is a major new workload for NVIDIA-powered workstation. Our collaboration with Microsoft transformed windows into the ideal platform for creators and designers, harnessing generative AI to elevate their creativity and productivity. At GTC, we announced NVIDIA Omniverse Cloud, an NVIDIA fully managed service running in Microsoft Azure that includes the full suite of Omniverse applications and NVIDIA OVX infrastructure. Using this full stack cloud environment, customers can design, develop, deploy, and manage industrial metaverse applications. NVIDIA Omniverse Cloud will be available starting in the second half of this year. Microsoft NVIDIA will also connect Office 365 applications with Omniverse. Omniverse Cloud is being used by companies to digitalize their workflows from design and engineering to smart factories and 3D content generation for marketing. The automotive industry has been a leading early adopter of Omniverse, including companies such as BMW Group, Geely Lotus, General Motors, and Jaguar Land Rover. Moving to Automotive. Revenue was $296 million, up 1% sequentially, and up 114% from a year ago. Our strong year-on-year growth was driven by the ramp of the NVIDIA DRIVE Orin across a number of new energy vehicles. As we announced in March, our automotive design win pipeline over the next six years now stands at $14 billion, up from $11 billion a year ago, giving us visibility into continued growth over the coming years. Sequentially, growth moderated as some NEV customers in China are adjusting their production schedules to reflect slower than expected demand growth. We expect this dynamic to linger for the rest of the calendar year. During the quarter, we expanded our partnership with BYD, the world's leading manufacturer of NEVs. Our new design win will extend BYD's use of the DRIVE Orin to its next-generation high-volume Dynasty, and Ocean series of vehicles, set to start production in calendar 2024. Moving to the rest of the P&L. GAAP gross margins was 64.6%, and non-GAAP gross margins were 66.8%. Gross margins have now largely recovered to prior peak level, and we have absorbed higher costs, and offset them by innovating and delivering higher valued products as well as products incorporating more and more software. Sequentially, GAAP operating expenses were down 3%, and non-GAAP operating expenses were down 1%, We have held OpEx at roughly the same level over the last past four quarters. We're working through the inventory corrections in gaming and professional visualization. We now expect to increase investments in the business while also delivering operating leverage. We returned $99 million to shareholders in the form of cash dividends. At the end of the Q1, we have approximately $7 billion remaining under our share repurchase authorization through December 2023. Let me turn to the outlook for the second quarter fiscal '24. Total revenue is expected to be $11 billion, plus or minus 2%. We expect this sequential growth to largely be driven by data center, reflecting a steep increase in demand related to generative AI and large language models. This demand has extended our data center visibility out a few quarters and we have procured substantially higher supply for the second half of the year. GAAP and non-GAAP gross margins are expected to be 68.6% and 70% respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $2.71 billion and $1.9 billion, respectively. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $90 million, excluding gains and losses from non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 14%, plus or minus 1%, excluding any discrete items. Capital expenditures are expected to be approximately $300 million to $350 million. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight some of the upcoming events, Jensen will give the COMPUTEX keynote address in person in Taipei this coming Monday, May 29 local time, which will be Sunday evening in the U.S. In addition, we will be attending the BofA Global Technology Conference in San Francisco on June 6. And Rosenblatt Virtual Technology Summit on The Age of AI on June 7, and the New Street Future of Transportation Virtual Conference on June 12. Our earnings call to discuss the results of our second quarter fiscal '24 is scheduled for Wednesday, August 23. Well, that covers our opening remarks. We're now going to open the call for questions. Operator, would you please poll for questions? Operator: Thank you. [Operator Instructions] We'll take our first question from Toshiya Hari with Goldman Sachs. Your line is open. Toshiya Hari: Hi. Good afternoon. Thank you so much for taking the question and congrats on the strong results, and incredible outlook. Just one question on data center. Colette, you mentioned the vast majority of the sequential increase in revenue this quarter will come from data center. I was curious what the construct is there, if you can speak to, what the key drivers are from April to July and perhaps more importantly, you talked about visibility into the second half of the year. I'm guessing it's more of a supply problem at this point, what kind of sequential growth beyond the July quarter can your supply chain support at this point? Thank you. Colette Kress: Okay. So, a lot of different questions there. So, let me see if I can start and I am sure Jensen will have some following up comments. So when we talk about our sequential growth that we're expecting between Q1 and Q2, our generative AI large language models are driving the surge in demand, and it's broad-based across both our consumer Internet companies, our CSPs, our enterprises, and our AI start-ups. It is also interest in both of our architectures, both of our Hopper latest architecture as well as our Ampere architecture. This is not surprising as we generally often sell both of our architectures at the same time. This is also a key area where deep recommendators are driving growth. And we also expect to see growth both in our computing as well as in our networking business. So, those are some of the key things that we have baked in when we think about the guidance we provided to Q2. We also surfaced in our opening remarks that we are working on both supply today for this quarter, but we have also procured a substantial amount of supply for the second half. We have significant supply chain flow to serve our significant customer demand that we see, and this is demand that we see across a wide range of different customers. They are building platforms for some of the largest enterprises, but also setting things up at the CSPs and the large consumer Internet companies. So, we have visibility right now for our data center demand that has probably extended out a few quarters and that's led us to working on quickly procuring that substantial supply for the second half. I'm going to pause there and see if Jensen wants to add a little bit more. Jensen Huang: I thought that was great color. Thank you. Operator: Next we'll go to C.J. Muse with Evercore ISI. Your line is open. C.J. Muse: Yeah. Good afternoon. Thank you for taking the question. I guess with data center, you are essentially doubling quarter-on-quarter, two natural kind of questions that relate to one another come to mind. Number one, where are we in terms of driving acceleration into servers to support AI? And as part of that, as you deal with longer cycle times with TSMC and your other partners, how are you thinking about managing their commitments there with where you want to manage your lead times in the coming years to best match that supply and demand? Thanks so much. Jensen Huang: Yeah, C.J. Thanks for the question. I'll start backwards. The -- remember, we were in full production of both Ampere and Hopper when I -- when the ChatGPT moment came. And it helped everybody crystallize how to transition from the technology of large language models to a product and service based on a chatbot. The integration of guardrails and alignment systems were through reinforcement learning human feedback, knowledge vector data bases for proprietary knowledge, connection to search, all of that came together in a really wonderful way and it's the reason why I call it the iPhone moment, all the technology came together and helped everybody realize what an amazing product that can be and what capabilities it can have. And so we were already in full production. NVIDIA's supply chain flow and our supply chain is very significant as you know. And we build supercomputers in volume, and these are giant systems and we build them in volume. It includes, of course, the GPUs, but on our GPUs, the system boards have 35,000 other components. And the networking, and fiberoptics, and the incredible transceivers and the NICs, the Smart NICs, the switches, all of that has to come together in order for us to stand-up a data center. And so we were already in full production when the moment came. We had to really significantly increase our procurement substantially for the second half as Colette said. Now, let me talk about the bigger picture and why the entire world's data centers are moving towards accelerated computing. It's been known for some time and you've heard me talk about it, that accelerated computing is a full stack problem, but it is full stack challenge, but if we could successfully do it in a large number of application domain has taken us 15 years. If - sufficiently that almost the entire data centers' major applications could be accelerated you could reduce the amount of energy consumed and the amount of cost for our data center substantially by an order of magnitude. It takes -- it costs a lot of money to do it because you have to do all the software and everything and you have to build all the systems and so on and so forth, but we’ve been at it for 15 years. And what happened is, when generative AI came along, it triggered a killer app for this computing platform that's been in preparation for some time. And so, now we see ourselves in two simultaneous transitions. The world's $1 trillion data center is nearly populated entirely by CPUs today, and $1 trillion, $250 billion a year, it's growing of course. But over the last four years, call it a $1 trillion worth of infrastructure installed. And it's all completely based on CPUs and dumb NICs. It's basically unaccelerated. In the future, it's fairly clear now with this -- with generative AI becoming the primary workload of most of the world's data centers generating information, it is very clear now that -- and the fact that accelerated computing is so energy efficient, that the budget of the data center will shift very dramatically towards accelerated computing and you're seeing that now. We're going through that moment right now as we speak. While the world's data center CapEx budget is limited but at the same time we're seeing incredible orders to retool the world's data centers. And so I think you're starting -- you're seeing the beginning of call it a 10-year transition to basically recycle or reclaim the world's data centers and build it out as accelerated computing. You'll have a pretty dramatic shift in the spend of the data center from traditional computing, and to accelerated computing with smart NICs, smart switches, of course, GPUs, and the workload is going to be predominantly generative AI. Operator: And we'll move to our next question, Vivek Arya with BofA Securities. Your line is open. Vivek Arya: Thanks for the question. Could I just wanted to clarify does visibility mean data center sales can continue to grow sequentially in Q3 and Q4 or do they sustain at Q2 levels? I just wanted to clarify that. And then Jensen, my question is that, given this very strong demand environment, what does that do to the competitive landscape? Does it invite more competition in terms of custom ASICs? Does it invite more competition in terms of other GPU solutions or other kinds of solutions? How do you see the competitive landscape change over the next two to three years? Colette Kress: Yeah, Vivek. Thanks for the question. Let me see if I can add a little bit more color. We believe that the supply that we will have for the second half of the year will be substantially larger than H1. So, we are expecting not only the demand that we just saw in this last quarter, the demand that we have in Q2 for our forecast, but also planning on seeing something in the second half of the year. We just have to be careful here. But we are not here to guide on the second half of that. Yes, we do plan a substantial increase in the second half compared to the first half. Jensen Huang: Regarding competition, we have competition from every direction. Start-ups really-really well-funded and innovative startups, countless of them all over the world. We have competitions from existing semiconductor companies. We have competition from CSPs with internal projects. And many of you know about most of these. And so, we're mindful of competition all the time, and we get competition all the time. But NVIDIA's value proposition at the core is, we are the lowest cost solution. We're the lowest TCO solution. And the reason for that is, because accelerated computing is two things that I talk about often, which is it's a full stack problem, it's a full stack challenge, you have to engineer all of the software and all the libraries and all the algorithms, integrated them into and optimize the frameworks and optimize it for the architecture of not just one ship but the architecture of an entire data center, all the way into the frameworks, all the way into the models. And the amount of engineering and distributed computing, fundamental computer science work is really quite extraordinary. It is the hardest computing as we know. And so, number one, it's a full stack challenge and you have to optimize it across the whole thing and across just the mind blowing number of stacks. We have 400 acceleration libraries. As you know, the amount of libraries and frameworks that we accelerate is pretty mind blowing. The second part is that generative AI is a large scale problem, and it's a data center scale problem, it's another way of thinking that the computer is the data center or to data center is the computer, it's not the chip, it's the data center and it's never happened like this before. And in this particular environment, your networking operating system, your distributed computing engines, your understanding of the architecture of the networking gear, the switches and the computing systems, the computing fabric, that entire system is your computer and that's what you're trying to operate. And so in order to get the best performance, you have to understand full stack and you have to understand data center scale, and that's what accelerated computing is. The second thing is that -- utilization, which talks about the amount of the types of applications that you can accelerate and diversity of our architecture keeps that utilization high. If you can do one thing and do one thing only and incredibly fast, then your data center is largely underutilized and it's hard to scale that up. And the thing is, universal GPU in fact that we accelerate so many stacks, makes our utilization incredibly high, and so number one is throughput, and that's software -- that's a software-intensive problems and data center architecture problems. The second is utilization versatility problem and the third is just data center expertise. We've built five data centers of our own and we've helped companies all over the world build data centers and we integrate our architecture into all the world's clouds. From the moment of delivery of the product to do standing up in the deployment, the time to operations of the data center is measured not -- if you're not good at it and not – not proficient at it, it could take months. Standing up a supercomputer, let's see, some of the largest supercomputers in the world were installed about a year and a half ago and now they're coming online, and so it's not – it unheard of to see a delivery to operations of about a year. Our delivery to operation is measured in weeks. And we've taken data centers and supercomputers and we've turned it into products, and the expertise of the team in doing that is incredible, and so. So, our value proposition is in the final analysis, all of this technology translates in the infrastructure, the highest throughput in the lowest possible cost. And so I think -- our market is of course very, very competitive, very large. But the challenge is really-really great. Operator: Next we go to Aaron Rakers with Wells Fargo. Your line is open. Aaron Rakers: Yeah. Thank you for taking the question and congrats on the quarter. As we kind of think about unpacking the various different growth drivers of the data center business going forward, I'm curious, Colette, of just how we should think about the monetization effect of software, considering that the expansion of your cloud service agreements continues to grow? I'm curious of what -- where do you think we're at in terms of that approach in terms of the AI enterprise software suite and other drivers of software only revenue going forward? Colette Kress: Thanks for the question. Software is really important to our accelerated platforms. Not only do we have a substantial amount of software that we are including in our nearest architecture and essentially, all products that we have. We are now with many different models to help customers start their work in generative AI and accelerated computing. So, anything that we have here from a DGX Cloud and providing those services, helping them build models or as we've discussed the importance of NVIDIA AI enterprise, essentially that operating system for AI. So, all things should continue to grow as we go forward, both the architecture and the infrastructure, as well as both availability of this offering, our ability to monetize [indiscernible] as well. I'll turn it over to Jensen, if he needs to add. Jensen Huang: Yeah. We can see in real-time the growth of generative AI and CSPs, both for training the models, refining the models, as well as deploying the models. As Colette said earlier, inference is now a major driver of accelerated computing because generative AI is used so capably in so many applications already. There are two segments that requires a new stack of software and the two segments are enterprise and industrials. Enterprise requires a new stack of software, because many enterprises need to have all the capabilities that we've talked about, whether it's large language models, the ability to adapt, and for your proprietary use-case and your proprietary data, align it to your own principles, and your own operating domains. You want to have the ability to be able to do that in a high performance computing sandbox, and we that DGX Cloud, and create a model. Then you want to deploy your chatbot or your AI in any Cloud, because you have services and you have agreements with multiple Cloud vendors and depending on the applications, you might deploy it on various clouds. And for the enterprise, we have NVIDIA AI Foundation for helping you create custom models and we have NVIDIA AI Enterprise. NVIDIA AI Enterprise is the only accelerated stack, GPU accelerated stack in the world that is Enterprise safe, and Enterprise supported. There are a constant patching that you have to do, there are 4,000 different packages that buildup NVIDIA AI Enterprise and represents the operating engine, end-to-end operating engine of the entire AI workflow. It's the only one of its kind from data ingestion, data processing, obviously, in order to train an AI model, you have a lot of data, you have to process and package up and curate, and align and there's just a whole bunch of stuff that you have to do to the data to prepare it for training. That amount of data, that could consume some 40%, 50%, 60% of your computing time and so, data processing is very big deal. And then the second aspect of it is training the model, refining the model and the third is deploying model for inferencing. NVIDIA AI Enterprise supports and patches and security patches continuously all of those 4,000 packages of software. And for an Enterprise that wants to deploy their engines, just like they want to deploy Red Hat Linux, this is incredibly complicated software in order to deploy that in every cloud and as well as on-prem, it has to be secure, it has to be supported. And so, NVIDIA AI Enterprise is the second point. The third is Omniverse. Just as people are starting to realize that you need to align an AI to ethics, the same for robotics, you need to align the AI for physics. And aligning an AI for ethics includes a technology called reinforcement learning human feedback. In the case of industrial applications and robotics, it's reinforcement learning Omniverse feedback. And Omniverse is a vital engine for software defined in robotic applications and industries. And so, Omniverse also needs to be a cloud service platform. And so our software stack, the three software stacks, AI Foundation, AI Enterprise and Omniverse runs in all of the world's clouds that we have partnerships, DGX Cloud partnerships with. Azure, we have partnerships on both AI as well as Omniverse. With GTP and Oracle, we have great partnerships in DGX Cloud for AI and AI Enterprise is integrated into all three of them and so I think the -- in order to for us to extend the reach of AI beyond the cloud, and into the world's Enterprise and into the world's industries, you need two new types of -- you need new software stacks in order to make that happen and by putting it in the cloud, integrate it into the world's CSP clouds, it's a great way for us to partner with the sales and the marketing team and the leadership team of all the cloud vendors. Operator: Next we'll go to Timothy Arcuri with UBS. Your line is Open. Tim Arcuri: Thanks a lot. I had a question and then I had a clarification as well. So, the question first is, Jensen, on the InfiniBand versus Ethernet argument, can you sort of speak to that debate and maybe how you see it playing out? I know you need the low late -- the low latency of InfiniBand for AI, but can you sort of talk about the attach rate of your InfiniBand solutions to what you're shipping on the core compute side and maybe whether that's similarly crowding out Ethernet like you are with on the compute side? And then the clarification, Colette, is that there wasn't a share buyback despite you still having about $7 billion on the share repo authorization. Was that just timing? Thanks. Jensen Huang: Colette, how about you go first? You should take the question. Colette Kress: That is correct. We have $7 billion available in recurrent authorization for repurchases. We did not repurchase anything in this last quarter, but we do repurchase opportunistically and we'll consider that as we go forward as well. Thankyou Jensen Huang: InfiniBand and Ethernet are Target different applications in a data center. All right. They both have their place. InfiniBand had a record quarter. We're going to have a giant record year. And InfiniBand has a really -- NVIDIA's Quantum InfiniBand has an exceptional roadmap. It's going to be really incredible. But the two networks are very different. InfiniBand is designed for an AI factory, if you will. If that data center is running a few applications for a few people for a specific use case and it's doing it continuously and that infrastructure costs you, pick a number, $500 million. The difference between InfiniBand and Ethernet could be 15%, 20% in overall throughput. And if you spent $500 million in an infrastructure and the difference is 10% to 20% and it's a $100 million, InfiniBand is basically free. That's the reason why people use it. InfiniBand is effectively free. The difference in data center throughput is just -- it's too great to ignore, and you're using it for that one application and so, however, if your data center is a cloud datacenter and its multi-tenant. It's a bunch of little jobs, a bunch of little jobs and is shared by millions of people. Then Ethernet is really do I answer? There's a new segment in the middle where the Cloud is becoming a generative AI cloud. It's not only AI factory per se. But it's still a multi-tenant Cloud but it wants to run generative AI workloads. This new segment is a wonderful opportunity and at COMPUTEX, I referred to it at the last GTC. At COMPUTEX, we're going to announce a major product line for this segment, which is Ethernet focused generative AI application type of clouds. But InfiniBand is doing fantastically and we're doing record numbers quarter-on-quarter year-on-year. Operator: Next we'll go to Stacy Rasgon with Bernstein Research. Your line is open. Stacy Rasgon: Hi, guys. Thanks for taking my question. I had a question on inference versus training for generative AI. So, you're talking about inference as being a very large opportunity. I guess, two sub parts to that. Is that, besides inference basically scales with like the usage versus like training is more of a one-and-done. And can you give us some sort of even if it's just like qualitatively, like if do you think are influence is bigger than training or vice-versa, like if it's bigger, how much bigger? Is it like the opportunity, is it 5x, is it 10x, is there anything you can give us on those two workloads within generative AI, it would be helpful. Jensen Huang: Yeah. I'll work backwards. You're never done with training. You're always -- every time you deploy, you're collecting new data. When you collect new data, you train with the new data. And so, you're never done training. You're never done producing and processing a Vector database that augments the large language model. You're never done with vectorizing all of the collected structured -- unstructured data that you have. And so, whether you're building a recommender system, a large language model, a vector database, these are probably the three major applications of the three core engines, if you will, of the future of computing. It's all a bunch of other stuff, but obviously these are very three very important ones. They are always running. You're going to see that more-and-more companies realize they have a factory for intelligence, an intelligence factory and in that particular case, it's largely dedicated to training and processing data and vectorizing data and learning representation of the data, so on and so forth. The inference part of it, are APIs that are either open APIs that can be connected to all kinds of applications, APIs that is integrated into workflows. But APIs of all kinds, there'll be 100s of APIs in the company, some of them they built themselves, some of them part that could -- many of them could come from companies like ServiceNow and Adobe that we're partnering with in AI Foundations. And they'll create a whole bunch of generative AI APIs that companies can then connect into their workflows or use as an application. And of course, there will be a whole bunch of Internet Service Companies. So, I think you're seeing for the very first time simultaneously a very significant growth in the segment of AI Factories, as well as a market that -- a segment that really didn't exist before, but now it's growing exponentially, practically by the weak for AI inference with APIs. The simple way to think about it in the end, is that, the world has a $1 trillion of data center installed and they used to be 100% CPUs. In the future, we know we've heard it in enough places and I think this year there is a ISC keynote was actually about the end of Moore's Law. We've seen it in a lot of places now that you can't reasonably scale-out data centers with general-purpose computing and that accelerated computing is the path forward and now it's got a killer app and it's got generative AI, and so the easiest way to think about that is your $1 trillion infrastructure. Every quarters capital CapEx budget would lean very heavily into generative AI into accelerated computing infrastructure everywhere from the number of GPUs that would be used in the CapEx budget to the accelerated switches and accelerated net -- networking chips that connect them all. That the easiest way to think about that is over the next four or five, 10 years, most of that $1 trillion and then compensating adjusting for all the growth in data center still, it will be largely generative AI and so that's probably the easiest way to think about that and that's training as well as inference. Operator: Next, we'll go to Joseph Moore with Morgan Stanley. Your line is open. Joseph Moore: Great. Thank you. I want to follow-up on that, in terms of the focus on inference. It's pretty clear that this is a really big opportunity around large language models, but the cloud customers are also talking about trying to reduce cost per query by very significant amounts. You can talk about the ramifications of that for you guys, is that where some of the specialty insurance products that you launched at GTC come in and just how are you going to help your customers get the cost per query down? Jensen Huang: Yeah. That's a great question. Whether your -- you start by building a large language model and you use that large language model very large version and you could distill them into medium, small and tiny size. And the tiny sized ones, you can put in your phone and your PC and so on and so forth and they all have good -- they all have -- it seems surprising, but they all can do the same thing. But obviously, the zero shot or the generalizeability of the large language model, the biggest one is much more versatile and it can do a lot more amazing things. And the large one would teach the smaller ones, how to be good AIs and so, you use the large one to generate prompts to align the smaller ones and so on and so forth. And so you start by building very large ones. And then you also have to train a whole bunch of smaller ones. Now, that's exactly the reason why we have so many different sizes of our inference. You saw that I announced L4, L40, H100 NBL -- which also have H100 HGX and then we have H100 multi-node with NVLink and so there is -- you could have model sizes of any kind that you like. The other thing that's important is, these are models, but they are connected ultimately to applications. And the applications could have image in, video out, video in, text out, image in, proteins out, text in, 3D out, video in, in the future, 3D graphics out. So, the input and the output requires a lot of pre and post-processing. The pre and post-processing can't be ignored. And this is one of the things that most of the specialized chip arguments fall apart and it's because the length -- the model itself is only call it 25% of the data -- of the overall processing of inference. The rest of it is about preprocessing and post-processing, security, decoding, all kinds of things like that. And so, I think the multimodality aspect of inference, the multi diversity of inference, that it's going to be done in the Cloud on-prem. It's going to be done in multi-cloud, that's the reason why we have the AI Enterprise in all the clouds. It's going to be done on-prem, it's the reason why we have a great partnership with Dell we just announced the other day, called project Helix, that's going to be integrated into third-party services. That's the reason why we have a great partnership with ServiceNow, and Adobe, because they're going to be creating a whole bunch of generative AI capabilities. And so, there's all the diversity, and the reach of generative AI is so broad, you need to have some very fundamental capabilities like what I just described, in order to really address the whole space of it. Operator: Next we'll go to Harlan Sur with JP Morgan. Your line is open. Harlan Sur: Hi. Good afternoon, and congratulations on the strong results and execution. I really appreciate more of the focus or some of the focus today in your networking products. I mean, it's really an integral part to sort of maximize the full performance of your compute platforms. I think so data center networking business is driving a part of $1 billion of revenues per quarter plus or minus, that's 2.5x growth from three years ago, right, when you guys acquired Mellanox. So very strong growth, but given the very high attach of your InfiniBand, Ethernet solutions, your accelerated compute platforms, is the networking run-rate stepping up in line with your compute shipment? And then, what is the team doing to further unlock more networking bandwidth going forward just to keep pace with the significant increase in compute complexity, datasets, requirements for lower latency, better traffic predictability, and so on? Jensen Huang: Yeah, Harlan. I really appreciate that. So, nearly everybody who thinks about AI, they think about that chip, the accelerator chip and in fact, it misses the whole point nearly completely. And I've mentioned before that accelerated computing is about the stack, about the software and networking, remember, we announced a very early-on this networking stack called DOCA and we have the acceleration library call Magnum IO. These two pieces of software are some of the crown jewels of our company. Nobody ever talks about it, because it's hard to understand, but it makes it possible for us to connect 10s of 1,000s of GPUs. How do you connect 10s of 1000s of GPUs, if the operating system of the data center, which is the infrastructure, is not insanely great, and so that's the reason why we're so obsessed about networking in the company. And one of the great things that we have -- we have Mellanox as you know quite well, was the world's highest performance and the unambiguous leader in high performance networking, that's the reason why our two companies are together. You also see that our network expands starting from NVLink, which is a computing fabric with a really super low latency and it communicates using memory references, not network package. And then we take NVLink, we connect it inside multiple GPUs, and I described, going beyond the GPU. And I'll talk a lot more about that at COMPUTEX in a few days. And then, that gets connected to InfiniBand, which includes the NIC, and the SmartNIC BlueField-3 that we're in full production with and the switches, all of the fiber optics that are optimized end-to-end. These things are running at an incredible line rates. And then beyond that, if you want to connect the smart AI factory -- the smart fact -- this AI factory into your computing fabric, we have a brand new type of Ethernet that we'll be announcing at COMPUTEX, and so -- this whole area of the computing fabric extending connecting all of these GPUs and computing units together, all the way through the networking, through the switches, the software stack is insanely complicated. And so, we're -- I'm delighted you understand it, and but this -- we don't break it out, particularly, because we think of the whole thing as a computing platform as it should be. We sell it to all of the world's data centers as components, so that they can integrate it into whatever style or architecture that they would like and we can still run our software stack. That's the reason why we break it up, it's way more complicated the way that we do it, but it makes it possible for NVIDIA's computing architecture to be integrated into anybody's data center in the world from Cloud of all different kinds to on-prem of all different kinds, all the way out to the edge to 5G and so this way of doing it is really complicated, but it gives us incredible reach. Operator: And our last question will come from Matt Ramsay with TD Cowen. Your line is open. Matt Ramsay: Thank you very much. Congratulations, Jensen, and to the whole team. One of the things I wanted to dig into a little bit is the DGX Cloud offering. You guys have been working on this for some time behind the scenes, where you sell in the hardware to your hyperscale partners and then lease it back for your own business, and the rest of us kind of found out about it publicly a few months ago. And as we look forward over the next number of quarters that Colette discussed to high visibility in the data center business. Maybe you could talk a little bit about the mix you're seeing of hyperscale customers buying for their own first-party internal workloads versus their own sort of third-party, their own customers versus what of that big upside in data center going forward is systems that you're selling in, with potential to support your DGX Cloud offerings and what you've learned since you've launched it about the potential of that business. Thanks. Jensen Huang: Yeah. Thanks Matt. It's -- without being too specific about numbers, but the ideal scenario, the ideal mix is something like 10% NVIDIA DGX Cloud and 90% the CSPs clouds, and the reason -- and our DGX Cloud is -- the NVIDIA stack is the pure NVIDIA stack. It is architected the way we like and achieves the best possible performance. It gives us the ability to partner very deeply with the CSPs to create the highest-performing infrastructure, number one. Number two, it allows us to partner with the CSPs to create markets like, for example, we're partnering with Azure to bring Omniverse cloud to the world's industries. And the world's never had a system like that, the computing stack. Now with all the generative AI stuff and all the 3D stuff and the physics stuff, incredibly large database and really high-speed networks and low-latency networks, that kind of a virtual industrial virtual world has never existed before. And so, we partnered with Microsoft to create Omniverse cloud inside Azure cloud. So, it allows us number two, to create new applications together and develop new markets together. And we go-to-market as one team and we benefit by getting our customers on our computing platform and they benefit by having us in their cloud, number one; but number two, the amount of data and services and security services and all of the amazing things that Azure and GCP and OCI have, they can instantly have access to that through Omniverse cloud. And so it's a huge win-win. And for the customers, the way that NVIDIA's cloud works for these early applications, they can do it anywhere. So one standard stack runs in all the clouds and if they would like to take their software and run it on the CSPs cloud themselves and manage it themselves, we're delighted by that, because NVIDIA AI Enterprise, NVIDIA AI Foundations. And long-term, this is going to take a little longer, but NVIDIA Omniverse will run in the CSPs clouds. Okay. So, our goal really is to drive architecture to partner deeply in creating new markets and the new applications that we're doing and provide our customers with the flexibilities to run in their -- in their everywhere, including on-prem and so, that -- those were the primary reasons for it and it's worked out incredibly. Our partnership with the three CSPs and that we currently have DGX Cloud in and their sales force and marketing teams, their leadership team is really quite spectacular. It works great. Operator: Thank you. I'll now turn it back over to Jensen Huang for closing remarks. Jensen Huang: The computer industry is going through two simultaneous transitions, accelerated computing and generative AI. CPU scaling has slowed, yet computing demand is strong and now with generative AI supercharged. Accelerated computing, a full stack and data center scale approach that NVIDIA pioneered is the best path forward. There is $1 trillion installed in the global data center infrastructure, based on the general-purpose computing method of the last era. Companies are now racing to deploy accelerated computing for the generative AI era. Over the next decade, most of the world's data centers will be accelerated. We are significantly increasing our supply to meet the surging demand. Large language models can learn information encoded in many forms. Guided by large language models, generative AI models can generate amazing content and with models to fine-tune, guardrail, align to guiding principles and ground the facts, generative AI is emerging from labs and is on its way to industrial applications. As we scale with cloud and Internet service providers, we are also building platforms for the world's largest enterprises. Whether within one of our CSP partners or on-prem with Dell Helix, whether on a leading enterprise platform like ServiceNow and Adobe or a bespoke with NVIDIA AI Foundations, we can help enterprises leverage their domain expertise and data to harness generative AI securely and safely. We are ramping a wave of products in the coming quarters, including H100, our Grace and Grace Hopper super chips and our BlueField-3 and Spectrum 4 networking platform. They are all in production. They will help deliver data center scale computing that is also energy-efficient and sustainable computing. Join us next week at COMPUTEX and we'll show you what's next. Thank you. Operator: This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is Emma, and I will be your conference operator today. At this time, I would like to welcome everyone to the NVIDIA's Fourth Quarter Earnings Call. [Operator Instructions]. Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the fourth quarter of fiscal 2023. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss the financial results for the first quarter of fiscal 2024. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, February 22, 2023, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thank you, Simona. Q4 revenue was $6.05 billion, up 2% sequentially, while down 21% year-on-year. Full year revenue was $27 billion, flat from the prior year. Starting with data center. Revenue was $3.62 billion was down 6% sequentially and up 11% year-on-year. Fiscal year revenue was $15 billion and up 41%. Hyperscale customer revenue posted strong sequential growth, though short of our expectations as some cloud service providers paused at the end of the year to recalibrate their build plans. Though we generally see tightening that reflects overall macroeconomic uncertainty, we believe this is a timing issue as the end market demand for GPUs and AI infrastructure is strong. Networking grew but a bit less than our expected on softer demand for general purpose CPU infrastructure. The total data center sequential revenue decline was driven by lower sales in China, which was largely in line with our expectations, reflecting COVID and other domestic issues. With cloud adoption continuing to grow, we are serving an expanding list of fast-growing cloud service providers, including Oracle and GPU specialized CSPs. Revenue growth from CSP customers last year significantly outpaced that of Data Center as a whole as more enterprise customers moved to a cloud-first approach. On a trailing 4-quarter basis, CSP customers drove about 40% of our Data Center revenue. Adoption of our new flagship H100 data center GPU is strong. In just the second quarter of its ramp, H100 revenue was already much higher than that of A100, which declined sequentially. This is a testament of the exceptional performance on the H100, which is as much as 9x faster than the A100 for training and up 30x faster than [inferencing of] (ph) transformer-based large language models. The transformer engine of H100 arrived just in time to serve the development and scale out of inference of large language models. AI adoption is at an inflection point. Open AI's ChatGPT has captured interest worldwide, allowing people to experience AI firsthand and showing what's possible with generative AI. These new types of neural network models can improve productivity in a wide range of tasks, whether generating text like marketing copy, summarizing documents like [indiscernible], creating images for ads or video games or answering customer questions. Generative AI applications will help almost every industry do more faster. Generative large language models with over 100 billion parameters are the most advanced neural networks in today's world. NVIDIA's expertise spans across the AI supercomputers, algorithms, data processing and training methods that can bring these capabilities to enterprise. We look forward to helping customers with generative AI opportunities. In addition to working with every major hyperscale cloud provider, we are engaged with many consumer Internet companies, enterprises and start-ups. The opportunity is significant and driving strong growth in the data center that will accelerate through the year. During the quarter, we made notable announcements in the financial services sector, one of our largest industry verticals. We announced a partnership with Deutsche Bank to accelerate the use of AI and machine learning in financial services. Together, we are developing a range of applications, including virtual customer service agents, speech AI, fraud detection and bank process automation, leveraging NVIDIA's full computing stack, both on-premise and in the cloud, including NVIDIA AI enterprise software. We also announced that NVIDIA captured leading results for AI inference in a key financial services industry benchmark for applications such as asset price discovery. In networking, we see growing demand for our latest generation InfiniBand and HPC optimized Ethernet platforms fueled by AI. Generative AI foundation model sizes continue to grow at exponential rates, driving the need for high-performance networking to scale out multi-node accelerated workloads. Delivering unmatched performance, latency and in-network computing capabilities, InfiniBand is the clear choice for power-efficient cloud scale, generative AI. For smaller scale deployments, NVIDIA is bringing its full accelerated stack expertise and integrating it with the world's most advanced high-performance Ethernet fabrics. In the quarter, InfiniBand led our growth as our Quantum 2 40 gigabit per second platform is off to a great start, driven by demand across cloud, enterprise and supercomputing customers. In Ethernet, our 40 gigabit per second Spectrum 4 networking platform is gaining momentum as customers transition to higher speeds, next-generation adapters and switches. We remain focused on expanding our software and services. We released version 3.0 of NVIDIA AI enterprise with support for more than 50 NVIDIA AI frameworks and pretrained model and new workflows for contact center intelligent virtual assistance, audio transcription and cybersecurity. Upcoming offerings include our NeMo and BioNeMo large language model services, which are currently in early access with customers. Now to Jensen to talk a bit more about our software and cloud business. Jensen Huang: Thanks, Colette. The cumulation of technology breakthroughs has brought AI to an inflection point. Generative AI's versatility and capability has triggered a sense of urgency at enterprises around the world to develop and deploy AI strategies. Yet, the AI supercomputer infrastructure, model algorithms, data processing and training techniques remain an insurmountable obstacle for most. Today, I want to share with you the next level of our business model to help put AI within reach of every enterprise customer. We are partnering with major service -- cloud service providers to offer NVIDIA AI cloud services, offered directly by NVIDIA and through our network of go-to-market partners, and hosted within the world's largest clouds. NVIDIA AI as a service offers enterprises easy access to the world's most advanced AI platform, while remaining close to the storage, networking, security and cloud services offered by the world's most advanced clouds. Customers can engage NVIDIA AI cloud services at the AI supercomputer, acceleration library software or pretrained AI model layers. NVIDIA DGX is an AI supercomputer, and the blueprint of AI factories being built around the world. AI supercomputers are hard and time-consuming to build. Today, we are announcing the NVIDIA DGX Cloud, the fastest and easiest way to have your own DGX AI supercomputer, just open your browser. NVIDIA DGX Cloud is already available through Oracle Cloud Infrastructure and Microsoft Azure, Google GCP and others on the way. At the AI platform software layer, customers can access NVIDIA AI enterprise for training and deploying large language models or other AI workloads. And at the pretrained generative AI model layer, we will be offering NeMo and BioNeMo, customizable AI models, to enterprise customers who want to build proprietary generative AI models and services for their businesses. With our new business model, customers can engage NVIDIA's full scale of AI computing across their private to any public cloud. We will share more details about NVIDIA AI cloud services at our upcoming GTC so be sure to tune in. Now let me turn it back to Colette on gaming. Colette Kress: Thanks, Jensen. Gaming revenue of $1.83 billion was up 16% sequentially and down 46% from a year ago. Fiscal year revenue of $9.07 billion is down 27%. Sequential growth was driven by the strong reception of our 40 Series GeForce RTX GPUs based on the Ada Lovelace architecture. The year-on-year decline reflects the impact of channel inventory correction, which is largely behind us. And demand in the seasonally strong fourth quarter was solid in most regions. While China was somewhat impacted by disruptions related to COVID, we are encouraged by the early signs of recovery in that market. Gamers are responding enthusiastically to the new RTX4090, 4080, 4070 Ti desktop GPUs, with many retail and online outlets quickly selling out of stock. The flagship RTX 4090 has quickly shot up in popularity on Steam to claim the top spot for the AI architecture, reflecting gamers' desire for high-performance graphics. Earlier this month, the first phase of gaming laptops based on the Ada architecture reached retail shelves, delivering NVIDIA's largest-ever generational leap in performance and power efficiency. For the first time, we are bringing enthusiast-class GPU performance to laptops as slim as 14 inches, a fast-growing segment, previously limited to basic tasks and apps. In another first, we are bringing the 90 class GPUs, our most performing models, to laptops, thanks to the power efficiency of our fifth-generation Max-Q technology. All in, RTX 40 Series GPUs will power over [170] (ph) gaming and creator laptops, setting up for a great back-to-schools season. There are now over 400 games and applications supporting NVIDIA's RTX technology for real-time ray tracing and AI-powered graphics. The AI architecture features DLSS 3, our third-generation AI-powered graphics, which massively boosts performance. With the most advanced games, Cyberpunk 2077, recently added DLSS 3 enabling a 3 to 4x boost in frame rate performance at 4K resolution. Our GeForce NOW cloud gaming service continued to expand in multiple dimensions, users, titles and performance. It now has more than 25 million members in over 100 countries. Last month, it enabled RTX 4080 graphics horsepower in the new high-performance ultimate membership tier. Ultimate members can stream at up to 240 frames per second from a cloud with full ray tracing and DLSS 3. And just yesterday, we made an important announcement with Microsoft. We agreed to a 10-year partnership to bring to GeForce NOW Microsoft's lineup of Xbox PC games, which includes blockbusters like Minecraft, Halo and Flight Simulator. And upon the close of Microsoft's Activision acquisition, it will add titles like Call of Duty and Overwatch. Moving to Pro Visualization. Revenue of $226 million was up 13% sequentially and down 65% from a year ago. Fiscal year revenue of $1.54 billion was down 27%. Sequential growth was driven by desktop workstations with strengths in the automotive and manufacturing industrial verticals. Year-on-year decline reflects the impact of the channel inventory correction, which we expect to end in the first half of the year. Interest in NVIDIA's Omniverse continues to build with almost 300,000 downloads so far, 185 connectors to third-party design applications. The latest released Omniverse has a number of features and enhancements, including support for 4K, real-time path tracing, Omniverse Search for AI-powered search through large untagged 3D databases, and Omniverse cloud containers for AWS. Let's move to automotive. Revenue was a record $294 million, up 17% from [indiscernible] and up 135% from a year ago. Sequential growth was driven primarily by AI automotive solutions. New program ramps at both electric vehicle and traditional OEM customers helped drive this growth. Fiscal year revenue of $903 million was up 60%. At CES, we announced a strategic partnership with Foxconn to develop automated and autonomous vehicle platforms. This partnership will provide scale for volume, manufacturing to meet growing demand for the NVIDIA Drive platform. Foxconn will use NVIDIA Drive, Hyperion compute and sensor architecture for its electric vehicles. Foxconn will be a Tier 1 manufacturer producing electronic control units based on NVIDIA Drive Orin for the global . We also reached an important milestone this quarter. The NVIDIA Drive operating system received safety certification from TÜV SÜD, one of the most experienced and rigorous assessment bodies in the automotive industry. With industry-leading performance and functional safety, our platform meets the higher standards required for autonomous transportation. Moving to the rest of the P&L. GAAP gross margin was 63.3%, and non-GAAP gross margin was 66.1%. Fiscal year GAAP gross margin was 56.9%, and non-GAAP gross margin was 59.2%. Year-on-year, Q4 GAAP operating expenses were up 21%, and non-GAAP operating expenses were up 23%, primarily due to the higher compensation and data center infrastructure expenses. Sequentially, GAAP operating expenses were flat, and non-GAAP operating expenses were down 1%. We plan to keep them relatively flat at this level over the coming quarters. Full year GAAP operating expenses were up 50%, and non-GAAP operating expenses were up 31%. We returned $1.15 billion to shareholders in the form of share repurchases and cash dividends. At the end of Q4, we had approximately %7 billion remaining under our share repurchase authorization through December 2023. Let me look to the outlook for the first quarter of fiscal '24. We expect sequential growth to be driven by each of our 4 major market platforms led by strong growth in data center and gaming. Revenue is expected to be $6.5 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 64.1% and 66.5%, respectively, plus or minus 50 basis points. GAAP operating expenses are expected to be approximately $2.53 billion. Non-GAAP operating expenses are expected to be approximately $1.78 billion. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $50 million, excluding gains and losses of nonaffiliated divestments. GAAP and non-GAAP tax rates are expected to be 13%, plus or minus 1%, excluding any discrete items. Capital expenditures are expected to be approximately $350 million to $400 million for the first quarter and in the range of $1.1 billion to $1.3 billion for the full fiscal year 2024. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight upcoming events for the financial community. We will be attending the Morgan Stanley Technology Conference on March 6 in San Francisco and the Cowen Healthcare Conference on March 7 in Boston. We will also host GTC virtually with Jensen's keynote kicking off on March 21. Our earnings call to discuss the results of our first quarter of fiscal year '24 is scheduled for Wednesday, May 24. Now we will open up the call for questions. Operator, would you please poll for questions? Operator: [Operator Instructions]. Your first question comes from the line of Aaron Rakers with Wells Fargo. Aaron Rakers: Clearly, on this call, a key focal point is going to be the monetization effect of your software and cloud strategy. I think as we look at it, I think, straight up, the enterprise AI software suite, I think, is priced at around $6,000 per CPU socket. I think you've got pricing metrics a little bit higher for the cloud consumption model. I'm just curious, Colette, how do we start to think about that monetization contribution to the company's business model over the next couple of quarters relative to, I think, in the past, you've talked like a couple of hundred million or so? Just curious if you can unpack that a little bit. Colette Kress: So I'll start and turn it over to Jensen to talk more because I believe this will be a great topic and discussion also at our GTC. Our plans in terms of software, we continue to see growth even in our Q4 results, we're making quite good progress in both working with our partners, onboarding more partners and increasing our software. You are correct. We've talked about our software revenues being in the hundreds of millions. And we're getting even stronger each day as Q4 was probably a record level in terms of our software levels. But there's more to unpack in terms of there, and I'm going to turn it to Jensen. Jensen Huang: Yes, first of all, taking a step back, NVIDIA AI is essentially the operating system of AI systems today. It starts from data processing to learning, training, to validations, to inference. And so this body of software is completely accelerated. It runs in every cloud. It runs on-prem. And it supports every framework, every model that we know of, and it's accelerated everywhere. By using NVIDIA AI, your entire machine learning operations is more efficient, and it is more cost effective. You save money by using accelerated software. Our announcement today of putting NVIDIA's infrastructure and have it be hosted from within the world's leading cloud service providers accelerates the enterprise's ability to utilize NVIDIA AI enterprise. It accelerates people's adoption of this machine learning pipeline, which is not for the faint of heart. It is a very extensive body of software. It is not deployed in enterprises broadly, but we believe that by hosting everything in the cloud, from the infrastructure through the operating system software, all the way through pretrained models, we can accelerate the adoption of generative AI in enterprises. And so we're excited about this new extended part of our business model. We really believe that it will accelerate the adoption of software. Operator: Your next question comes from the line of Vivek Arya with Bank of America. Vivek Arya: Just wanted to clarify, Colette, if you meant data center could grow on a year-on-year basis also in Q1? And then Jensen, my main question kind of relate to 2 small related ones. The computing intensity for generative AI, if it is very high, does it limit the market size to just a handful of hyperscalers? And on the other extreme, if the market gets very large, then doesn't it attract more competition for NVIDIA from cloud ASICs or other accelerator options that are out there in the market? Colette Kress: Thanks for the question. First, talking about our data center guidance that we provided for Q1. We do expect a sequential growth in terms of our data center, strong sequential growth. And we are also expecting a growth year-over-year for our data center. We actually expect a great year with our year-over-year growth in data center probably accelerating past Q1. Jensen Huang: Large language models are called large because they are quite large. However, remember that we've accelerated and advanced AI processing by a million x over the last decade. Moore's Law, in its best days, would have delivered 100x in a decade. By coming up with new processors, new systems, new interconnects, new frameworks and algorithms and working with data scientists, AI researchers on new models, across that entire span, we've made large language model processing a million times faster, a million times faster. What would have taken a couple of months in the beginning, now it happens in about 10 days. And of course, you still need a large infrastructure. And even the large infrastructure, we're introducing Hopper, which, with its transformer engine, it's new NVLink switches and its new InfiniBand 400 gigabits per second data rates, we're able to take another leap in the processing of large language models. And so I think the -- by putting NVIDIA's DGX supercomputers into the cloud with NVIDIA DGX cloud, we're going to democratize the access of this infrastructure, and with accelerated training capabilities, really make this technology and this capability quite accessible. So that's one thought. The second is the number of large language models or foundation models that have to be developed is quite large. Different countries with different cultures and its body of knowledge are different. Different fields, different domains, whether it's imaging or its biology or its physics, each one of them need their own domain of foundation models. With large language models, of course, we now have a prior that could be used to accelerate the development of all these other fields, which is really quite exciting. The other thing to remember is that the number of companies in the world have their own proprietary data. The most valuable data in the world are proprietary. And they belong to the company. It's inside their company. It will never leave the company. And that body of data will also be harnessed to train new AI models for the very first time. And so we -- our strategy and our goal is to put the DGX infrastructure in the cloud so that we can make this capability available to every enterprise, every company in the world who would like to create proprietary data and so -- proprietary models. The second thing about competition. We've had competition for a long time. Our approach, our computing architecture, as you know, is quite different on several dimensions. Number one, it is universal, meaning you could use it for training, you can use it for inference, you can use it for models of all different types. It supports every framework. It supports every cloud. It's everywhere. It's cloud to private cloud, cloud to on-prem. It's all the way out to the edge. It could be an autonomous system. This one architecture allows developers to develop their AI models and deploy it everywhere. The second very large idea is that no AI in itself is an application. There's a preprocessing part of it and a post-processing part of it to turn it into an application or service. Most people don't talk about the pre and post processing because it's maybe not as sexy and not as interesting. However, it turns out that preprocessing and post-processing oftentimes consumes half or 2/3 of the overall workload. And so by accelerating the entire end-to-end pipeline, from preprocessing, from data ingestion, data processing, all the way to the preprocessing all the way to post processing, we're able to accelerate the entire pipeline versus just accelerating half of the pipeline. The limit to speed up, even if you're instantly passed if you only accelerate half of the workload, is twice as fast. Whereas if you accelerate the entire workload, you could accelerate the workload maybe 10, 20, 50x faster, which is the reason why when you hear about NVIDIA accelerating applications, you routinely hear 10x, 20x, 50x speed up. And the reason for that is because we accelerate things end to end, not just the deep learning part of it, but using CUDA to accelerate everything from end to end. And so I think the universality of our computing -- accelerated computing platform, the fact that we're in every cloud, the fact that we're from cloud to edge, makes our architecture really quite accessible and very differentiated in this way. And most importantly, to all the service providers, because of the utilization is so high, because you can use it to accelerate the end-to-end workload and get such a good throughput, our architecture is the lowest operating cost. It's not -- the comparison is not even close. So -- anyhow those are the 2 answers. Operator: Your next question comes from the line of C.J. Muse with Evercore. Christopher Muse: I guess, Jensen, you talked about ChatGPT as an inflection point kind of like the iPhone. And so curious, part A, how have your conversations evolved post ChatGPT with hyperscale and large-scale enterprises? And then secondly, as you think about Hopper with the transformative engine and Grace with high-bandwidth memory, how have you kind of your outlook for growth for those 2 product cycles evolved in the last few months? Jensen Huang: ChatGPT is a wonderful piece of work, and the team did a great job, OpenAI did a great job with it. They stuck with it. And the accumulation of all of the breakthroughs led to a service with a model inside that surprised everybody with its versatility and its capability. What people were surprised by, and this is in our -- and close within the industry is well understood. But the surprising capability of a single AI model that can perform tasks and skills that it was never trained to do. And for this language model to not just speak English, or can translate, of course, but not just speak human language, it can be prompted in human language, but output Python, output Cobalt, a language that very few people even remember, output Python for Blender, a 3D program. So it's a program that writes a program for another program. We now realize -- the world now realizes that maybe human language is a perfectly good computer programming language, and that we've democratized computer programming for everyone, almost anyone who could explain in human language a particular task to be performed. This new computer -- when I say new era of computing, this new computing platform, this new computer could take whatever your prompt is, whatever your human-explained request is, and translate it to a sequence of instructions that you process it directly, or it waits for you to decide whether you want to process it or not. And so this type of computer is utterly revolutionary in its application because it's democratized programming to so many people really has excited enterprises all over the world. Every single CSP, every single Internet service provider, and they're, frankly, every single software company, because of what I just explained, that this is an AI model that can write a program for any program. Because of that reason, everybody who develops software is either alerted or shocked into alert or actively working on something that is like ChatGPT to be integrated into their application or integrated into their service. And so this is, as you can imagine, utterly worldwide. The activity around the AI infrastructure that we build Hopper and the activity around inferencing using Hopper and Ampere to inference large language models, has just gone through the roof in the last 60 days. And so there's no question that whatever our views are of this year as we enter the year has been fairly, dramatically changed as a result of the last 60, 90 days. Operator: Your next question comes from the line of Matt Ramsay with Cowen & Company. Matthew Ramsay: Jensen, I wanted to ask a couple of questions on the DGX Cloud. And I guess, we're all talking about the drivers of the services and the compute that you're going to host on top of these services with the different hyperscalers. But I think we've been kind of watching and wondering when your data center business might transition to more of a systems level business, meaning pairing and [indiscernible] InfiniBand with your Hopper product, with your Grace product and selling things more on a systems level. I wonder if you could step back, over the next 2 or 3 years, how do you think the mix of business in your data center segment evolves from maybe selling cards to systems and software? And what can that mean for the margins of that business over time? Jensen Huang: Yes, I appreciate the question. First of all, as you know, our Data Center business is a GPU business only in the context of a conceptual GPU because what we actually sell to the cloud service providers is a panel, a fairly large computing panel of 8 Hoppers or 8 Amperes that's connected with NVLink switches that are connected with NVLink. And so this board represents essentially 1 GPU. It's 8 chips connected together into 1 GPU with a very high-speed chip-to-chip interconnect. And so we've been working on, if you will, multi-die computers for quite some time. And that is 1 GPU. So when we think about a GPU, we actually think about an HGX GPU, and that's 8 GPUs. We're going to continue to do that. And the thing that the cloud service providers are really excited about is by hosting our infrastructure for NVIDIA to offer because we have so many companies that we work directly with. We're working directly with 10,000 AI start-ups around the world, with enterprises in every industry. And all of those relationships today would really love to be able to deploy both into the cloud at least or into the cloud and on-prem and oftentimes multi-cloud. And so by having NVIDIA DGX and NVIDIA's infrastructure are full stack in their cloud, we're effectively attracting customers to the CSPs. This is a very, very exciting model for them. And they welcomed us with open arms. And we're going to be the best AI salespeople for the world's clouds. And for the customers, they now have an instantaneous infrastructure that is the most advanced. They have a team of people who are extremely good from the infrastructure to the acceleration software, the NVIDIA AI open operating system, all the way up to AI models. Within 1 entity, they have access to expertise across that entire span. And so this is a great model for customers. It's a great model for CSPs. And it's a great model for us. It lets us really run like the wind. As much as we will continue and continue to advance DGX AI supercomputers, it does take time to build AI supercomputers on-prem. It's hard no matter how you look at it. It takes time no matter how you look at it. And so now we have the ability to really prefetch a lot of that and get customers up and running as fast as possible. Operator: Your next question comes from the line of Timothy Arcuri with UBS. Timothy Arcuri: Jensen, I had a question about what this all does to your TAM. Most of the focus right now is on text, but obviously, there are companies doing a lot of training on video and music. They're working on models there. And it seems like somebody who's training these big models has maybe, on the high end, at least 10,000 GPUs in the cloud that they've contracted and maybe tens of thousands of more to inference a widely deployed model. So it seems like the incremental TAM is easily in the several hundred thousands of GPUs and easily in the tens of billions of dollars. But I'm kind of wondering what this does to the TAM numbers you gave last year. I think you said $300 billion hardware TAM and $300 billion software TAM. So how do you kind of think about what the new TAM would be? Jensen Huang: I think those numbers are really good anchor still. The difference is because of the, if you will, incredible capabilities and versatility of generative AI and all of the converging breakthroughs that happened towards the middle and the end of last year, we're probably going to arrive at that TAM sooner than later. There's no question that this is a very big moment for the computer industry. Every single platform change, every inflection point in the way that people develop computers happened because it was easier to use, easier to program and more accessible. This happened with the PC revolution. This happened with the Internet revolution. This happened with mobile cloud. Remember, mobile cloud, because of the iPhone and the App Store, 5 million applications and counting emerged. There weren't 5 million mainframe applications. There weren't 5 million workstation applications. There weren't 5 million PC applications. And because it was so easy to develop and deploy amazing applications part cloud, part on the mobile device and so easy to distribute because of app stores, the same exact thing is now happening to AI. In no computing era did 1 computing platform, ChatGPT, reached 150 million people in 60, 90 days. I mean, this is quite an extraordinary thing. And people are using it to create all kinds of things. And so I think that what you're seeing now is just a torrent of new companies and new applications that are emerging. There's no question this is, in every way, a new computing era. And so I think this -- the TAM that we explained and expressed, it really is even more realizable today and sooner than before. Operator: Your next question comes from the line of Stacy Rasgon with Bernstein. Stacy Rasgon: I have a clarification and then a question both for Colette. The clarification, you said H-100 revenue's higher than A100. Was that an overall statement? Or was that at the same point in time like after 2 quarters of shipments? And then for my actual question. I wanted to ask about auto, specifically the Mercedes opportunity. The Mercedes had an event today, and they were talking about software revenues for their MB Drive that could be single digit or low billion euros by mid-decade and mid billion euros by the end of the decade. And I know you guys were supposedly splitting the software revenues 50-50. Is that kind of the order of magnitude of software revenues from the Mercedes deal that you guys are thinking of and over that similar time frame? Is that how we should be modeling that? Colette Kress: Great. Thanks, Stacy, for the question. Let me first start with your question you had about H-100 and A100. We began initial shipments of H-100 back in Q3. It was a great start. Many of them began that process many quarters ago. And this was a time for us to get production level to them in Q3. So Q4 was an important time for us to see a great ramp of H-100 that we saw. What that means is our H-100 was the focus of many of our CSPs within Q4, and they were all wanting to get both get up and running in cloud instances. And so we actually saw less of A100 in Q4 of what we saw in H-100 at a larger amount. We tend to continue to sell both architectures going forward, but just in Q4, it was a strong quarter for Your additional questions that you had on Mercedes Benz. I'm very pleased with the joint connection that we have with them and the work. We've been working very diligently about getting ready to come to market. And you're right. They did talk about the software opportunity. They talked about their software opportunity in 2 phases, about what they can do with Drive as well as what they can also do with Connect. They extended out to a position of probably about 10 years looking at the opportunity that they see in front of us. So it aligns with what our thoughts are with a long-term partner of that and sharing that revenue over time. Jensen Huang: One of the things that, if I could add, Stacy, to say something about the wisdom of what Mercedes is doing. This is the only large luxury brand that has, across the board, from every -- from the entry all the way to the highest end of their luxury cars, to install every single one of them with a rich sensor set, every single one of them with an AI supercomputer, so that every future car in the Mercedes fleet will contribute to an installed base that could be upgradable and forever renewed for customers going forward. If you could just imagine what it looks like if the entire Mercedes fleet that is on the road today were completely programmable, that you can OTA, it would represent tens of millions of Mercedeses that would represent revenue-generating opportunity. And that's the vision that Ola has. And what they're building for, I think, it's going to be extraordinary. The large installed base of luxury cars that will continue to renew with -- for customers' benefits and also for revenue-generating benefits. Operator: Your next question comes from the line of Mark Lipacis with Jefferies. Mark Lipacis: I think for you, Jensen, it seems like every year a new workload comes out and drives demand for your process or your ecosystem cycles. And if I think back facial recognition and then recommendation engines, natural language processing, Omniverse and now generative AI engines, can you share with us your view? Is this what we should expect going forward, like a brand-new workload that drives demand to the next level for your products? And the reason I ask is because I found it interesting your comments in your script where you mentioned that your kind of view about the demand that generative AI is going to drive for your products and now services is -- seems to be a lot, better than what you thought just over the last 90 days. So -- and to the extent that there's new workloads that you're working on or new applications that can drive next levels of demand, would you care to share with us a little bit of what you think could drive it past what you're seeing today? Jensen Huang: Yes, Mark, I really appreciate the question. First of all, I have new applications that you don't know about and new workloads that we've never shared that I would like to share with you at GTC. And so that's my hook to come to GTC, and I think you're going to be very surprised and quite delighted by the applications that we're going to talk about. Now there's a reason why it is the case that you're constantly hearing about new applications. The reason for that is, number one, NVIDIA is a multi-domain accelerated computing platform. It is not completely general purpose like a CPU because a CPU is 95%, 98% control functions and only 2% mathematics, which makes it completely flexible. We're not that way. We're an accelerated computing platform that works with the CPU that offloads the really heavy computing units, things that could be highly, highly paralyzed to offload them. But we're multi-domain. We could do particle systems. We could do fluids. We could do neurons. And we can do computer graphics. We can do . There are all kinds of different applications that we can accelerate, number one. Number two, our installed base is so large. This is the only accelerated computing platform, the only platform. Literally, the only one that is architecturally compatible across every single cloud from PCs to workstations, gamers to cars to on-prem. Every single computer is architecturally compatible, which means that a developer who developed something special would seek out our platform because they like the reach. They like the universal reach. They like the acceleration, number one. They like the ecosystem of programming tools and the ease of using it and the fact that they have so many people they can reach out to, to help them. There are millions of CUDA experts around the world, software all accelerated, tool all accelerated. And then very importantly, they like the reach. They like the fact that you can see -- they can reach so many users after they develop the software. And it is the reason why we just keep attracting new applications. And then finally, this is a very important point. Remember, the rate of CPU computing advance has slowed tremendously. And whereas back in the first 30 years of my career, at 10x in performance at about the same power every 5 years and then 10x every 5 years. That rate of continued advance has slowed. At a time when people still have really, really urging applications that they would like to bring to the world, and they can't afford to do that with the power keep going up. Everybody needs to be sustainable. You can't continue to consume power. By accelerating it, we can decrease the amount of power you use for any workload. And so all of these multitude of reasons is really driving people to use accelerated computing, and we keep discovering new exciting applications. Operator: Your next question comes from the line of Atif Malik with Citi. Atif Malik: Colette, I have a question on data center. You saw some weakness on build plan in the January quarter, but you're guiding to year-over-year acceleration in April and through the year. So if you can just rank order for us the confidence in the acceleration. Is that based on your H-100 ramp or generative AI sales coming through or the new AI services model? And also, if you can talk about what you're seeing on the enterprise vertical. Colette Kress: Sure. Thanks for the question. When we think about our growth, yes, we're going to grow sequentially in Q1 and do expect year-over-year growth in Q1 as well. It will likely accelerate there going forward. So what do we see as the drivers of that? Yes, we have multiple product cycles coming to market. We have H-100 in market now. We are continuing with our new launches as well that are sometimes fueled with our GPU computing with our networking. And then we have grades coming likely in the second half of the year. Additionally, generative AI, it's sparked interest definitely among our customers, whether those be CSPs, whether those be enterprises, one of those be start-ups. We expect that to be a part of our revenue growth this year. And then lastly, let's just not forget that given the end of Moore's Law, there's an error here of focusing on AI, focusing on accelerated continuing. So as the economy improves, this is probably very important to the enterprises and it can be fueled by the existence of cloud first for the enterprises as they [indiscernible]. I'm going to turn it to Jensen to see if has any additional things he'd like to add. Jensen Huang: No, you did great. That was great. Operator: Your last question today comes from the line of Joseph Moore with Morgan Stanley. Joseph Moore: Jensen, you talked about the sort of 1 million times improvement in your ability to train these models over the last decade. Can you give us some insight into what that looks like in the next few years and to the extent that some of your customers with these large language models are talking about 100x the complexity over that kind of time frame. I know Hopper is 6x better transformer performance. But what can you do to scale that up? And how much of that just reflects that it's going to be a much larger hardware expense down the road? Jensen Huang: First, I'll start backwards. I believe the number of AI infrastructures are going to grow all over the world. And the reason for that is AI, the production of intelligence, is going to be manufacturing. There was a time when people manufacture just physical goods. In the future, there will be -- almost every company will manufacture soft goods. It just happens to be in the form of intelligence. Data comes in. That data center does exactly 1 thing and 1 thing only. It cranks on that data and it produces a new updated model. Where raw material comes in, a building or an infrastructure cranks on it, and something refined or improved comes out that is of great value, that's called the factory. And so I expect to see AI factories all over the world. Some of it will be hosted in cloud. Some of it will be on-prem. There will be some that are large, and there are some that will be mega large, and then there'll be some that are smaller. And so I fully expect that to happen, number one. Number two. Over the course of the next 10 years, I hope through new chips, new interconnects, new systems, new operating systems, new distributed computing algorithms and new AI algorithms and working with developers coming up with new models, I believe we're going to accelerate AI by another million x. There's a lot of ways for us to do that. And that's one of the reasons why NVIDIA is not just a chip company because the problem we're trying to solve is just too complex. You have to think across the entire stack all the way from the chip, all the way into the data center across the network through the software. And in the mind of 1 single company, we can think across that entire stack. And it's really quite a great playground for computer scientists for that reason because we can innovate across that entire stack. So my expectation is that you're going to see really gigantic breakthroughs in AI models in the next company, the AI platforms in the coming decade. But simultaneously, because of the incredible growth and adoption of this, you're going to see these AI factories everywhere. Operator: This concludes our Q&A session. I will now turn the call back over to Jensen Huang for closing remarks. Jensen Huang: Thank you. The accumulation of breakthroughs from transformers, large language model and generative AI has elevated the capability and versatility of AI to a remarkable level. A new computing platform has emerged. New companies, new applications and new solutions to long-standing challenges are being invented at an astounding rate. Enterprises in just about every industry are activating to apply generative AI to reimagine their products and businesses. The level of activity around AI, which was already high, has accelerated significantly. This is the moment we've been working towards for over a decade. And we are ready. Our Hopper AI supercomputer with the new transformer engine and Quantum InfiniBand fabric is in full production, and CSPs are racing to open their Hopper cloud services. As we work to meet the strong demand for our GPUs, we look forward to accelerating growth through the year. Don't miss the upcoming GTC. We have much to tell you about new chips, systems and software, new CUDA applications and customers, new ecosystem partners and a lot more on NVIDIA AI and Omniverse. This will be our best GTC yet. See you there. Operator: This concludes today's conference. You may now disconnect.

Operator: Good afternoon. My name is Emma, and I will be your conference operator today. At this time, I would like to welcome everyone to the NVIDIA's third quarter earnings call. [Operator instructions] Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the third quarter of fiscal 2023. With me today from NVIDIA are Jen-Hsun Huang, president and chief executive officer; and Colette Kress, executive vice president and chief financial officer. I'd like to remind you that our call is being webcast live on NVIDIA's investor relations website. The webcast will be available for replay until the conference call to discuss our financial results for the fourth quarter and fiscal 2023. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, November 16, 2022, and based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. Q3 revenue was $5.93 billion, down 12% sequentially and down 17% year on year. We delivered record data center and automotive revenue. while our gaming and pro visualization platforms declined as we work through channel inventory corrections and challenging external conditions. Starting with data center. Revenue of $3.83 billion was up 1% sequentially and 31% year-on-year. This reflects very solid performance in the face of macroeconomic challenges new export controls and lingering supply chain disruptions. Year-on-year growth was driven primarily by leading U.S. cloud providers and a broadening set of consumer Internet companies for workloads such as large language models, recommendation systems and generative AI. As the number and scale of public cloud computing and Internet service companies deploying NVIDIA AI grows our traditional hyperscale definition will need to be expanded to convey the different end market use cases. We will align our data center customer commentary going forward accordingly. Other vertical industries, such as automotive and energy, also contributed to growth with key workloads relating to autonomous driving, high-performance computing, simulations and analytics. During the quarter, the U.S. government announced new restrictions impacting exports of our A100 and H-100 based products to China, and any product destined for certain systems or entities in China. These restrictions impacted third quarter revenue, largely offset by sales of alternative products into China. That said, demand in China more broadly remains soft, and we expect that to continue in the current quarter. We started shipping our flagship 100 data center GPU based on the new hopper architecture in Q3. A100-based systems are available starting this month from leading server makers including Dell, Hewlett Packard Enterprise, Lenovo and SuperMicro. Early next year, the first H-100 based cloud instances will be available on Amazon Web Services, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure. A100 delivered the highest performance and workload versatility for both AI training and inference in the latest MLPerf industry benchmarks. H-100 also delivers incredible value compared to the previous generation for equivalent AI performance it offers three x lower total cost of ownership while using five x fewer server nodes and 3.5 x less energy. Earlier today, we announced a multiyear collaboration with Microsoft to build an advanced cloud-based AI supercomputer to help enterprises train, deploy and scale AI including large state-of-the-art models. MacBook Azure will incorporate our complete AI stack, adding tens and thousands of A100 and A100 GPUs. Quantum 2 400 gigabit per second InfiniBand networking and the NVIDIA AI enterprise software suite to its platform. Oracle and NVIDIA are also working together to offer AI training and inference at scale to thousands of enterprises. This includes bringing to Oracle Cloud infrastructure, the full NVIDIA accelerated computing stack and adding tens of thousands of NVIDIA GPUs, including the A100 and H-100. Cloud-based high-performance in the company, new scale is adopting NVIDIA AI enterprise and other software to address the industrial scientific communities, rising demand for AI in the cloud. NVIDIA AI will bring new capability to rescale high-performance computing as a service offerings, which include simulation and engineering software used across industries. Networking posted strong growth driven by hyperscale customers and easing supply constraints. -- our new Quantum 240 gigabit per second InfiniBand and Spectrum Ethernet networking platforms are building momentum. We achieved an important milestone this quarter with VMware. And whose leading server virtualization platform, vSphere, has been rearchitected over the last two years to run on DPUs and now supports our BlueField DPUs. Our joint enterprise AI platform is available first on Dell PowerEdge servers. The BlueField DPU design win pipeline is growing and the number of infrastructure softer partners is expanding, including Arista, Check Point, Juniper, [Inaudible] Networks and Red Hot. The latest top 500 list of supercomputers released this week at Supercomputing '22 and has the highest ever number of NVIDIA-powered systems, including 72% of the total and 90% of new systems on the list. Moreover, NVIDIA powers 23 of the top 30 of the Green 500 list, demonstrating the energy efficiency of accelerated computing. The No. 1 most energy-efficient system is the Flat Iron Institute Henry, which is the first top 500 system featuring our H-100 GPUs. At GTC, we announced the NVIDIA Omniverse Computing System, or OVS, reference designs featuring the new L4 GPU based on the ADA Lovelace architecture. These systems are designed to build and operate 3D virtual world using NVIDIA Omniverse enterprise. NVIDIA OBX systems will be available from Inspur, Lenovo and Super Micro by early 2023. We Lockheed Martin and Jaguar Land Rover will be among the first customers to receive OVS systems. We are further expanding our AI software and services offerings with NVIDIA and Bio Nemo large language model services, which are both entering early access this month. These enable developers to easily adopt large language models and deploy customized AI applications for content generation, tech summarization, chatbox, co-development, protein structure and biomolecular property predictions. Moving to gaming. Revenue of $1.57 billion was down 23% sequentially and down 51% from a year ago, reflecting lower sell-in to partners to help align channel inventory levels with current demand expectations. We believe Channel inventories are on track to approach normal levels as we exit Q4. Sell-through for our gaming products was relatively solid in the Americas and EMEA and but softer in Asia Pac as macroeconomic conditions and covered lockdowns in China continued to weigh on consumer demand. Our new Ada Lovelace GPU architecture had an exceptional launch. The first ADA GPU, the GeForce RTX 4090 became available in mid-October and a tremendous amount and positive feedback from the gaming community. We sold out quickly in many locations and are working hard to keep up with demand. The next member of the ATA family, RTX 4080 is available today. The RTX 40 Series GPUs features DLSS 3, the neuro rendering technology that uses AI to generate entire frames for faster game play. Our third-generation RTX technology has raised the bar for computer graphics and help supercharge gaming. For example, the 15-year old classic game portal, now reimagined with full ray tracing and DLSS 3 has made it on Steam's top 100 most wish-listed gains. The total number of RTX games and applications now exceeds 350. There is tremendous energy in the gaming community that we believe will continue to fuel strong fundamentals over the long term. The number of simultaneous users on steam just hit a record of $30 million, surpassing the prior peak of $28 million in January. Activision's Call of Duty Modern Warfare 2 set a record for the franchise with more than $800 million in opening weekend sales. topping the combined box office openings of movie blockbusters, TopGun Maverick and Dr. Strains in the Multiverse of [Inaudible]. And this month's League of Legends World Championship in San Francisco sold out minutes with 18,000 esports fans packed the arena where the Golden State Warriors play. We continue to expand the GeForce NOW cloud gaming service. In Q3, we added over 85 games to the library, bringing the total to over 1,400. We also launched GeForce now on the new gaming devices, including Logitech, Cloud handheld, cloud gaming Chromebooks and Razor 5G Edge. Moving to Probi Revenue of $200 million was down 60% sequentially and down 65% from a year ago, reflecting lower sell-in to partners to help align channel inventory levels with the current demand expectations. These dynamics are expected to continue in Q4. Despite near-term challenges, we believe our long-term opportunity remains intact, fueled by AI simulation, computationally intensive design and engineering workloads. At GTC, we announced NVIDIA Omniverse Cloud Services, our first software and infrastructure as a service offering, enabling artists, developers and enterprise teams to design, publish and operate metaverse applications from anywhere on any device. Omniverse Cloud Services runs on Omniverse cloud computer, a computing system comprised of NVIDIA OBX for graphics and physics simulation. NVIDIA HDX for AI workloads and the NVIDIA graphics delivery network, a global scale, distributed data center network for delivering low-latency metaverse graphics on the edge. Leaders in some of the world's largest industries continue to adopt Omniverse. Home improvement retailer, Lowe's is using it to help design, build and operate digital twins for their stores. Charter Communications and advanced analytics company, heavy AI are creating Omniverse power digital twins to optimize Charter's wireless network. In Deutsche Bahn, operator of German National Railway is using Omniverse to create digital twins of its rail network and train AI models to monitor the network, increasing safety and reliability. Moving to automotive. Revenue of $251 million, increased 14% sequentially and 86% from a year ago. Growth was driven by an increase in AI automotive solutions as our customers drive or on-based production ramp, continue to scale. Automotive has great momentum and is on its way to be our next multibillion-dollar platform. Global cars unveiled the all-new flagship Volvo EX90 SUV powered by the NVIDIA Drive platform. This is the first model to use Volvo's software-defined architecture with a centralized core computer containing both drive Orin and DRIVEXaviar, along with 30 sensors. Other recently announced design wins and new model introductions include ton, auto, Neo, Polystar and [Inaudible]. At GTC, we also announced that NVIDIA Drive Super Chip, the successor to Orin in our automotive SoC road map, drive [Inaudible] delivers up to 2,000 tariff lots of performance and leverages technologies introduced in our Grace Hopper and ADA architectures. It is capable of running both the automated drive and in-vehicle infotainment systems. Simultaneously offering a LIFA performance while reducing cost and energy consumption. Driver will be available for automakers 25 models with Geely owned automaker, Zika as the first announced customer. Moving to the rest of the P&L. GAAP gross margin was 53.6% and and non-GAAP gross margin was 56.1%. Gross margins reflect $702 million in inventory charges largely related to lower data center demand in China, partially offset by a warranty benefit of approximately $70 million. Year-on-year, GAAP operating expenses were up 31%, and non-GAAP operating expenses were up 30%, primarily due to higher compensation expenses related to headcount growth and salary increases and higher data center infrastructure expenses. Sequentially, both GAAP and non-GAAP operating expense growth was in the single-digit percent, and we plan to keep it relatively flat at these levels over the coming quarters. We returned $3.75 billion to shareholders in the form of share repurchases and cash dividends. At the end of Q3, we had approximately $8.3 billion remaining under our share repurchase authorization through December 23. Let me turn to the outlook for the fourth quarter of fiscal 2023. We expect our data center revenue to reflect early production shipments of the A100, offset by continued softness in China. In gaming, we expect to resume sequential growth with our revenue still below end demand as we continue to work through the channel inventory correction. And in automotive, we expect the continued ramp of our Oren design wins. All in, we expect modest sequential growth driven by automotive, gaming and data center. Revenue is expected to be $6 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be $63.2 million and 66%, respectively, plus or minus 50 basis points. GAAP operating expenses are expected to be approximately $2.56 billion. Non-GAAP operating expenses are expected to be approximately $1.78 billion. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $40 million, excluding gains and losses on nonaffiliated investments. GAAP and non-GAAP tax rates are expected to be 9%, plus or minus 1%, excluding any discrete items. Capital expenditures are expected to be approximately $500 million to $550 million. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight upcoming events for the financial community. We'll be attending the Credit Suisse conference in Phoenix on November 30. The rate Virtual Tech Conference on December 5 and and the JPMorgan Forum on January 5 in Las Vegas. Our earnings call to discuss the results of our fourth quarter and fiscal 2023 are scheduled for Wednesday, February 22. We will now open the call for questions. Operator, could you please poll for questions? Operator: [Operator instructions] Your first question comes from the line of Vivek Arya with Bank of America Securities. Your line is now open. Vivek Arya: Thanks for taking my question. Colette, just wanted to clarify first, I think last quarter, you gave us a sell-through rate for your gaming business at about $2.5 billion a quarter. I think you said China is somewhat weaker. So I was hoping you could update us on what that sell-through rate is right now for gaming. And then, Jen-Hsun, the question for you. A lot of concerns about large hyperscalers cutting their spending and pointing to a slowdown. So if, let's say, U.S. cloud capex is flat or slightly down next year, do you think your business can still grow in the data center and why? Colette Kress: Yes. Thanks for the question. Let me first start with the sell-through on our gaming business. we had indicated, if you put two quarters together, we would see approximately $5 billion in normalized sell-through for our business. Now, during the quarter, sell-through in Q3 three was relatively solid. We've indicated that although China lockdowns continue to channel -- excuse me, challenge our overall China business. It was still relatively solid. Notebook sell-through was also quite solid. And desktop, a bit softer, particularly in that China and Asia areas. We expect though stronger end demand, though, as we enter into Q4, driven by the upcoming holidays, as well as the continuation of the ADA adoption. Jen-Hsun Huang: Vivek, our data center business is indexed to two fundamental dynamics. The first has to do with general purpose computing no longer scaling. And so, acceleration is necessary to achieve the necessary level of cost efficiency scale and energy efficiency scale so that we can continue to increase workloads while saving money and saving power. Accelerated computing is recognized generally as the path forward as general purpose computing slows. The second dynamic is AI. And we're seeing surging demand in some very important sectors of AIs in important breakthroughs in AI. One is called deep recommender systems, which is quite essential now to the best content or item or product to recommend to somebody who's using a device that is like a selfie or interacting with a computer just using voice. You need to really understand the nature, the context of the person making the request and make the appropriate recommendation to them. The second has to do with large language models. This is -- this started several years ago with the invention of the transformer, which led to Bert, which led to GP3, which led to a whole bunch of other models now associated with that. We now have the ability to learn representations of languages of all kinds. It could be human language. It could be the language of biology. It could be the language of chemistry. And recently, I just saw a breakthrough called Jeans LM, we just one of the first example of learning the language of human genomes. The third has to do with generative AI. You know that the first 10 years, we've dedicated ourselves to perception AI. But the goal of perception, of course, is to understand context. But the ultimate goal of AI is to make a contribution to create something to generate product. And this is now the beginning of the era of generative AI. You probably see it all over the place, whether they're generating images or generating videos or generating text of all kinds and the ability to augment our performance to enhance our performance to make productivity enhanced to reduce cost and improve whatever we do with whatever we have to work with, productivity is really more important than ever. And so, you could see that our company is indexed to two things, both of which are more important than ever, which is power efficiency, cost efficiency and then, of course, productivity. And these things are more important than ever. And my expectation is that we're seeing all the strong demand and surging demand for AI and for niche reasons. Operator: Your next question comes from the line of C.J. Muse with Evercore. Your line is now open. C.J. Muse: Yeah, Good afternoon and thank you for taking the question. You started to bundle on NVIDIA enterprise now with the H-100. I'm curious if you can talk about how we should think about timing around software monetization? And how we should kind of see this flow through the model, particularly with the focus on the AI enterprise and Omnivere side of things? Jen-Hsun Huang: Yes. Thanks, CJ. We're making excellent progress in NVIDIA AI enterprise. In fact, you saw probably that we made several announcements this quarter associated with clouds. You know that NVIDIA has a rich ecosystem. And over the years, our rich ecosystem and our software stack has been integrated into developers and start-ups of all kinds, but more so -- more than ever, we're at the tipping point of clouds, and that's fantastic. Because if we could get NVIDIA's architecture and our full stack into every single cloud, we could reach more customers more quickly. And this quarter, we announced several initiatives, one has several partnerships and collaborations, one that we announced today, which has to do with Microsoft and our partnership there. It has everything to do with scaling up AI because we have so many start-ups clamoring for large installations of our GPU so that they could do large language model training and building their start-ups and scale out of AI to enterprise and all of the world's Internet service providers. Every company we're talking to would like to have the agility and the scale, flexibility of clouds. And so, over the last year or so, we've been working on moving all of our software stacks to the cloud are of our platform and software stacks to the cloud. And so, today, we announced that Microsoft and ourselves are going to standardize on the NVIDIA stack, for a very large part of the work that we're doing together so that we could take a full stack out to the world's enterprise. That's all software included. We, a month ago, announced the same similar type of partnership with Oracle. You also saw that rescale a leader in high-performance computing cloud has integrated NVIDIA AI into their stack. [Inaudible] has been integrated into GCP. And we announced recently Nemo, large language model and bionemo large language model to put NVIDIA software in the cloud. And we also announced Omniverse is now available in the cloud. The goal of all of this is to move the NVIDIA platform full stack off boarding the cloud so that we can engage customers much, much more quickly and customers could engage our software if they would like to use it in the cloud, it's per GPU instance hour if they would like to utilize our software on-prem, they could do it through software license. And so, license and subscription. And so, in both cases, we now have software available practically everywhere you would like to engage it. The partners that we work with are super excited about it because MBDA's rich ecosystem is global, and this could bring both new consumption into their cloud for both them and ourselves, but also connect all of these new opportunities to the other APIs and other services that they offer. And so, our software stack is making really great progress. Operator: Your next question comes from the line of Chris Caso with Credit Suisse. Your line is now open. Chris Caso: Yes. Thank you. Good evening. I wonder if you could give some more color about the inventory charges you took in the quarter and then internal inventory in general. In the documentation, you talked about that being a portion of inventory on hand plus some purchase obligations. And you also spoke in your prepared remarks that some of this was due to China data centers. So if you can clarify what was in those charges. And then, in general, for your internal inventory. Does that still need to be worked down? And what are the implications if that needs to be worked down over the next couple of quarters? Colette Kress: Thanks for the question, Chris. So as we highlighted in our prepared remarks, we booked an entry of $702 million for inventory reserves within the quarter. Most of that, primarily, all of it is related to our data center business, just due to the change in expected demand looking forward for China. So when we look at the data center products, a good portion of this was also the A100, which we wrote down. Now, looking at our inventory that we have on hand and the inventory that has increased, a lot of that is just due to our upcoming architectures coming to market. our ADA architecture, our hopper architecture and even more in terms of our networking business. We have been building for those architectures to come to market and as such to say. We are always looking at our inventory levels at the end of each quarter for our expected demand going forward. But I think we've done a solid job that we used in this quarter just based on that expectation going forward. Operator: Your next question comes from the line of Timothy Arcuri with UBS. Your line is now open. Timothy Arcuri: Thanks a lot. Colette, can you -- I have a two-part question. First, is there any effect of stockpiling in the data center guidance? I ask because you now have the A800 that is sort of a modified version of the A100 with the lower data transfer rate. So one could imagine that customers might be stocking that while they can still get it. And I guess the second part of that is related to the inventory charge, can you just go into that a little bit more? Because last quarter, it made sense that you took a charge because revenue was less than you thought, but revenue came in pretty much in line. And it sounded like China was a net neutral. So is the charge related to just working A100 inventory down faster? Is that what the charges related to? Colette Kress: Sure. So let me talk about the first statement that you indicated. Most of our data center business that we see is we're working with customers specifically on their needs to build out accelerated computing and AI. It's just not a business in terms of where units are being held for that. They're usually four very, very specific products and projects that we see. So I'm going to answer no. Nothing that we can see. Your second question regarding the inventory provisions. At the end of last quarter, we were beginning to see softness in China. We've always been looking at our needs long term. It's not a statement about the current quarter in inventory, as you can see. It usually takes two or three quarters for us to build product for the future demand. So that's always a case of the inventory that we are ordering. So now looking at what we've seen in terms of continued lockdowns, continued economy challenges in China it was time for us to take a hard look of what do we think we'll need for data center going forward and not leg for write-downs. Operator: Your next question comes from the line of Stacy Rasgon with Bernstein. Your line is now open. Stacy Rasgon: Hi, guys. Thanks for taking my question. Colette, I had a question on the commentary you gave on the sequentials. It kind of sounded like data center maybe had some China softness issues. You said gaming resumed sequential growth. But then you said sequential growth for the company driven by auto gaming and data center. How can all three of those grow sequentially if the overall guidance is kind of flattish? Are they all just like growing just a little bit? Or is one of them actually down? Like how do we think about the segments into Q4 given that commentary? Colette Kress: Yes. So your question is regarding the sequentials from Q3 to our guidance that we provided for Q4. As we are seeing the numbers in terms of our guidance, you're correct, is only growing about $100 million. And we've indicated that three of those platforms will likely grow just a little bit. But our pro visualization business we think is going to be flattish and likely not growing as we're still working on correcting the channel inventory levels. to get to the right amount. It's very difficult to say which will have that increase. But again, we are planning for all three of those different market platforms to grow just a little bit. Operator: Your next question comes from the line of Mark Lipacis with Jefferies. Your line is now open. Mark Lipacis: Hi. Thanks for taking my question. Jen-Hsun, I think for you, you've articulated a vision for the data center we're a solution with an integrated solution set of a CPU, GPU and DPU is deployed for all workloads or most workloads, I think. Could you just give us a sense of or talk about where is this vision in the penetration cycle? And maybe talk about Grace Grace's importance for realizing that vision, what will Grace deliver versus an off-the-shelf x86 where -- do you have a sense of where Grace will get embraced first or the fastest within that vision? Jen-Hsun Huang: Grace's data moving capability is off the charts. Grace also is memory coherent to our GPU, which allows our GPU to expand its effective GPU memory, fast GPU memory by a factor of 10. That's not possible without special capabilities that are designed between hopper and Grace and the architecture of Grace. And so, it was designed. Grace is designed for very large data processing at very high speeds. Those applications are related to, for example, data processing is related for recommender systems, which operates on petabytes of live data at a time. It's all hot. It all needs to be fast, so that you can make a recommendation within milliseconds to hundreds of millions of people using our service. It is also quite effective at AI training, machine learning. And so, those kind of applications are really terrific. We -- Grace, I think I've said before that we will have production samples in Q1, and we're still on track to do that. Operator: Your next question comes from the line of Harlan Sur with J.P. Morgan. Your line is now open. Harlan Sur: Good afternoon and thanks for taking my question. Your data center networking business, I believe, is driving about $800 million per quarter in sales, very, very strong growth over the past few years. Near term, as you guys pointed out, and the team is driving strong Nick and blue food attached to your own compute solutions like DGX and more partner announcements like VMware, but we also know that networking has pretty large exposure to general purpose cloud and hyperscale compute spending trends. So what's the visibility and growth outlook for the networking business over the next few quarters? Jen-Hsun Huang: Yes. If I could take that. First, thanks for your question. Our networking, as you know, is heavily indexed to high-performance computing. We're not -- we don't serve the vast majority of commodity networking. All of our network solutions are very high end, and they're designed for data centers that move a lot of data. Now, if you have a hyperscale data center these days, and you are deploying a large number of AI applications. It is very likely that the network bandwidth that you provision has a substantial implication on the overall throughput of your data center. So the small incremental investment they make in high-performance networking translates to billions of dollars of savings slightly in provisioning the service or billions of dollars more throughput, which increases their economics. And so, these days, with disaggregated and I application, AI provisioning and data centers, high-performance networking is really quite fantastic and it pays for itself right away. But that's where we are focused in high-performance networking and provisioning AI services in -- well, the AI applications that we focus on. You might have noticed that NVIDIA and Microsoft are building one of the largest AI infrastructures in the world. And it is completely powered by NVIDIA's InfiniBand 400 gigabits per second network. And the reason for that is because that network pays for itself instantaneously. The investment that you're going to put into the infrastructure is so significant that if you were to be dragged by slow networks, obviously, the efficiency of the overall infrastructure is not as high. And so, in the places where we focus networking is really quite important. It goes all the way back to when we first announced the acquisition of Mellanox. I think at the time, they were doing about a few hundred million dollars a quarter, about $400 million a quarter. And now we're doing what they used to do in the old days, in a year, practically coming up in a quarter. And so, that kind of tells you about the growth of high-performance networking. It is an indexed to overall enterprise and data center spend but it is highly indexed to AI adoption. Operator: Your next question comes from the line of Aaron Rakers with Wells Fargo. Your line is now open. Aaron Rakers: Thanks for taking the question. I want to expand on the networking question a little bit further. When we look at the Microsoft announcement today, we think about what Meda is doing on the AI footprint that they're deploying. Jen-Hsun, can you help us understand like where your InfiniBand networking sits relative to like traditional data center switching? And maybe kind of build on that, how you're positioning spectrum for in the market, does that compete against a broader set of opportunities in the Ethernet world for AI fabric networking? Jen-Hsun Huang: Yes. Thanks, Erin. The math is like this. If you're going to spend $20 billion on an infrastructure and the efficiency of that overall data center is improved by 10%. The numbers are huge. And when we do these large language models and recommender systems, the processing is done across the entire data center. And so, we distribute the workload across multiple GPUs, multiple nodes and it runs for a very long time. And so, the importance of the network can be overemphasized. And so, the difference of 10% in overall improvement in efficiency, which is very to achieve. The difference between NVIDIA's InfiniBand, the entire software stack with what we call Magnum IO, which allows us to do computing in the network itself. A lot of software is running in the network itself, not just moving data around. We call it in-network computing because a ton of software is done at the edge at the -- within the network itself. We achieved significant differences in overall efficiency. And so, if you're spending billions of dollars on the infrastructure, or even hundreds of millions of dollars of interest on the infrastructure. The difference is really quite profound. Operator: Your next question comes from the line of Ambrish Srivastava with BMO. Your line is now open. Ambrish Srivastava: Hi. Thank you very much. I actually had a couple of clarifications. Colette, in the data center side, is it a fair assumption that compute was down Q-over-Q in the reported quarter because the quarter before, Mellanox or the networking business was up as it was called out. And again, you said it grew quarter over quarter. So is that a fair assumption? And then, I had a clarification on the USG band. Initially, it was supposed to be a $400 million, really going to what the government was trying to firewall. Is the A800 -- I'm just trying to make sure I understand it. Isn't that against the spirit of what the government is trying to do, i.e., firewall, high-performance compute? Or is A800 going to a different set of customers? Colette Kress: Thank you for the question. So looking at our compute for the quarter is about flattish. Yes, we're seeing also growth growth in terms of our networking, but you should look at our Q3 compute is about flatters with last quarter. Jen-Hsun Huang: Ambrish, A800 hardware, the hardware of ensures that it always meets U.S. government's clear test for export control. And it cannot be customer reprogrammed or application reprogrammed to exceed it. It is hardware limited. It is in the hardware that determines 800s capabilities. And so, it meets the clear test in letter and in spirit. We raised the concern about the $400 million of A100s because we were uncertain about whether we could execute. The introduction of A800 to our customers and through our supply chain in time. The company did remarkable feeds to swarm this situation and make sure that our business was not affected and our customers were not affected. But A800 hardware surely ensures that it always meets U.S. government's clear tests for export control. Operator: Your next question comes from the line of William Stein with Truist Securities. Your line is now open. William Stein: Thank you. I'm hoping you can discuss the pace of 100 growth as we progress over the next year. We've gotten a lot of questions as to whether the ramp in this product should look like a sort of traditional product cycle where there's quite a bit of pent-up demand for this significant improved performance product and that there's supply available as well. So does this rollout sort of look relatively typical from that perspective? Or should we expect a more perhaps delayed start of the growth trajectory where we see maybe substantially more growth in, let's say, second half of '23. Jen-Hsun Huang: H-100 ramp is different than the A100 ramp in several ways. The first is that the TCO, the cost benefits, the operational cost benefits because of the energy savings because every data center is now Power Limited. And because of this incredible transformer engine that's designed for the latest AI models. The performance over Ampere is so significant that I -- and because of the pent-up demand for hopper because of these new models that are that I spoke about earlier, deep recommender systems and large language models and generative AI models. Customers are clamoring to ramp hopper as quickly as possible, and we are trying to do the same. We are all hands on deck to help the cloud service providers stand up the supercomputers. Remember, I is the only company in the world that produces and ships semi-custom supercomputers in high volume. It's a miracle to ship one supercomputer every three years. it's unheard of to ship supercomputers to every cloud service provider in a quarter. And so, we're working hand with every one of them, and every one of them are racing to stand up hoppers. We expect them to have hopper cloud services stood up in Q1. And so, we are expecting to ship some volume, we're expecting to ship production in Q4, and then we're expecting to ship large volumes in Q1. That's a faster transition than MPIR. And so, it's because of the dynamics that I described. Operator: Your next question comes from the line of Matt Ramsay with Cowen. Your line is now open. Matt Ramsay: Yeah. Thank you very much. Good afternoon. I guess, Colette, I heard in your script that you had talked about maybe a new way of commenting on or reporting hyperscaler revenue in your data center business. And I wonder if you could maybe give us a little bit more detail about what you're thinking there and what sort of drove the decision? And I guess the derivative of that, Jen-Hsun, how -- that decision to talk about the data center business to hyperscalers differently. I mean, what does that mean for the business that is just a reflection of where demand is and you're going to break things out differently? Or is something changing about the mix of I guess, internal properties versus vertical industry demand within the hyperscale customer base. Colette Kress: Yes, Matt, thanks for the question. Let me clarify a little bit in terms of what we believe we should be looking at when we go forward and discussing our data center business. Our data center business is becoming larger and larger and our customers are complex. And when we talk about hyperscale, we tend to talk about seven, eight different companies. But the reality is there's a lot of very large companies that we could add to that discussion based on what they're purchasing. Additionally, looking at the cloud, looking at our cloud purchases and what our customers are building for the cloud is an important area to focus on because this is really where our enterprise is where our researchers, where our higher education is also purchasing. So we're trying to look for a better way to describe the color of what we're seeing in the cloud and also give you a better understanding of some of these large installments that we're seeing in the hyperscales. Jen-Hsun Huang: Yes. Let me double click on what Colette just said, which is absolutely right. There are two major dynamics that's happening. First, the adoption of NVIDIA in Internet service companies around the world, the number and the scale by which they're doing it has grown a lot. Internet service companies. And these are Internet service companies that offer services, but they're not public cloud computing companies. The second factor has to do with cloud computing. We are now at the tipping point of cloud computing. Almost every enterprise in the world has both a cloud-first and a multi-cloud strategy. It is exactly the reason why all of the announcements that we made this year -- this quarter, this last quarter since GTC about all the new platforms that are now available in the cloud. a CSP, a hyperscaler is both -- are two things to us, therefore, a hyperscaler can be a sell to customer. They are also a cell with partner on the public cloud side of their business. Because of the richness of NVIDIA's ecosystem because we have so many Internet service customers and enterprise customers using NVIDIA's full stack. The public cloud side of their business really enjoys and values the partnership with us and the cell with relationship they have with us. And it's pretty clear now that for all of the hyperscalers, the public cloud side of their business will likely would very likely be the vast majority of their overall consumption. And so, because the world CSPs, the world's public clouds is only at the early innings of their enterprise to lifting enterprise to the cloud world it's very, very clear that the public cloud side of the business is going to be very large. And so, increasingly, our relationship with CSPs, our relationship with hyperscalers will -- will include, of course, continuing to sell to them for internal consumption but very importantly, sell with for the public cloud side. Operator: Your next question comes from the line of Joseph Moore with Morgan Stanley. Your line is now open. Joseph Moore: Great. Thank you. I wonder if you could talk to looking backward at the crypto impact. Obviously, that's gone from your numbers now, but do you see any potential for liquidation of GPUs that are in the mining network, any impact going forward? And do you foresee blockchain being an important part of your business at some point down the road? Jen-Hsun Huang: We don't expect to see blockchain being an important part of our business down the road. There is always a resell market. If you look at any of the major resell sites, eBay, for example, there are secondhand graphics cards for sale all the time. And the reason for that is because a 3090 that somebody bought today, is upgraded to a 4090 or 3090 by a couple of years ago, it was up are until 4090 today. That 3090 could be sold to somebody and enjoyed it sold at the right price. And so, the volume of -- the availability of secondhand and used graphics cards has always been there. And the inventory is never zero. and when the inventory is larger than usual, like all supply demand, it would likely drift lower price and affect the lower ends of our market. But my sense is that where we're going right now with ADA is targeting very clearly in the upper range, the top half of our market. And and early signs are, and I'm sure you're also seeing that the ADA launch was a home run. That we shipped a large volume of 4090s because as you know, we were prepared for it. And yet within minutes, they were sold out around the world. And so, the reception of 4090 and the reception of 4080 today has been off the charts. And that says something about the strength and the health and the vibrancy of the gaming market. So we're super enthusiastic about the ADA launch. We have many more ad products to come. Operator: Your last question today comes from the line of Toshiya Hari with Goldman Sachs. Your line is now open. Toshiya Hari: Great. Thank you so much for squeezing me in. I had two quick ones for Colette. On supply, I think there was some mixed messaging in your remarks. I think you talked about supply being a headwind at one point. And then, when you were speaking to the networking business, I think you talked about supply easing. So I was hoping you can kind of speak to supply if you're caught up to demand at this point. And then, secondly, just on stock-based compensation, pretty mundane topic I realize, but it is -- I think in the quarter, it was about $700 million. It's becoming a bigger piece of your opex. So curious how we should be modeling that going forward. Colette Kress: Sure. When we look at our supply constraints that we have had in the past, each and every quarter, this is getting better Networking was one of our issues probably a year ago, and it has taken us probably to this quarter. and next quarter to really see our supply improved so that we can support the pipeline that we have for our customers that are -- now that's our supply. We've also made a discussion regarding our customers, supply constraints, issues when setting up a data center, even getting data center capacity has been very difficult. And therefore, that challenges them in their purchasing decisions as they're still looking for certain parts of that supply chain to come through. So that hopefully clarifies what we were talking about regarding two areas of supply. In our stock-based compensation, what we'll see, it's very difficult to predict what our stock-based compensation would be when it arrives. We have provided to our incoming employees but also once a year to our employees, and it's a single date in terms of when that is priced. So it's difficult to determine, but stock-based compensation is an important part of our employees' compensation and will continue to be. So we look at it from an overall compensation perspective. So up until now and when we do the focal, we'll see about the same size with a few additions for the reduced level of employee hiring that we have right now. Operator: Thank you. I will now turn the call back over to Jen-Hsun Huang for closing remarks. Jen-Hsun Huang: Thanks, everyone. We are quickly adapting to the macro environment. Correcting inventory levels, offering alternative products to data center customers in China and keeping our opex flat for the next few quarters. Our new platforms are off to a great start and formed the foundation for our resumed growth. MRTX is reinventing 3D graphics with ray tracing and AI. The launch of [Inaudible] is phenomenal. Gamers waited in long lines around the world, 4090 stocks sold out quickly. Hopper, with its revolutionary transformer engine is just in time to meet the surging demand for recommender systems, large language models and generative AI. NVIDIA networking is synonymous with the highest data center throughput and enjoying record results. Oren is the world's first computing platform designed for AI-powered autonomous vehicles and robotics and putting automotive on the road to be our next multibillion-dollar platform. These computing platforms run NVIDIA AI and NVIDIA Omniverse, software libraries and engines that help the companies build and deploy AI to products and services. we this pioneering work and accelerated computing is more vital than ever. Limited by business, general purpose commuting has slowed to a crawl just as AI demands more computing. Scaling through general purchase computing alone is no longer viable, both from a cost or power standpoint. Accelerated computing is the path forward. We look forward to updating you on our progress next quarter. Operator: [Operator signoff]

Operator: Good afternoon. My name is Regina, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA’s Second Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speakers’ remarks, there will be question-and-answer session. [Operator Instructions] [Technical Difficulty] Simona Jankowski: Conference call for the second quarter of fiscal 2023. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I’d like to remind you that our call is being webcast live on NVIDIA’s Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the third quarter of fiscal 2023. The content of today’s call is NVIDIA’s property. It can’t be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today’s earnings release. Our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, August 24, 2022, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. This was a challenging quarter. Total revenue of $6.7 billion was down 19% sequentially and up 3% year-on-year, below the $8.1 billion outlook we provided on our last earnings call. As we indicated in our pre-announcement press release on August 8, we experienced a shortfall to our expectations driven primarily by weaker Gaming revenue. Today, we will share with you more details on our Q2 results and Q3 outlook. Starting with Gaming. Revenue of $2.04 billion was down 44% sequentially and down 33% year-on-year, reflecting challenging market conditions. As discussed in May, we expected a sequential decline in Gaming revenue due to softness in Europe related to the war in Ukraine and COVID lockdowns in China. The decline in Gaming GPU revenue was sharper than anticipated, driven by both lower units and lower ASPs. Macroeconomic headwinds across the world drove a sudden slowdown in consumer demand. We implemented programs with our Gaming channel partners to adjust pricing in the channel and to price position current high-end desktop GPUs as we prepare for a new architecture launch. As noted last quarter, we had expected cryptocurrency mining to make a diminishing contribution to gaming demand. We are unable to accurately quantify the extent to which reduced crypto mining contributed to the decline in gaming demand. While Gaming navigates significant short-term macroeconomic challenges, we believe the long-term fundamentals in Gaming remain strong. NVIDIA RTX has redefined computer graphics and is now supported by almost 300 games and applications. NVIDIA’s GeForce GPUs are the most coveted brand by gamers, representing 15 of the top 15 most popular GPUs on Steam. Gaming has emerged from the pandemic an even more popular form of entertainment and social connectivity. Estimated GeForce sell-through is up over 70% since before the pandemic, and peak concurrent users on Steam are also up more than 70% over the same time period. GeForce NOW registered members now exceed 20 million. This quarter, we added 80 more titles, including the hugely popular Genshin Impact, bringing our total to over 1,350. Moving to Professional Visualization. Revenue of $496 million was down 20% sequentially and down 4% from a year ago. A sequential increase in mobile revenue was more than offset by lower desktop revenue, particularly at the high end. As macroeconomic headwinds intensified, enterprise demand slowed and OEMs worked to reduce inventory. We expect these trends to persist in Q3. While ProViz is undergoing a near-term adjustment after doubling last year, we believe we have expanded the market opportunity over the last couple of years with AI and Omniverse workloads. We believe hybrid work is here to stay, and with it, the need for collaborative 3D design enabled by professional graphic workstations, both at home and in the office as well as in the cloud. In June, we announced a partnership with Siemens to enable the industrial levers and AI-powered digital twins, connecting Siemens Xcelerator platform to NVIDIA Omniverse. This connection opens Siemens to the vast ecosystem of NVIDIA Omniverse and NVIDIA to Siemens ecosystem of the world’s largest industries. Earlier this month at SIGGRAPH, the premier computer graphics conference, we announced advancements to several foundational technologies of the metaverse, defined as the 3D version of the Internet. First, NVIDIA Omniverse Avatar Cloud Engine will enable businesses to create and deploy assistants and avatars, transforming interactions across a range of industries. We also unveiled 11 new Omniverse connectors, bringing the total number of connectors to the Omniverse USD ecosystem to 112. And finally, we released SDKs for the new field of neural graphics, which intertwine AI and graphics to help automate the creation of virtual world. Moving to Automotive. Revenue of $220 million increased 59% sequentially and 45% from the year-ago quarter. Strong growth was driven by auto AI solutions, which include AI cockpit and self-driving revenue, with particular strength in self-driving as new energy vehicle design wins ramp into volume. We believe Q2 was an inflection point for our automotive revenue as NVIDIA Orin has great momentum. During the quarter, we announced rollout plans of new vehicles from OEM partners, NIO, Li Auto, JIDU and Human Horizons as well as Pony.ai’s line of self-driving trucks and robotaxis, all built on NVIDIA DRIVE. Looking forward, we expect our $11 billion automotive design win pipeline to translate to continued growth. Moving to Data Center. Revenue of $3.81 billion grew 1% sequentially and 61% year-on-year. Although a record, this was somewhat short of our expectations as we were impacted by supply chain disruptions. Revenue from hyperscale customers nearly doubled year-on-year. Sequentially, sales to North America hyperscale and cloud computing customers increased but were more than offset by lower sales to China hyperscale customers affected by domestic economic conditions. Vertical industries grew both, sequentially and year-on-year. Key workloads driving growth include natural language processing, recommender systems, autonomous vehicle fleet, data processing and training and cloud graphics. Let me share a couple of customer examples. Pinterest transitioned to 100x larger recommender models by moving its inference from CPUs to NVIDIA GPUs. Its ability to deploy a higher-quality model at high throughput and low latency resulted in a 16% increase in engagement, a critical metric for the company, which has over 400 million users and 300 billion images. And Tesla recently upgraded its supercomputer to use over 7,000 A100 GPUs for autopilot training. From a product perspective, networking led growth this quarter with strong demand from our high-speed Ethernet adapters and design win momentum toward next-generation adopters, including the ConnectX-6 and ConnectX-7. We also see growing interest from cloud service providers for our new Spectrum-4 400 gigabit per second Ethernet networking platform. Additionally, we are ramping into the upcoming launches of our next generation platforms. The Hopper architecture flagship H100 data center GPU is in production. Grace is our first CPU. Top computer makers, including Dell, HPE, Inspur, Lenovo and Supermicro are adopting the new NVIDIA Grace CPU Superchip and Grace Hopper Superchip to build the next generation of supers. 72% of the systems on the latest top 500 list of the world’s fastest supercomputers are powered by NVIDIA, including 31 of 39 new systems. NVIDIA’s [Indiscernible] Selene supercomputer ranks at number 8 in the top 500 and is the world’s fastest enterprise supercomputer. Moreover, 22 of the top 30 systems on the Green 500 list of the most energy-efficient supercomputers are powered by NVIDIA. Significant advances in software technologies are key to our platform performance. In the past two years, our A100-based platform has delivered 6x more performance as measured by the MLPerf industry benchmark, largely through new software technologies and optimizations. Last month, we announced an update to the NeMo Megatron framework that can speed up the training of large language models by up to 30%, improving a multi-hundred million dollar AI infrastructure by 30% translates to significant value for customers. LLM are one of the most important neural networks today, ranging in size from tens of billions to over 1 trillion parameters. Learning from text, they can be used for real-time content generation, tech summarization, customer service chat box and question answering for conversational AI interfaces. Currently, these capabilities are available to early access customers to run on NVIDIA DGX SuperPOD and NVIDIA DGX Foundry as well as in Microsoft Azure cloud with other platforms available soon. We are working with the industry leaders in large language models, a very active and exciting space of AI. Moving to the rest of the P&L. GAAP gross margin was 43.5%, and non-GAAP gross margin was 45.9%. Gross margin includes $1.22 billion in charges for inventory and related reserves based on revised expectations of future demand and $122 million for warranty reserves. These charges incurred in the quarter reflect purchase commitments that we made during the time of severe component shortages and our current expectation of ongoing macro uncertainty. We believe our long-term gross margin profile is intact. GAAP operating expenses were up 36% from a year ago and down 32% sequentially as Q1 included a $1.35 billion acquisition termination charge related to the Arm transaction. Non-GAAP operating expenses were up 38% from a year ago and up 9% sequentially. These increases were driven primarily by employee growth costs, as well as increases in salaries to support our employees during this high inflationary environment, and engineering development of new products coming to market. We have slowed operating expense growth, balancing investments for long-term revenue growth while managing near-term profitability. Our full year non-GAAP OpEx is expected to grow over 30%. During the first half of fiscal 2023, we returned $5.5 billion to shareholders in the form of share repurchases and cash dividends. We plan to continue share repurchases. We have nearly $12 billion remaining under our authorization through December of 2023. Let me turn to the outlook for the third quarter of fiscal ‘23. We expect Gaming and ProViz revenue to decline sequentially as OEMs and channel partners reduce inventory levels to align with current levels of demand and prepare for our new product generation. We expect that decline to be partially offset by sequential growth in data center and automotive. Revenue is expected to be $5.9 billion plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 62.4% and 65%, respectively, plus or minus 50 basis points. GAAP operating expenses are expected to be approximately $2.59 billion. Non-GAAP operating expenses are expected to be approximately $1.82 billion. GAAP and non-GAAP other income and expenses are expected to be an expense of approximately $10 million, excluding gains and losses on nonaffiliated investments. GAAP and non-GAAP tax rates are expected to be 9.5%, plus or minus 1%, excluding any discrete items. Capital expenditures are expected to be approximately $550 million to $600 million. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight upcoming events for the financial community. We will be attending the Jefferies Conference in Chicago on August 30th and the Goldman Sachs Conference in San Francisco on September 12th. And we will be holding a financial analyst Q&A with management following Jensen’s GTC keynote on September 20th. Our earnings call to discuss the results of our third quarter of fiscal 2023 is scheduled for Wednesday, November 16th. We will now open the call for questions. Operator, can you assist? Would you please poll for questions? Operator: [Operator Instructions] Your first question comes from the line of C.J. Muse with Evercore ISI. C.J. Muse: I think the question we all have is what is normalized revenues for gaming for you guys? Obviously, this is a challenge to you as well. But curious how you’re thinking about it today. Is the fiscal ‘20 recovery post the first half ‘19 correction an appropriate framework, or was that inflated by crypto as well? And I guess, as part of that, how do we think about the cascading in of the new product cycle? And is there potential for future reserves needed to be taken if gaming does not meet your new updated outlook? Thanks so much. Colette Kress: Well, let me start first there and see if I can assist in terms of how to think about after we get through our completion just now of Q2 and what we have provided here for guidance for Q3. Across those two quarters, the Q2 of ‘23, the Q3 of ‘23, we have likely undershipped gaming to our end demand significantly. We expect that sell-through or essentially our end demand for those combined two quarters of Q2 and Q3 to be approximately $5 billion. Now, on top of this, keep in mind that we do have gaming growth drivers to consider for the future. These can include our new gaming product introductions that are around the corner as well as new segments of the market that we plan to reach with our gaming technology to just name a couple. I’ll turn it over to Jensen to talk a little bit more about that. Now, regarding any further types of write-downs on this perspective, we did a thorough assessment with this quarter, not only just looking at what we needed for this quarter, but what we need for the long term. Keep in mind, our inventory provisions and write-downs that we took into account had to reflect some of the purchasing that we did a supply back more than a year ago when we were still in extreme supply shortages in almost all of our products. And so, this was an opportunity for us to resize given the macroeconomic conditions, what we needed in terms of supply. So, our expectations were higher, and we took this opportunity to write them down to what our current expectations are. I’ll turn it over to Jensen to see if he wants to add more. Jensen Huang: Yes. Thanks, Colette. C.J., our sell-through is off the highs in the beginning of the year, but it’s still very solid. In fact, sell-through is -- has increased 70% since pre-COVID, pre-pandemic. And so, it’s very clear that gaming is -- the fundamentals of gaming are strong, and this medium is really doing well. Not to mention the gaming platforms are being used -- our gaming PCs are being used for influencers, people sharing content, creating content, V bloggers, VTuber, there’s all kinds of new ways of engaging and spending time with video games. Our strategy is to reduce the sell-in -- reduce the sell-in this quarter, next quarter to let channel inventory correct. Obviously, we’re off the highs, and the macro condition turned sharply worse. And so, our first strategy is to reduce sell-in in the next couple of quarters to correct channel inventory. We’ve also instituted programs to price position our current products to prepare for next-generation products. Ampere is the most popular GPU we’ve ever created. It is in the top 15 most popular gaming GPUs on Steam. And it remains the best GPUs in the world, and it will be very successful for some time. However, we do have exciting new next-generation coming and it’s going to be layered on top of that. And so, we’ve taken -- we’ve done two things. We’ve reduced sell-in to let channel inventory correct and we’ve implemented programs with our partners to price position the products in the channel in preparation for our next generation. All of this we anticipate were working towards a path to being in a good shape going into next year. Okay? So, that’s what our game plan is. Operator: Your next question will come from the line of Vivek Arya with Bank of America Securities. Vivek Arya: Actually, I just wanted to clarify, Jensen. So, should we assume that a gaming sell-in will kind of stay at these levels into your Q1 or Q2, or depending on new product launch, it might recover? So just wanted to make sure our baseline assumption is set there. And then, my question is actually similar on the data center. Sales are pretty strong right now, but there is a concern that data center CapEx could be the next shoe to drop in this rolling correction in semiconductors. I’m curious, what’s your sense of utilization of your data center shipments? And what is the risk that there could be a correction in the data center given some of the macro caution expressed by some of the hyperscaler and enterprise customers? Jensen Huang: Thanks, Vivek. The sell-through -- the sell-through, as I mentioned earlier, of GeForce is solid. The end market gaming demand is solid. It’s off the highs, which was really high recently in the beginning of the year. And so, we have -- and because we were building for such a vibrant market, we found ourselves with excess inventory. And so, our strategy is to sell well below -- sell in well below the current sell-through levels in the marketplace to give the channel an opportunity to correct. We’ll do that for a couple of quarters or so. We believe that by the end of the year, we’ll be in a good shape going into next year. And so, I hope that answers your question. But, the important thing is our sell-in rate is far below what is happening in the market for sell-throughs. The sell-through is solid, has increased 70% since pre-COVID. And so, the gaming market is really quite vibrant. On the second question, on data center end markets, we hear fairly broadly that GPU supply is in shortage in the cloud. We hear quite broadly that demand for GPU rentals far exceeds current supply. And it’s fairly sensible to us, partly because the number of use cases for GPUs in the cloud has grown quite a bit. If you look at one particular segment in just managing -- collecting data and managing the data of the AV fleet and using that data to train AI models, using that data to reconstruct HD maps, the usage of GPUs in the cloud for just that one application has grown a lot. And furthermore, there’s the deep learning-based recommender systems has demonstrated such significant effectiveness. And it helps internet service providers to enhance engagement, enhance click-through rate. And so that -- so this particular form of recommender systems is going to really drive a fair amount of data processing and machine learning in the cloud. And then, of course, over the last several years, a very important model has emerged called transformers. You and I’ve spoken about this model several times in the past. And it’s been found that this transformer model, this large language -- this language model, which when scaled up in size, exhibits really spectacular and effective capabilities for -- to be used to learn skills with either few shots or almost no shot, meaning it could learn skills, it could perform skills that it has never learned because the knowledge was somehow encoded from the large amount of data that it had learned from. And so, this large language model area of innovation is used in, of course, conversational chat, Q&A summarization, text generation, image generation. But very importantly, it’s being used in life sciences for understanding chemistry. We’ve done some very important work in this area ourselves called MegaMolBART, understanding proteins, understanding DNA to learn the language of these large -- very, very large, spatially as well as temporally or sequentially types of data. And so, the impact of this area is really quite worth staying close to. It’s called large language models. I think Stanford did a paper that called it the Foundation models that could be used for training all kinds of other types of AIs. And so, we’re seeing a great deal of demand for GPUs in the cloud. We were challenged this quarter with a fair amount of supply chain challenges because as you know, we don’t just sell the GPU chip, but these systems are really complex with a large number of chips in the system components that we offer like HGX. And so kitting -- all of the components that have to come together for us to be able to deliver the final component. And then furthermore, these data centers sit idle until the last piece comes together. And the last piece includes very complicated switches and very complicated NICs and networkings and cables. And so these -- building these high-performance computing data centers at very large scale for the world’s cloud is not particularly easy. And so the supply chain challenges have been somewhat disruptive. But the demand is there. And on top of that, we’re ramping into Hopper, which is really a fantastic generation. Operator: Your next question will come from the line of Matt Ramsay with Cowen. Matt Ramsay: I wanted to follow up on the data center question that Vivek just asked from a couple of angles. I guess, Colette, the first angle being, in the release, you guys talked about pulling high $200 million of revenue into the July quarter from October, but also with supply chain challenges, maybe some deliveries that were meant for July got pushed back into October. So if you could talk a little bit more about those dynamics and just clarify for us that the October sequentially up data center guidance is actually clean of any pull-ins. And Jensen, the second part, the moving pieces, networking stronger in data center, U.S. hyperscale stronger, China hyperscale weaker. If you could kind of walk us through the trends that you’re seeing into the October and January quarters. And in those sort of those breakouts and when clarify for us when you think H-100 will really start to drive revenue. Colette Kress: Great. Thanks for the question. And it’s kind of a little bit of an add-on to some of the statements that Jensen was discussing regarding our supply chain and what we’re seeing today. Our supply chain during the quarter really was quite difficult, was quite challenging to work through. Our platforms, including HDX, networking chips, cables, switches, were very important to the customers. It’s not just about us selling the GPUs. So even though customers orders components themselves, they’re looking from us what we may refer to as kits, kits that go with those GPUs for them to stand up their data centers. We also experienced supply disruptions internally with our logistics and our component availability. Some of our supply arrived very late in the quarter. We had very little time from a logistics and availability to get those things out. Customers were impacted as well by availability of key third-party other components that we weren’t offering, which were slowing down some of their deployments. So what we did in our Q2 orders that couldn’t be delivered in Q3, given that some of these supply constraints existed, and we had Q3 demand where we did have supply in Q2. So we worked with customers to optimize that supply and demand, and that’s what we’ve disclosed to you. Jensen Huang: Let me answer the questions about the North American and the China hyperscalers. The Chinese hyperscalers and the Chinese Internet companies really, really slowed down infrastructure investment this year, particularly starting in -- they’ve been rather slow in building out and really accelerate -- well really slowed down in Q2. This slowdown can’t last forever. And the number of new technologies in software, the number of people who are using clouds and the number of cloud services is continuing to grow. And so I fully expect investment to return. They’re a very important market for us, a very large market for us. And the fact that North American hyperscalers doubled year-over-year our revenues at North American hyperscalers, and that was offset by declines in China said something about the slowdown in China. And so I don’t think that’s going to last forever. I think it’s going to return. With respect to Hopper, we’re in full production now. And we’re racing to get Hopper 2, all of the CSPs are dying to get them. And it goes with our HGX, which is multiple Hoppers on a system tray, it’s really a supercomputer in a motherboard, if you will. And it goes along with it networking gear and switch gear. And so there’s the enormous amounts of resources apply from all of the CSPs around the world and ourselves to get Hopper. We expect to ship substantial Hoppers in Q4. Operator: Your next question will come from the line of Toshiya Hari with Goldman Sachs. Toshiya Hari: I had a question regarding the $1.22 billion inventory charge, maybe for Colette, on this one specifically. In the CFO commentary, I think you stated that the inventory charge is related to weaker demand in both data center and gaming. I think the gaming side is pretty clear based on your comments so far. Curious what’s changed on the data center side? Is it mostly the Chinese hyperscalers that Jensen just spoke to? Or is there something else going on in terms of how you’re thinking about demand in data center over the next couple of quarters? And related to this, curious if the delay in Sapphire Rapids at Intel is having an impact on your business in the near to medium term? Colette Kress: Thanks for the question. So our inventory charges, as we commented, we’re taking a thorough look of not only this last quarter as well as the quarter that we’re guiding, but looking over the long term of what we need for demand and then what we had in terms of supply. Remember, we had purchased this very early on in the year as we needed to, to drive the commitment of the supply that we already have. And so what is happening here for the data center, we had great, high expectations. We still have very strong, solid growth projection for data center as well. We’re going to take this opportunity for some of the prior architecture pieces to write down those given what we see as just a change in terms of our expectations going forward. So you are correct. There are also pieces in there for gaming. We have written down some silicon and chips as the macroeconomic conditions and you get ready for our future product launches take into account, but there’s also components, services and capacity in some of the other drivers that are incorporated in those write-downs. Jensen Huang: Our Hopper supports previous generation CPUs. But I guess, next-generation GPUs, CPUs, Sapphire Rapids and Genoa after that as well as Graviton. And so -- so we certify and test across all of the CPUs because the cloud service providers demand it. And they intend to deploy NVIDIA accelerators, NVIDIA Hoppers across a large number of CPUs. There is no question that the delay is disruptive and a lot of engineers have to scramble. It would have been a lot easier if next-generation CPUs were to have executed more perfectly. However, Hopper goes into an environment with CSPs where they connect our PCI Express connectors to old generation, current generation CPUs as well. And so nobody likes the delay. The next-generation CPUs will trigger a refresh of infrastructure and new servers. And so I’m super excited about them. However, we’re going to be able to go to market plentifying with Hopper supporting existing infrastructure. Operator: Your next question will come from the line of Aaron Rakers with Wells Fargo. Aaron Rakers: Yes. And a lot of it’s kind of been talked a little bit about, but I just -- Colette, I want to think about the numbers a little bit more. I guess, based on the guidance commentary, where I’ve come up thinking about would be like a 30-plus percent sequential decline in gaming and Professional Visualization and maybe kind of low to mid-single-digit growth in Data Center and Auto. So I guess, is that the right context? And with that and with Hopper coming down the pipeline, we’re going to have a lot of questions around kind of the deceleration of year-over-year growth in Data Center, I believe. Do you think that Hopper, as that comes fully available, it sounds like in fiscal 4Q, that you actually see Data Center growth reaccelerate as that product cycle materializes? Colette Kress: Yes. Thanks for the question. And that’s a pretty good understanding of our guidance. And we do expect, yes, gaming to decrease, not in the dollar amount that it decreased between Q1 and Q2. So that may be of our 2 areas of the decline, our gaming and ProViz, that may be about 3/4 of it associated with that gaming. And then Professional Visualization would probably be about 1/4 of the 2 areas that will decline. Our Data Center yes, we do expect it to grow. It may grow about what we just saw between Q1 and Q2. We’ll continue to look at it. There may be some more opportunity there. And automotive, very similar to our thoughts at the very beginning of the quarter, we are expecting continued growth through each of the quarters of this fiscal year. We felt that Q2 was an inflection point. So we’ll continue to grow into Q3 and hopefully Q4 going forward. I’ll turn it over to Jensen to see his thoughts in terms of Hopper, what Hopper brings to us in Q4 and expectations. Jensen Huang: The first thing I’d say, Aaron, is that we are selling in or we’re selling far below the market demand, far -- excuse me, far below the market sell-through. And the reason for that is to allow the inventory the channel inventory, the OEM inventories to correct. And this allows us to prepare for our next generation. And our next generation has Hopper for compute, but we also have the next generation for computer graphics that will be coming to market. Hopper is a giant new generation because it is designed to perform this new type of AI model called Transformers. It has an engine inside it called Transformer engine with numerical formats and pipelines that allows us to do a spectacular job on Transformer-type of models, which includes large language models, but it also includes computer vision models that are now able to be processed with this new type of AI model called Transformers. And so I fully expect Hopper 2 to be the next springboard for future growth. And -- and the importance of this new model, Transformer, can’t possibly be understated and can’t be overstated. This is the impact of this model across robotics, computer vision, languages, biology, chemistry, drug design is just really quite spectacular. And I’m sure that you’ve been hearing about this new breakthrough in AI, and Hopper was designed for this. Operator: Your next question will come from the line of Atif Malik with Citi. Atif Malik: Colette, can you talk about networking versus compute? Were they both supply constrained in the July quarter? And are they both sequentially growing in the October quarter? Colette Kress: So within our Q2 results, we have been continuing to improve our supply for networking. We have a lot of important products that the CSP needs, many of our customers’ needs, and we have been working to really improve that supply. And we were able to set very strong growth in terms of networking, both sequentially and year-over-year. And as we move into the next quarter, we’re going to have to see which is going to be growth larger. We’re just going to have to take a lot once we finish that quarter. But our supply for compute is here. But as we’ve discussed, sometimes it’s important that they have many of our other components that we provide in networking at the same time that we are providing the GPUs. So sometimes those are very important for us to deliver together. So we always have to keep that in mind. So it’s not always supply constrained, but there are certain parts of it that are. Operator: Your next question will come from the line of Joseph Moore with Morgan Stanley. Joseph Moore: Great. I wanted to ask why did the supply constraints hit you guys so hard this quarter? I mean you’ve done such a great job in the last couple of years outgrowing really everybody in a very challenging supply chain environment. It hasn’t tripped you up at all. And it seems like now it’s kind of hitting you fairly hard at a time when, in other cases, it’s kind of easing. So I’m just kind of curious, what is it about the timing and how long does it sort of take? Or is that a Hopper issue? Is it related to other components? As you talked about, how long does it take to clear those issues up? Colette Kress: Let me start and see if Jensen wants to add on to it. Our execution has absolutely been phenomenal. When you think about the challenges of we’re almost putting together a full data center for our customers and getting it shipped out. So we’re no different in the same way that the CSPs are challenging. We’re setting up their data centers as we’re such an integral part of that. And so networking has been short of supply. These are the same supply issues that some of our CSPs are having. So our supply arrived a little bit late in the quarter for some of our key products that we needed to get out. And putting that together caused some disruption in our logistics and distribution. We were pleased in terms of reaching the leverage of networking that we did, but we did have some challenges this quarter. Operator: Your next question will come from the line of Stacy Rasgon with Bernstein. Stacy Rasgon: I wanted to go back to that data center inventory charge. You listed data center first when you talked primarily related to data center and gaming. Can you give us a feeling for how much of that charge was data center versus gaming? And then to follow up on that, it did sound like to me that it was mostly Ampere and not Hopper change in expectations. Can you verify that or clarify it? And just talk about what’s happened with your expectations for Hopper? Have they gone up or down? Or has there been any change at all relevant to that inventory charge? Colette Kress: Yes, Stacy. Regarding our inventory charges that we had, when you think about what we have in supply, whether it be chips, components, whether it be memory. Remember, a lot of these things can be used interchangeably across the 2. Additionally, the value of our inventory for data center is much different than the value of what we have for gaming from an overall cost perspective. So we’re creating capacity opportunity, putting together all of those systems in terms of data center. It is prior architectures. Absolutely, this is not a question regarding anything of our future products coming to market. Nothing on the inventory provision has to do with that. So we took this as looking at the macroeconomic conditions, as we’ve discussed. Our expectations, our plans were higher. They’re still quite solid that we see in demand both for gaming as well as solid for data center, and that will continue. But we did have to just take a rightsizing of that note. Jensen Huang: Hopper was designed for transformers. The new transformers was going to be important. Nobody could have predicted the profound importance of large language models. Large language models, excitement, innovation, ideas, companies, start-ups, industries, all exceeding everyone’s expectations. I don’t think anybody could have predicted the impact of Transformers as it scaled up to these giant sizes. There’s a fair amount of literature now written about language models that were smallish in the old days, in the beginning several years ago, 3 years ago. And the ones that are in the hundreds of billions and moving towards probably several trillion parameters, the effectiveness of the AI is really quite spectacular. And to have AI that was never trained on a particular skill and yet within 1 shot or 1 shot of trying or even no shots, are able to perform that skill is beyond anybody’s expectations, I would think. And so I think the -- the success of Hopper is -- reflects the amount of work and pent-up demand for large training systems that Hopper is going to go into. If that’s an indicator, I think Hopper is going to be a spectacular success. Operator: Your next question will come from the line of Srini Pajjuri with SMBC Nikko. Srini Pajjuri: I have a question on gross margins, Colette. The 65% non-GAAP number you’re guiding to, does that include or assume any additional write-offs on the inventory front? And then just to expand on that, your mix is probably a tailwind to gross margins given that gaming is down significantly and data center is up a little bit. And I’m just wondering, are there any other offsets? Because I would have thought gross margins could actually be better than your longer-term model because of the mixed tailwinds? Colette Kress: Yes. Thanks for the question. So our gross margins outside of the inventory charges in Q2 as well as going into Q3 is really about our sales mix that we have and probably also to understand that our sales mix in the next quarter for GPUs is not in the high end. And so that has impacted our gross margin as we move into Q3. You are correct. We do expect that data center will assist in our gross margins but we also have growth plans in auto. Auto is below our company average, and so that will tend to offset some of those upper bound things that we will see in terms of data center. From time to time, there’s always a small amount of scraps that we will have in our gross margin estimates. So nothing material is planned. But there is small scraps that may occur from quarter-to-quarter that are included in our gross margins. Operator: Your next question will come from the line of Timothy Arcuri with UBS. Tim Arcuri: Colette, I had a clarification and then a question. So my clarification is whether October gross margins are benefiting at all from the sale of previously written down inventory. And then my question is whether you can give us the enterprise cloud split in data center because it sounds like the mix shifted more towards enterprise in July. And I think investors might want to see that as risky in the face of enterprise clearly slowing. So I’m wondering if you can give us that. Colette Kress: Okay. No. No, there is nothing in our Q3 regarding those inventory provisions that we took in terms of earning that back, in terms of our Q3. Regarding our split between our hyperscalers and data centers and what we refer to as our vertical industries. They always tend to be about the same, 50% for one, 50% for the others. They’re still in about that range. We had discussed that our China hyperscales did not drive growth in terms of sequentially here. And so that did influence in terms of the hyperscales, but still we are approximately in that 50%, 50%. Operator: Your next question will come from the line of Harlan Sur with J.P. Morgan. Harlan Sur: On the weakness in ProViz, this is an enterprise-focused business, right? Granted, it’s a somewhat narrow vertical market, but it does sort of play into the market concerns that consumer is weak now, enterprise is the next shoe to drop. So is the decline in ProViz attributed to enterprise spending weakness? Or is there some other dynamic? And why or why not is this not a leading indicator for your enterprise and vertical industry segments within your data center business? Colette Kress: So our Pro Visualization business, just in such a short time, they were in short supply and really trying to feed so much of the industry’s desire to both refresh and attracted to the new RTX workstations that were there. We were fueling both mobile as well as desktop for that market and growing quite nicely. Remember, all of last year, doubled the size of ProViz in 1 year. So right now, the OEMs are concentrating on their levels of inventory. We want to make sure that they can get through that inventory. But it -- keep in mind, very similar to our discussion on gaming, there is still solid demand. There is still solid demand. We just have to correct some of the inventory, but we still see both the opportunities that we’ve created for the market for these to be long-standing. And your second question, remind me again? Jensen Huang: Whether the broader enterprise market is -- the verticals are going to be affected by that? I would say, first of all, we don’t know. Second of all, unlike like our Workstation business, our ProViz business, there’s no installed base. Most of the ProViz sales tend to be tend to be upgrades or replacements from something that has -- our installed base of 3 or 4 or 5 years that people -- whatever upgrade cycle they happen to have. And so in the case of ProViz, the companies that are buying are ProViz, our ProViz systems likely already have systems that they’ve been using. And so if they were to tighten up ProViz for whatever reason, the people could continue to use what they have. In the case of our AI business, there’s no real installed base. These are all brand-new things that people are growing into. And the productivity benefits or the cost savings benefits of using autonomous systems is fairly profound. And it’s not so much that the demand isn’t out there. Everybody would like to be more productive. Everybody would like to save more money. Everybody would like to move faster. It’s just that AI understanding and AI’s use is still spreading. And so we’re delighted by the rate of growth and the rate of adoption of enterprise. My sense is that our AI business and our Viz business have very different characteristics for that reason. But what Colette said earlier is about our ProViz businesses last quarter is absolutely true, which is OEMs realizing that the end market is slowing and taking the opportunity to correct their inventory. Operator: Your final question will come from the line of Ross Seymore with Deutsche Bank. Ross Seymore: Let me ask a question. I just had a longer-term question about once your gaming business normalizes, with the absence of crypto in a general sense and with the merge coming, et cetera, how do you view the pricing environment? I know you guys really weren’t raising prices like we saw in the MSR key premiums in the aftermarket. But generally, your mix was quite rich over the last year or 2. You’re going to have lovely coming in that will obviously help the mix sequentially versus the last couple of quarters. But how do you think about it normalizing? Is that $2.5 billion per quarter sell-through rate comes, is the ASP mix across your stack about the same? Or does it go down because of the absence of that crypto tightening dynamic? Jensen Huang: I would say that without crypto dynamic, the mix would go down. However, the overall trend long term, the ASP is drifting up. And the way to think about that is a game console, when -- my first game console was $99. Lately, game consoles are selling for about $599. And the reason for that is because it’s more useful than ever. You use your gaming console for your greatest form of entertainment, and you use it for a very, very long time. And GeForce essentially is a game console inside your PC. And we’ve always believed that the ASP of GeForce should drift towards the average selling price of a game console. And so it should be something along the lines of $500 or so roughly at this time. We also have GeForce in the cloud. And because GeForce in the cloud is hosting many gamers simultaneously, it tends to want to be a much more powerful GeForce. And so our cloud gaming GeForce tends to be -- our cloud gaming graphics tends to be a much higher end. And so -- and then, of course, there’s the design aspect of it. Most designers and most creators are able to use GeForce these days. And they use their PC to create content, and much of that content goes into video games and/or they’re using video games to create their artistic content. And so the GeForce is not just for gaming for them. The GeForce is essentially their creative work station as well. And so there are several dynamics that are causing the ASP of GeForce to go up, and we’ve been seeing this trend for several years now. Operator: Thank you. I will now turn the call back over to Jensen for closing remarks. Jensen Huang: Thanks, everyone. We’re navigating our supply chain transitions in a challenging macro environment. In Gaming, our partners and ecosystem are responding to a sudden slowdown in consumer demand and correcting channel inventory. Still, the fundamentals of gaming are strong. We’ll get through this over the next few months and go into next year with our new architecture. I look forward to telling you more about it at GTC next month. In Data Center, AI where computers are helping us write software that was impossible before is driving a computing revolution and transforming every industry. NVIDIA’s leadership in full stack data center scale, accelerated computing has made us the ideal partner for companies racing to leverage the power of AI. Even with the current macroeconomic headwinds, demand for our data center products have never been stronger. The next wave of computing is coming. With AI and 3D graphics advances, developers will extend the Internet with virtual world overlays that connect to the physical world. This next evolution of the Internet is called metaverse. We created Omniverse to connect the digital and physical world and be an open platform for creating and operating metaverse applications. The immediate applications for Omniverse span product design, manufacturing and operations. Omniverse is off to a great start. Our automotive revenue is inflecting, and we expect it to be our next $1 billion business. Autonomous driving is one of the biggest challenges AI can solve, and computing opportunity for us spans the data center to the car. Autonomous driving will transform the auto industry into a tech industry. Automotive is one of the first to transform into a software-defined tech industry that all industries will be. We’re building NVIDIA AI and NVIDIA Omniverse to be the engines for the world’s enterprise to become software-defined, AI-powered technology companies. I look forward to next month’s GTC conference, where we will share new advances of RTX reinventing 3D graphics and gaming. AI’s continuing breakthroughs and building the metaverse, the next evolution of the internet. So join us. We look forward to updating you on our progress next quarter. Thank you. Operator: This concludes today’s conference call. You may now disconnect.

Operator: Good afternoon. My name is David, and I'll be your conference operator today. At this time, I'd like to welcome everyone to NVIDIA's first quarter earnings call. Today's conference is being recorded. All lines have been placed on mute to prevent any background noise. After the speaker’s remarks, there will be question-and-answer session. [Operator Instructions] Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the First Quarter of Fiscal 2023. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that, our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2023. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 25, 2022, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. We delivered a strong quarter driven by record revenue in both Data Center and Gaming with strong fundamentals and execution against a challenging macro backdrop. Total revenue of $8.3 billion was a record, up 8% sequentially and up 46% year-on-year. Data Center has become our largest market platform, and we see continued strong momentum going forward. Starting with Gaming. Revenue of $3.6 billion rose 6% sequentially and 31% year-on-year, powered by the GeForce RTX 30 Series product cycle. Since launching in the fall of 2020, the RTX 30 Series has been our best Gaming product cycle ever. The gaming industry has grown tremendously with 100 million new PC gamers added in the past two years according to Newzoo. And NVIDIA RTX has set new standard for the industry with demand from both first-time GPU buyers as well as those upgrading their PCs to experience the 250-plus RTX-optimized games and apps, double from last year. We estimate that almost a-third of the GeForce Gaming GPU installed base is now on RTX. RTX has brought tremendous energy into the gaming world, and has helped drive a sustained expansion in our higher-end platforms and installed base with significant runway still ahead. Overall, end demand remained solid though mixed by region, and demand in Americas remained strong. However, we started seeing softness in parts of Europe related to the war in the Ukraine and parts of China due to the COVID lockdowns. As we expect some ongoing impact as we prepare for a new architectural transition later in the year, we are projecting Gaming revenue to decline sequentially in Q2. Channel inventory has nearly normalized and we expect it to remain around these levels in Q2. The extent in which cryptocurrency mining contributed to Gaming demand is difficult for us to quantify with any reasonable degree of precision. The reduced pace of increase in Ethereum network hash rate likely reflects lower mining activity on GPUs. We expect a diminishing contribution going forward. Laptop Gaming revenue posted strong sequential and year-on-year growth, driven by the ramp of the NVIDIA RTX 30 Series lineup. With this year's spring refresh and ahead of the upcoming back-to-school season, there are now over 180 laptop models featuring RTX 30 Series GPUs and our energy-efficient thin and light Max-Q technologies, up from 140 at this time last year. Driving this growth are not just gamers, but also the fast-growing category of content creators for whom we offer dedicated NVIDIA studio drivers. We've also developed applications and tools to empower artists from Omniverse for advanced 3D and collaboration to broadcast for live streaming to Canvas for painting landscapes with AI. The creator economy is estimated at $100 billion and powered by 80 million individual creators and broadcasters. We continued to build out our GeForce NOW cloud gaming service. Gamers can now access RTX 3080 class streaming, our new top-tier offering with subscription plans of $19.99 a month. We added over 100 games to the GeForce NOW library, bringing the total to over 1,300 games. And last week, we launched Fortnite on GeForce NOW with touch controls for mobile devices, streaming through the Safari web browser on iOS and the GeForce NOW Android app. Moving to Pro Visualization. Q1 revenue was $622 million was down sequentially 3% and up 67% from a year ago. Demand remains strong as enterprises continued to build out their employee's remote office infrastructure to support hybrid work. Sequential growth in the mobile workstation GPUs was offset by lower desktop revenue. Strong year-on-year growth was supported by the NVIDIA RTX Ampere architecture product cycle. Top use cases include digital content creation at customers such as Sony Pictures Animation and medical imaging at customers such as Medtronic. In just its second quarter of general availability, our Omniverse enterprise software is being adopted by some of the world's largest companies. Amazon is using Omniverse to create digital twins to better optimize warehouse design and flow and to train more intelligent robots. Kroger is using Omniverse to optimize store efficiency with digital twin store simulation. And PepsiCo is using Omniverse digital twins to improve the efficiency and environmental sustainability of the supply chain. Omniverse is also expanding our GPU sales pipeline, driving higher end and multiple GPU configurations. The Omniverse ecosystem continues to rapidly expand with third-party developers in the robotics, industrial automation, 3D design and rendering ecosystems developing connections to Omniverse. Moving to automotive. Q1 revenue of $138 million, increased 10% sequentially and declined 10% from the year ago quarter. Our DRIVE Orin SoC is now in production and kicks off a major product cycle with auto customers ramping in Q2 and beyond. Orin has great traction in the marketplace with over 35 customer wins from automakers, truck makers and robotaxi companies. In Q1, BYD, China's largest EV maker and Lucid an award winning EV pioneer were the latest to announce that they are building their next-generation fleets on DRIVE Orin. Our automotive design win pipeline now exceeds $11 billion over the next six years, up from $8 billion just a year ago. Moving to Data Center. Record revenue of $3.8 billion grew 15% sequentially and accelerated to 83% growth year-on-year. Revenue from hyperscale and cloud computing customers more than doubled year-on-year, driven by strong demand for both external and internal workloads. Customers remain supply constrained in their infrastructure needs and continue to add capacity as they try to keep pace with demand. Revenue from vertical industries grew a strong double-digit percentage from last year. Top verticals driving growth this quarter include consumer Internet companies, financial services and telecom. Overall, Data Center growth was driven primarily by strong adoption of our A100 GPU for both training and inference with large volume deployments by hyperscale customers and broadening adoption across the vertical industries. Top workloads includes recommender systems, conversational AI, large language models and cloud graphics. Networking revenue accelerated on strong broad-based demand for our next-generation 25, 50 and 100-gig ethernet adapters. Customers are choosing NVIDIA's networking products for their leading performance and robust software functionality. In addition, networking revenue is benefiting from growing demand for DGX super pods and cross-selling opportunities. Customers are increasingly combining our compute and networking products to build what are essentially modern AI factories with data as the raw material input and intelligence as the output. Our networking products are still supply constrained though we expect continued improvement throughout the rest of the year. One of the biggest workloads driving adoption of NVIDIA AI is natural language processing, which has been revolutionized by transformer based models. Recent industry breakthroughs traced to transformers include; large language models like GPT-3, NVIDIA Megatron BERT for drug discovery and DeepMind AlphaFold for a protein structure prediction. Transformers allow self-supervised learning without the need for human labeled data. They enable unprecedented levels of accuracy for TAF such as text generation, translation, summarization and answering questions. To do that, Transformers use enormous training data sets and very large networks well into the hundreds of billions of parameters. To run these giant models without sacrificing low inference times, customers like Microsoft are increasingly deploying NVIDIA AI, including our NVIDIA Ampere architecture-based GPUs and full software stack. In addition, we are seeing a rising wave of customer innovation using large language models that is driven by increased demand for NVIDIA AI and GPU instances in the cloud. At GTC, we announced our next-generation Data Center GPU, the H100 based on the new or upper architecture. Packed with 80 billion transistors, H100 is the world's largest, most powerful accelerator, offering an order of magnitude leap in performance over the A100. We believe H100 is hitting the market at the perfect time. H100 is ideal for advancing large language models and deep recommender systems the two largest scale AI workloads today We are working with leading server makers and hyperscale customers to qualify and ramp H100. As well as the new DGX H100 AI supercomputing system will ramp in volume late in the calendar year. Building on the H100 product side, we are on track to launch our first ever Data Center CPU, Grace, in the first half of 2023. Grace is the ideal CPU for AI factories. This week at COMPUTEX, we announced that dozens of server models based on Grace will be brought to market by the first wave of system builders, including ASUS, Foxconn, Gigabyte, QCT, Supermicro and Wiwynn. These servers will be powered by the NVIDIA Grace CPU Super Chip, which features two CPUs and the Grace Upper Super Chip, which pairs an NVIDIA Hopper GPU with an NVIDIA Grace CPU in an integrated model. We've introduced new reference designs based on Grace for the massive new workloads of next-generation data centers. CGX for cloud graphics and gaming, OVX for digital twins or Omniverse and HGX for HPC and AI. These server designs are all optimized for NVIDIA's rich accelerated computing software stacks and can be qualified as part of our NVIDIA certified systems lineup. The enabler for the Grace Hopper and Grace Super Chips is our ultra energy-efficient, low-latency, high-speed memory coherent interconnect NVLink, which scales from die to die, chip to chip and system to system. With NVLink, we can configure Grace and Hopper to address a broad range of workloads. Future NVIDIA chips, the CPUs, GPUs, DPUs, NICs and SoCs will integrate NVLink just like Grace Hopper based on our world-class SERDES technology. We're making NVLink open to customers and partners to implement custom chips that connect to NVIDIA's platforms. In networking, we're kicking off a major product cycle with the introduction of Spectrum-4, the world's first 400-gigabit per second end-to-end Ethernet networking platform, including the Spectrum-4 Switch, ConnectX-7 SmartNIC, Bluefield-3 DPU and the DOCA software. Built for AI and video Spectrum 4 arrives as data centers are growing exponentially and demanding extreme performance, advanced security and powerful features to enable high-performance advanced virtualization and simulation at scale. Across our businesses, we are launching multiple new GPU, CPU, DPU and SOC quarters over the coming quarters, with a ramp in supply to support the customer demand. Moving to the rest of the P&L, GAAP gross margin for the first quarter was 65.5% and non-GAAP gross margin was up 67.1%, up 90 basis points from a year ago, and up 10 basis points sequentially. We have been able to offset rising costs and supply chain pressures. We expect to maintain gross margins at current levels in Q2. Going forward, as new products ramp and software becomes a larger percent of revenue, we have opportunities to increase gross margins longer term. GAAP operating margin was 22.5%, impacted by a $1.35 billion acquisition termination charge related to the ARM transaction. Non-GAAP operating margin was 47.7%. We are closely managing our operating expenses to balance the current macro environment with our growth opportunities, and we've been very successful in hiring so far this year and are now slowing to integrate these new employees. This also enables us to focus our budget on taking care of our existing employees as inflation persist. We are still on track to grow our non-GAAP operating expenses in the high 20s range this year. we expect sequential increases to level off after Q2 as the first half of the year includes a significant amount of expenses related to the bring-up of multiple new products, which should not reoccur in the second half. During Q1, we repurchased $2 billion of our stock. Our Board of Directors increased and extended our share repurchase program to repurchase an additional common stock up to a total of $15 billion through December 2023. Let me now turn to the outlook for the second quarter of fiscal 2023. Our outlook assumes an estimated impact of approximately $500 million relating to Russia and China COVID lockdowns. We estimate the impact of lower sell-through in Russia and China to affect our Q2 Gaming sell-in by $400 million. Furthermore, we estimate the absence of sales to Russia to have a $100 million impact on Q2 in Data Center. We expect strong sequential growth in Data Center and Automotive to be more than an offset by the sequential decline in Gaming. Revenue is expected to be $8.1 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 65.1% and 67.1%, respectively, plus or minus 50 basis points. GAAP operating expenses are expected to be $2.46 billion. Non-GAAP operating expenses are expected to be $1.75 billion. GAAP and non-GAAP other income and expenses are expected to be an expense of approximately $40 million, excluding gains and losses on non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 12.5% plus or minus 1%, excluding discrete items. And capital expenditures are expected to be approximately $400 million to $450 million. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight the upcoming events for the financial community. We will be attending the BofA Securities Technology Conference in person on June 7, where Jensen will participate in a keynote fireside chat. Our earnings call to discuss the results of our second quarter of fiscal 2023 is scheduled for Wednesday, August 24. We will now open the call for questions. Operator, can you please poll for questions? Thank you. Operator: Thank you. [Operator Instructions] We'll take our first question from C.J. Muse with Evercore ISI. Your line is open. C.J. Muse: Yes, good afternoon. Thank you for taking the question. I guess would love to get an update on how you're thinking about the Gaming cycle from here. The business has essentially doubled over the last two years. And now we've got some crosswinds with crypto falling off, channel potentially clearing ahead of a new product cycle. You talked about macro challenges. But at the same time, only a third of the installed base has RTX and we're moving out from under supply. So we'd love to hear your thoughts from here once we get beyond kind of the challenges around COVID lockdown in the July quarter? How are you thinking about Gaming trends? Jensen Huang: Yes, C.J., thanks for the question. The -- you captured a lot of the dynamics well in your question. The underlying dynamics of the Gaming industry is really solid, net of the situation with COVID lockdown in China and Russia. The rest of the market is fairly robust and we expect the Gaming dynamics to be intact. The several things that are driving the Gaming industry. In the last two years alone, 100 million new gamers came into the PC industry. The format has expanded tremendously. And the ways that people are using their PCs to connect with friends, to be an influencer as a platform for themselves, use it for broadcast. So, many people are now using their home PCs as their second workstation, if you will, second studio. Because they're also working from home. It is our primary way of communicating these days. The need for GeForce PCs have never been greater. And so I think the fundamental dynamics are really good. And so as we look into the second half of the year, we look -- it's hard to predict exactly what -- when COVID and the war in Russia is going to be behind us. But nonetheless, the governing dynamics of the Gaming industry is great. Operator: Next, we'll go to Matt Ramsay with Cowen. Your line is open. Matt Ramsay: Thank you very much. Good morning. Jensen, I wanted to ask a bit of a question on the Data Center business. In this upcoming cycle with H-100, there's some I/O upgrades that are happening in servers that I think are going to be a fairly strong driver for you in addition to what's going on with Hopper and the huge performance leaps that are there. I wanted to ask a longer-term question, though, around your move to NVLink with Grace and Hopper and what's going on with your whole portfolio. Do you envision the business continuing to be sort of card-driven attached to third-party servers, or do you think revenue shifts dramatically, or in a small way, over time, to be more sort of vertically integrated all of the chips together on NVLink? And how is the industry sort of responding to that potential move? Thanks. Jensen Huang: Yes. I appreciate the question. The -- let's see, the first point that you made is a very big point. The next generation of servers that are being teed up right now are all Gen 5. The I/O performance is substantially higher than what was available before. And so, you're going to see a pretty large refresh as a result of that. Brand-new networking cards from our company and others. Gen 5, of course, drives new platform refresh. And so, we're perfectly timed to ramp into the Gen 5 generation with Hopper. There are a lot of different system configurations you want to make. If you take a step back and look at the type of systems that are necessary for data processing, scientific computing, machine learning and training, inference done in the cloud for hyperscale nature, done on-prem for enterprise computing, done at the edge. Each one of these workloads and deployment locations, the way that you manage would dictate a different system architecture. So there isn't one size that fits all, which is one of the reasons why it's so terrific that we support PCI Express, that we innovated chip-to-chip interconnect for diverse -- before anybody else did, this is now some seven years ago, we're in our fourth generation of NVLink that allows us to connect two chips next to each other, two dies, two chips, two modules, two SXM modules to two systems to multiple systems. And so our coherent chip-to-chip link, NVLink has made it possible for us to mix and match chips, dies, packages, systems and all of these different types of configurations. And I think that, over time, you're going to see even more types of configurations. And the reason for that has to do with a couple of very important new type of data centers that are emerging. And you're starting to see that now with fairly large installations, infrastructures with NVIDIA, HPC and NVIDIA AI. These are really AI factories where you're processing the data, refining the data and turning that data into intelligence. These AI factories are essentially running one major workload and they're running at 24/7. Deep recommender systems is a good example of that. In the future, you're going to see large language models essentially becoming a platform themselves. That would be running 24/7, hosting a whole bunch of applications. And then on the other end, you're seeing data centers at the edge that are going to be robotics or autonomous data centers that are running 24/7. They are going to be running in factories and retail stores and warehouses, logistics warehouses, all over the world. So these two new types of data centers are just emerging, and they also have different architectures. So I think the net of it all is that our ability to support every single workload because we have a universal accelerator, running every single workload from data processing to data analytics to high-performance computing to training to inference that we can support Arm and x86 that we support PCI Express to Multisystem NVlink to multi-chip NVLink to multi-die NVLink, that capability for us is -- makes it possible for us to really be able to serve all of these different segments. With respect to vertical integration, I think that system integration, the better way of maybe saying that is that system integration is going to come in all kinds of different ways. We're going to do semi-custom chips as we've done with many companies in the past, including Nintendo. We'll do semi-custom chiplets as we do with NVLink. NVLink is open to our partners. And they could bring it to any fab and connect it coherently into our chip. We could do multi module packages. We could do multi-package systems. So there's a lot of different ways to do system integration. Operator: Next, we'll go to Stacy Rasgon with Bernstein Research. Your line is now open. Stacy Rasgon: Hi, guys. Thanks for taking my questions. I wanted to follow up on the sequential. So Colette, I know you said the $500 million was a $400 million hit to Gaming and a $100 million hit to data. So I'm assuming that -- that doesn't mean the Gaming is down $400 million. I mean it's Gaming -- do you see Gaming actually down more than the actual Russia and lockdown hit. And I guess just how do I think about the relative sequentials of the businesses in light of those constraints that you guys are facing? Colette Kress: Sure. Let me start first with what does that mean to Gaming. What does that mean to Gaming for Q2? We do expect Gaming to decline into Q2. We still believe our end demand remains very strong. Ampere has just been a great architecture, and there's many areas where we continue to see strength and growth in both our sell-through and probably what we will see added into that channel as well. But in total, Q2 Gaming will decline from last quarter from Q1 that it will probably decline in the teens. As we try and work through some of these lockdowns in China, which are holding us up. So overall, the demand for Gaming is still strong. We still expect end demand to grow year-over-year in Q2. Operator: Next, we'll go to Mark Lipacis with Jefferies. Your line is open. Mark Lipacis: Hi, thanks for taking my question. If you listen to the networking OEMs, this earnings season, it seems that there was a lot of talk about increased spending by enterprises on their data centers and sometimes you hear them talking about how this is being driven by AI. You talked about your year-over-year growth in your cloud versus enterprise spending. I wonder if you could talk about what you were seeing sequentially? Are you seeing a sequential inflection in the enterprise? And can you talk about the attach rate of software for enterprise versus data centers. And, which software is -- are you seeing the most interest? I know you talked about, is it Omniverse? Is it natural language processing, or is there one big driver, or is it a bunch of drivers for the various different software packages you have? Thank you. Jensen Huang: Yeah. Thanks, Mark. We had a record Data Center business this last quarter. We expect to have a record, another record quarter this quarter, and we're fairly enthusiastic about the second half. AI and data-driven machine learning techniques for writing software and extracting insight from the vast amount of data that companies have is incredibly strategic to all the companies that we know. Because in the final analysis, AI is about automation of intelligence and most companies are about domain-specific intelligence. We want to produce intelligence. And there are several techniques now that have been created to make it possible for most companies to apply their data to extract insight and to automate a lot of the predictive things that they have to do and do it quickly. And so I think the trend that you hear other people are experiencing about machine learning, data analytics, data driven insights, artificial intelligence. However, it's described, it's all exactly the same thing. And it's sweeping just about every industry and every company. Our networking business is also highly supply constrained. Our demand is really, really high. And it requires a lot of components aside from just our chips. Components and transceivers and connectors and cables. And just -- it's a really -- it's a complicated system, the network, and there are many physical components. And so the supply chain has been problematic. We're doing our best and our supply has been increasing from Q4 to Q1. We're expecting it to increase in Q2 and increase in Q3 and Q4. And so we're really, really grateful for the support from the component industry around us, and we'll be able to increase that. With respect to software, there are two, well, first of all, there are all kinds of machine learning models, computer vision, speech AI, natural language understanding, all kinds of robotics applications, the most -- probably the largest, the most visible one is self-driving cars, which is essentially a robotic AI. And then recently, this incredible breakthrough from an AI model called Transformers that has led to really, really significant advances in natural language understanding. And so they're all these different types of models. There are thousands and thousands of species of AI models and used in all these different industries. One of my favorite, I'll just say it very quickly and I'll answer that question about the software. One of my favorites is using Transformers to understand the language of chemistry or using transformers and using AI models to understand the language of proteins, amino acids, which is genomics. To apply AI to understand -- to recognize the patterns, to understand the sequence and essentially understand the language of chemistry and biology is a really, really important breakthrough. And all of this excitement around synthetic biology, much of it stands back to the – some of these inventions. But anyhow, all of these different models need an engine to run on. And that engine is called NVIDIA AI. In the case of hyperscalers, they can cobble together a lot of open source and we provide a lot of our source to them and a lot of our engines to them for them to operate their AI. But for enterprises, they need someone to package it together and be able to support it and refresh it, update it for new architecture, support old architectures in their installed base, etcetera, and all the different use cases that they have. And so that engine is called NVIDIA AI. It's almost like a sequel engine, if you will. And except this is an engine for artificial intelligence. There's another engine that we provide and that engine is called Omniverse and it's designed for the next wave of AI, where artificial intelligence has to not just manipulate information like recommender systems and conversational systems and such. But it has to interact with physical systems. Whether it's interacting with physics directly, meaning robotics or being able to automate physical systems like heat recovery steam generators, which is really important today. And so Omniverse is designed to be able to sit at that interface, that intersection between simulation and artificial intelligence, and that's what Omniverse is about. Omniverse has now – let’s see some -- we're still early in the deployment of Omniverse for commercial license. It's been a couple of quarters now since we've released Omniverse enterprise. And I think, at this point, we have 10% of the world's top 100 companies that are already customers, licensing customers, substantially more who we're evaluating. I think it's been downloaded nearly 200,000 times. It has been tried in some 700 companies. And Colette highlighted some of the companies, you might see some of the companies that are using it in all kinds of interesting applications at GTC. And so, I fully expect that the NVIDIA AI engine, the Omniverse engine, are going to be very successful for us in the future and contribute greatly to our earnings. Operator: Next, we'll go to Vivek Arya with BofA Securities. Your line is open. Vivek Arya: Thanks. Just wanted to clarify, Colette, if your Q2 outlook includes any destocking benefits from the new products that you're planning to launch this year? And then my question is gentleman for you. You're still guiding Data Center to a very strong, I think, close to 70% or so year-on-year growth, despite all the headwinds. Are you worried at all about all the headlines about the slowdown in the macro economy? I like is there any cyclical impact on Data Center growth that we should keep in mind as we think about the second half of the year? Colette Kress: Yes. Vivek, let me first answer the question that you asked regarding any new products as we look at Q2. As we discussed about it, most of the ramp that we have of our new architectures, we're going to see in the back half of the year. We're going to start to see, for example, Hopper will probably be here in Q3, but starting to ramp closer to the end of the calendar year. So, you should think about most of our product launches to be ramping in the second half of the year on that part. I'll turn it over for Jensen Huang for the rest. Jensen Huang: Thanks. Our Data Center demand is strong and remains strong. Hyperscale and cloud computing revenues, as you mentioned, has grown significantly. It's doubled year-over-year. and we're seeing really strong adoption of A100. A100 is really quite special and unique in the world of accelerators. And this is one of the really, really great innovations as we extended our GPU from graphics to CUDA to Tensor Core GPUs. It's now a universal accelerator. And so you could use it for data processing for ETL, for example, extract, transform and load. You could use it for database acceleration. Many sequel functions are accelerated on NVIDIA GPUs. We accelerate Rapids, we accelerate which is the Python version a Data Center scale version of Pandas, we accelerate Spark 3.0. And so from database queries to data processing, to extraction, and transform and loading of data before you do training and inference and whatever image processing or other algorithmic processing you need to do can be fully accelerated on A100. And so we're seeing great success there. on at the core and closer to what is happening today, you're seeing several different very important new AI models that are being invested in at very, very large scale and with great urgency. You probably have heard about Deep Recommender Systems. This is the economic engine, the information filtering engine of the Internet, if not for the recommender system, it would be practically impossible for us to enjoy our Internet experience shopping experience with trillions of things that are changing in the world every day constantly and be able to use your three-inch phone to even engage the Internet. And so all of that magic is made possible by this incredible thing call a recommender system second thing is conversational AI. You're seeing chat bots and website customer service, even live customer service being now supported by AI, conversational AI has an opportunity to enhance the customer service on the one hand. On the other hand, supplement for a lot of labor shortage. And then the third is this groundbreaking piece of work as related to Transformers that led to natural language understanding breakthrough. But within it, is this incredible thing called large language models, which embeds human knowledge because it's been trained and so much data. And we recently announced Megatron 530B. And it was a collaboration we did with Microsoft, the foundation of – I think they call it Turing. And this language model and others like it, like open AI, GPD 3 are really transformative and they take an enormous amount of computation. However, the net result is a pre-trade model that is really quite remarkable. Now we're working with thousands of start-ups, large companies that are building who are using the public cloud. And so it's driving a lot of demand for us in the public cloud. I think we have now 10,000 AI inception startups that are working with us and using NVIDIA AI, whether it's on-prem or in the cloud, it saves money, because the computation time is significantly reduced. The quality of service is a lot better and they could do greater things. And so that's driving AI in the cloud. And so all of these different factors, whether it's just the industrial recognition of the importance of AI, the transformative nature of these new AI models recommender systems, large language models, conversational AI. The thousands of companies around the world that are using NVIDIA AI in the cloud -- driving public cloud demand, all of these things are driving our Data Center growth. And so we expect to see Data Center demand remain strong. Operator: Next, we'll go to Tim Arcuri with UBS. Your line is open. Tim Arcuri: Thank you very much. I had a question about this $500 million impact for July and whether it's more supply related or demand related. And that's because most others in semis are sort of setting this China stuff, in particular, is more of a logistics issues, so more of a supply issues, but the language Colette you were using in your commentary side of lower sell-through in gaming and sort the absence of sales in Russia, to me that sounds a little more demand which would make sense in the context of this new freeze on hiring that you have. So, I ask because if it's supply related, then you could argue that it's not perishable and really just timing. But if demand related that might never come back and it to be the beginning of a falling night. So, I wonder if you can sort of walk through that for me? Thanks. Colette Kress: Thanks, Tim, for the question. Let me try and bet here on the China and Russia, two very different things. The current China lockdowns that we are seeing interestingly has implications to both supply and demand. We have seen challenges in terms of the logistics throughout the country, things going in out of the country. It puts a lot of pressure on just logistics that were already under pressure. From a demand perspective, it has also been head from the gaming side. You have very large cities that are in full lockdown, focusing really on other important things for the citizens there. So, it's impacting our demand. We do believe that they will come out of COVID and the demand for our products will come back. We do believe that will occur. The supply will sort it out. It's very difficult to determine how. Now, in the case of Russia, we're not selling to Russia. That's something that we had announced earlier last quarter. But there were plans and Russia has been a part of our overall company revenue of probably about 2% of our company revenue historically and a little larger percentage when you look at our Gaming business. I hope that helped. Operator: Next, we'll go to Ambrish Srivastava with BMO. Your line is now open. Ambrish Srivastava: Hi. Thank you very much Colette and Jensen. I actually really appreciate it that you called out demand from those two companies, it feels like it's healthy to say demand is a problem, so refreshing to hear that. I had a question on the second half and it relates to both Data Center as well as Gaming. So, last couple of times you have talked publicly, you have made comments that your visibility into the Data Center has never been better. So, I was wondering if you just take out the Russia impact, is that still true, all the orders that you have been getting there intact and you did say that business will see a strong momentum. I just want to make sure that statement of confidence you have made stays? And then on Gaming, Colette, do we expect second half to be up year-over-year just based on the guide for second quarter? It seems like it could be up sequentially but may not return to year-over-year growth in Q3. Thank you. Jensen Huang: Yes. Ambrish, thanks for the question. On first principles, it should be the case that our visibility of Data Centers is vastly better, vastly better than a couple of years ago. And the reason for that is several. One, if you recall a couple two, three years ago, deep learning and AI was starting to accelerate in the most computer science deep companies in the world with CSPs and hyperscalers. And -- but just about everywhere else, it was still quite nascent. And there was a couple of reasons for that. Obviously, the understanding of the technology is not as pervasive at the time. The type of industrial use cases for artificial intelligence requires labeling of data that's really quite difficult. And then now with Transformers, you have unsupervised learning and other techniques, zero-shot learning that allows us to do all kinds of interesting things without having to have human-labeled data. We even have synthetic generated data with Omniverse that helps customers do data generation without having to label data, which is either too costly or, quite frankly, oftentimes impossible. And so now, the knowledge and the technology has evolved to a place that most of the industries could use artificial intelligence at a fairly effective way and in many industries rather transformative. And so I think, number one, we went from clouds and hyperscalers to all of industries. Second, we went from training-focused to inference. Most people thought that inference was going to be easy. It turns out the inference is by far the harder. And the reason for that is because there are so many different models and there are so many different use cases and so many quality of service requirements, and you want to run these inference models in a small of a footprint as you can. And so when you scale out, the number of users that use the service is really quite high. So using acceleration, using NVIDIA's platform, we could inference any model from computer vision to speech to chemistry to biology, you name it. And we do it so quickly and so fast that the cost is very low. And so the more acceleration you do, the more money you will save. And that, I think, that wisdom is absolutely true. And so the second dimension is training to inference. The third dimension is that we now have so many different types of configurations of systems that we can go from high-performance computing systems all the way to cloud to on-prem to edge. And then the final concept is really this industrial deployment now of AI that's causing us to be able to in just about every industry, find growth. And so as you know, our cloud and hyperscalers are growing very, very quickly. However, the vertical part, vertical industries, which is financial services and retail and telco and all of those vertical industries have also grown very, very nicely. And so, in all of those different dimensions, our visibility should be a lot better. And then starting a couple of years ago, adding the Mellanox portfolio to our company, we're able to provide a lot more solution-oriented end-to-end platform solutions for companies that don't have the skills and don't have the technical depth to be able to stand up these sophisticated systems. And so, our networking business is growing very, very nicely as well. Operator: Next, we'll go to Harlan Sur with JPMorgan. Your line is open. Harlan Sur: Hi, good afternoon. Thanks for let me ask the question. I just want to maybe just ask this question a little bit more directly. So, it's good to see the team being able to drive – navigate the dynamic supply chain environment, right? You look strong sequential growth in data center in April, here in the July quarter, even with some demand impact from Russia, right? And so, as we think about the second half of the year, cloud spending is strong, and it's actually, I think, accelerating. You're getting ready to ramp H100 later in the year. Mellanox, I think, is getting more supply as you move through the year. And in general, I think previously, you guys were anticipating sequential supply and revenue growth for the business through this entire year. I understand the uncertainty around gaming, but does the team expect continued sequential growth in data center through the remainder of the year? Jensen Huang: Either one of those answers -- the answer is yes. We see a strong demand in data center, hyperscale to cloud computing to vertical industries. Ampere is going to continue to scale out. It's been qualified in every single company in the world. And so, after two years, it remains the best universal accelerator on the planet, and it's going to continue to scale out in all these different domains and different markets. We're going to layer on top of that, a brand-new architecture Hopper. We’re going to layer on top of that brand-new networking architectures. Quantum 3, CX-7, BlueField 3 and we have increasing supply. And so, we're looking forward to an excellent quarter next quarter again for data centers and going into the second half. Operator: Next, we'll go to Chris Caso with Raymond James. Your line is open. Chris Caso: Yes, thank you. Wonder if you could speak a little bit about the purchase obligations, which seemed like they were up again in the quarter. And how that – was that a function of longer-dated obligations or a higher magnitude of obligations? And maybe you could just speak to supply constraints in general. You've mentioned a couple of times in the call, about continued constraints in the networking business. What about the other parts of the business? Where are you still constrained? Colette Kress: Yes. So let me start here, and I'll see if Jensen wants to add more of that. Our purchase obligations, as well as our prepaid have two major things to keep in mind. One, for the first time ever, we are prepaying to make sure that we have that supply and those commitments long term. And additionally, on our purchase obligations, many of them are for long lead time items that are a must for us to procure to make sure that we have the products coming to market. A good percentage of our purchase commitments is for our Data Center business, which you can imagine, are much larger systems, much more complex systems and those things that we are procuring to make sure we can feed the demand both in the upcoming quarters and further. Areas in terms of where we are still a little bit supply constrained are networking. Our demand is quite strong. We've been improving it each time. But yes, we still have demand -- excuse me, supply concerns with networking still. Is there others that you want to add on, Jensen? Jensen Huang: No, I thought you were perfect. That's perfect. Operator: Our final question comes from Aaron Rakers with Wells Fargo. Your line is open. Aaron Rakers: Yes, thanks for fitting me in. And most of my questions around Gaming and Data Center have been answered. But I guess I'll ask about the Auto segment. While it's still small, clearly, you guys sound confident in that business starting to see "significant sequential growth" into this next quarter. I'm wondering if you could help us kind of think about the trajectory of that business over the next couple of quarters? And I think, in the past, you've said that, that should start to really inflect higher as we move into the second half of the year. Just curious if you could help us think about that piece of the business? Jensen Huang: Several data points. We are just starting. We have just started shipping Orin in the first quarter of shipping production Orin. Orin is a robotics processor. It's designed for a software-defined robotic car or robotic pick and placer or a robotic mover, logistics mover. We've been designed into 35 car and trucks and robo taxi companies and more others, if you include logistics movers and last-mile delivery systems and farming equipment and the number of design wins for Orin is really quite fantastic. Orin is a revolutionary processor. And it's designed as a, if you will, a Data Center on a chip. And it is the first Data Center on a chip that is robotic, processes sensor information, it's safe, it has the ability to be rather resilient as confidential computing. It is designed to be secure, designed to be all those things because these data centers are going to be everywhere. And so Orin is really a technological marvels in production. We experienced very likely the lowest auto quarter in some time for some time. And the reason for that is because over the next six years-or-so, we have $11 billion and counting of business that we've secured estimated. And so I think it's a fairly safe thing to say now that Orin and our autonomous vehicle and robotics business is going to be our next multibillion dollar business. It's on its way surely there. The robotics and autonomous systems and autonomous machines, whether they move or not move, but AI systems that are at the physical edge is surely going to be the next major computing segment. It is surely going to be the next major Data Center segment. We've been working in this area, as you know, for a decade. We have a fair amount of expertise in this area. And Orin is just one example of our work here. We have four pillars to our strategy for autonomous systems. Starting from the data processing and the AI training part of it, to train robotics AIs; second, to simulate robotics AIs, which is omniverse; third, to the memory of the robotics AI otherwise known as mapping; and then finally, the actual robotics application and the robotics processor in the system, and that's where Orin goes. But Orin is just one of our four pillars of our robotics strategy and the next wave of AI. And so I am really optimistic and really enthusiastic about the next phase of the computer industry's growth. And I think a lot of it is going to be at the edge. A lot of it's going to be about robotics. Operator: Thank you. I'll now turn it back over to Jensen Huang for any additional closing remarks. Jensen Huang: Thanks, everyone. The full impact and duration of the war in Ukraine and COVID lockdowns in China is difficult to predict. However, the impact of our technology and our market opportunities remain unchanged. The effectiveness of deep learning AI continues to stand. The transformer model, which led to the natural language understanding breakthroughs is being advanced to learn patterns with great spatial, sequential and temporal complexity. Researchers are creating transformer models that are revolutionizing applications from robotics to drug discovery. The effectiveness of deep learning AI is driving companies across industries to adopt NVIDIA for AI computing. We're focused on four major initiatives. First, ramping our next generation of AI infrastructure chips and platforms, Hopper GPU, BlueField DPU, NVLink, InfiniBand, Quantum InfiniBand, Spectrum Ethernet Networking. And all this to help customers build their AI factories and take advantage of new AI breakthroughs like transformers. Second, ramping our system and software industry partners to launch Grace, our first CPU. Third, ramping Orin, our new robotics processor and nearly 40 customers building cars, robo taxis, trucks, delivery robots, logistics robots, farming robots to medical instruments. And fourth, with our software platforms, adding new value to our ecosystem with NVIDIA AI and NVIDIA Omniverse and expanding into new markets with new CUDA acceleration libraries. These initiatives will greatly advance AI. And while continuing to extend this most impactful technology of our time to scientists in every field and companies in every industry. We look forward to updating you on our progress next quarter. Thank you. Operator: This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is David, and I will be your conference operator today. At this time, I'd like to welcome everyone to NVIDIA's Fourth Quarter Earnings Call. Today's conference is being recorded. All lines have been placed on mute to prevent any background noise. After the speakers’ remarks, there will be a question-and-answer session. [Operator Instructions]. Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's Conference Call for the Fourth Quarter of Fiscal 2022. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the first quarter of fiscal 2023. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, February 16, 2022, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. We had an excellent quarter with revenue up 53% year-on-year to $7.6 billion. We set records for total revenue as well as for Gaming, Data Center and Professional Visualization. Full year revenue was a record $26.9 billion, up 61%, compounding the prior year's growth of 53%. Starting with Gaming. Revenue of $3.4 billion rose 6% sequentially and was up 37% from a year earlier. Fiscal year revenue of $12.5 billion was up 61%. Gaming has become the top entertainment medium that continues to show strong momentum. Just last month, Steam hit a record 28 million concurrent users, up 50% in two years. Record desktop revenue in the quarter was led by our growth in our GeForce RTX 30 Series products with continued strength in the high end. At CES, we announced the RTX 3050 GPO, which hit retail in late January, bringing NVIDIA RTX and AI technologies to more mainstream audiences. Laptop gaming revenue also set a record, driven by the ramp of the new GeForce RTX 3070 Ti and 3080 Ti GPUs, which were also announced at CES. These leverage our fourth-generation Max-Q technology to enable quiet thin and light gaming laptops. All in, we announced over 160 new laptop designs for NVIDIA and tier architecture RTX 30 Series GPUs. These include a number of studio systems targeting the tens of millions of creators driving the future of design, innovation and virtual world. In addition to supporting the new RTX 30 Series GPUs, Studio laptops future support for NVIDIA software with Omniverse, Tundus and Broadcast. Availability of our gaming products in the channel remains low. NVIDIA RTX ecosystem continues to expand with over 30 new RTX games and applications added this quarter, including blockbuster hit like Battlefield 2042, Grand Theft Auto, Call of Duty: Vanguard and God of War. In addition, several new titles support NVIDIA Reflex for low-latency impact. Our GPUs are capable of cryptocurrency mining, so we have limited visibility into how much of this impacts our overall GPU demand. Nearly all desktop NVIDIA Ampere architecture GeForce GPU shipments are light cache rate to help direct GeForce supply to gamers. Cryptomining processor revenue was $24 million, which is included in OEM and other. We continue to expand the NVIDIA GeForce NOW cloud gaming ecosystem with new hit titles, including EA's Battlefield IV and Battlefield V. At CES, we announced a partnership with Samsung to integrate GeForce NOW in its smart TVs starting in Q2 of this year. This follows last month's beta release of the GeForce NOW for LG smart TVs. In addition, we teamed up with AT&T to bring GeForce NOW to 5G mobile devices in the U.S. We also added our first GFN data center in Canada. Moving to pro visualization. Q4 revenue was $643 million was up 11% sequentially and up 109% from a year ago. Fiscal year revenue of $2.1 billion was up 100%. Sequential growth in the quarter was driven by a shift to higher-value workstation and the continued ramp of our NVIDIA healthcare architecture. We believe strong demand is fueled by continued build-outs for hybrid work environment as well as growth in key workloads, including 3D design, AI and rendering. For example, Sony Pictures ImageWorks is using NVIDIA RTX to accelerate ray-tracing for rendering-related applications. Motion is using NVIDIA RTX for AI to assist in predictive maintenance of the vehicles. And Duke Energy is using NVIDIA RTX for AI and VR to map, view and maintain energy facilities. NVIDIA Omniverse enterprise software entered general availability. And while it's still in early days, customer feedback so far has been very positive, with multiple significant enterprise licensees already signed. In addition to software licenses, Omniverse also drives computing opportunity for NVIDIA RTX in laptops, workstations and on-prem servers and the cloud. Omniverse can be used by individuals for free and by enterprise teams via software subscriptions. At CES, we made the free version of Omniverse for individual's general availability. Omniverse allows creators with RTX GPUs to connect leading 3D design applications to a single scheme and superset their work with AI and physics. We also announced early access to Omniverse Cloud, which adds one-click capability to collaborate with other artists, whether across the room or across the globe. For digital twin applications, we announced the Isaac Autonomous Mobile Robot platform using Omniverse and securely orchestrated and cloud delivered with the platform optimizes operational efficiency and accelerate deployment from logistics remods. It consists of several NVIDIA AI technologies and SDKs, including data for high-precision mapping, metropolis or situational awareness and reop for real-time route optimization. Moving to automotive. Q4 revenue was $125 million, declined 7% sequentially and 14% from the year ago quarter. Fiscal year revenue of $566 million was up 6%. We have just started shipments of our Orion-based product platform and expect to return to sequential revenue growth in Q1 with more meaningful inflection in the second half of the fiscal year and momentum building into calendar 2023 [Technical Difficulty] I will now hand it over to Jensen to provide more color on this morning's automotive news. Jensen Huang: Thanks, Colette. Earlier today, we announced a partnership with Jaguar Land Rover to jointly develop and deliver fleets of software-defined cars. Starting in 2025, all new Jaguar and Land Rover vehicles will have next-generation automated driving systems, plus AI-enabled software and services built on the NVIDIA DRIVE platform. DRIVE Orin will be the AI computer brain running our DRIVE AV and DRIVE IX software. And the DRIVE Hyperion sensor network will be the central nervous system. This new vehicle architecture will enable a wide spectrum of active safety, automated driving and parking systems. Inside the vehicle, the system will deliver AI features, including driver and occupant monitoring and advanced visualization of the vehicles surroundings. We are very much looking forward to partnering with Thierry Bolloré, JLR's CEO, and his team to reinvent the future of luxury cars. Our full stack end-to-end approach is a new business model that offers downloadable AV and AI services to the fleet of JLR vehicles with a shared software revenue stream for both companies over the life of the fleet. This partnership follows the template of our announcement with Mercedes-Benz. Our shared software revenue opportunity with both OEMs will scale with the size of their NVIDIA-powered fleet, which, combined, can exceed 10 million cars over a decade. Colette, back to you. Colette Kress: Thanks, Jensen. Moving to Data Center. Record revenue of $3.3 billion grew 11% sequentially and 71% from a year earlier. Fiscal year revenue of $10.6 billion was up 58%. Data center growth in the quarter was once again led by our compute products on strong demand for NVIDIA AI. Hyperscale and cloud demand was outstanding, with revenue more than doubling year-on-year. Vertical Industries also posted strong double-digit year-on-year growth led by consumer Internet companies. The flagship NVIDIA A100 GPU continue to drive strong growth. Inference-focused revenue more than tripled year-on-year. Accelerating inference growth has been enabled by widespread adoption of our Triton and France server software, which helps customers deliver fast and scalable AI in production. Data center compute demand was driven by continued deployment of our Ampere architecture-based product for fast-growing AI workloads such as natural language processing and deep learning recommendation systems as well as cloud executing. For example, Block Inc., a global leader in payment, uses conversational AI in its Square Assistant to schedule appointments with customers. These AI models are trained on video GPUs in AWS and perform inference 10x faster on the AWS GP service and on our CPUs. Social media company Snap used NVIDIA GPUs and Merlin deep recommendator software to improve inference cost efficiency by 50% and decrease latency to 2x. For the third year in a row, industry benchmarks show that NVIDIA AI continues to lead the industry in performance. Along with partners like Microsoft Azure, NVIDIA such records in the latest benchmarks for AI training across 8 popular AI workloads, including computer vision, natural language processing, recommendation systems, reinforcement learning and detection. NVIDIA AI was the only platform to make submissions across all benchmarks and use cases, demonstrating versatility as well as our performance. The numbers show performance gains on our A100 GPUs of over 5x in just 2 months, thanks to continuous innovations across the full stack in AI algorithms, optimization tools and system software. Over the past 3 years, they saw performance gains of over 20x powered by advances we have made across our full stack offering GPUs, networks, systems and software. The leading performance of NVIDIA AI is sought after by some of the world's most technically advanced companies. Meta Platforms unveils its new AI supercomputer research, SuperCluster, with over 6,000 A100 GPUs moved to an NVIDIA -- Meta's early benchmarks showed its system can train large natural language processing models 3x faster and run computer vision jobs 20x faster than the prior system. In a second phase later this year, the system will expand to 16,000 GPUs that Meta believes will deliver 5x of mixed precision AI performance. In addition to performance at scale, Meta cited extreme reliability, security, privacy and flexibility to handle a wide range of AI models as its key criteria for the system. We continue to broaden the reach and ease the adoption of NVIDIA AI into vertical industries. Our ecosystem of NVIDIA-certified systems expanded with Cisco and Hitachi -- which joined Dell, HewlettPackard Enterprise, Insper, Lenovo and Supermicro, among other sever manufacturers. We released version 1.1 of our NVIDIA AI Enterprise software, allowing enterprises to accelerated annual workloads on VMware, on mainstream IT infrastructure as well. And we expanded the number of system integrators qualified for NVIDIA AI Enterprise. Forrester Research in its evaluation of Enterprise AI infrastructure providers recognized NVIDIA in the top category of leaders. An example of a partner that's helping to expand our reach into enterprise IT is Deloitte, a leading global consulting firm, which has built its center for AI computing on NVIDIA DGX Superpod. At CES, we extended our collaboration to AV development, leveraging our own robust AI infrastructure and Deloitte's team of 5,500 system integration developers and 2,000 data scientists to architect solutions for truly intelligent transportation. Our networking products posted strong sequential and year-over-year growth, driven by exceptional demand across use cases ranging from computing, supercomputing and enterprise to storage. adopters-led growth driven by adoption of our next-generation products and higher-speed deployments. While revenue was gated by supply, we anticipate improving capacity in coming quarters, which should allow us to serve with significant customer demands we're seeing. Across the board, we are excited about the traction we are seeing with our new software business models, including NVIDIA AI, NVIDIA Omniverse and NVIDIA DRIVE. We are still early in the software revenue ramp. Our pipelines are building as customers across the industry seek to accelerate their pace of adoption and innovation with NVIDIA. Now let me turn it back over to Jensen for some comments on Arm. Jensen Huang: Thanks, Colette. Last week, we terminated our efforts to purchase Arm. When we entered into the transaction in September 2020, we believe that would accelerate Arm's focus on high-performance CPUs and and help Arm expand into new markets, benefiting all our customers in the entire ecosystem. Like any combination of pioneers of important technologies, our proposed acquisition spurred questions from regulators worldwide. We appreciated the regulatory concerns. For over a year, we worked closely with SoftBank and Arm to explain our vision for Arm and reassure regulators that NVIDIA would be a worthy steward of the Arm ecosystem. We gave it our best shot, but the headwinds were too strong, and we could not give regulators the comfort they needed to approve our deal. NVIDIA's work in accelerated computing and our overall strategy will continue as before. Our focus is accelerated computing. We are on track to launch our Arm-based CPU, targeting giant AI and HPC workloads in the first half of next year. Our 20-year architectural license to Arm's IP allows us the full breadth and flexibility of options across technologies and markets. We will deliver on our 3-chip strategy across CPUs, GPUs and DPUs. Whether x86 or Arm, we will use the best CPU for the job. And together with partners in the computer industry, offer the world's best computing platform to tackle the impactful challenges of our time. Back to you, Colette. Colette Kress: Thanks, Jensen. We're going to turn to our P&L and our outlook. For the discussion of the rest of the P&L, please refer to the CFO commentary published earlier today on our Investor Relations lean. Let me turn to the outlook for the first quarter of fiscal 2023. We expect sequential growth to be driven primarily by Data Center. Gaming will also contribute to growth. Revenue is expected to be $8.1 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 65.2% and 67%, respectively, plus or minus 50 basis points. GAAP operating expenses are expected to be $3.55 billion, including the Arm write-off of $1.36 billion. Non-GAAP operating expenses are expected to be $1.6 billion. For the fiscal year, we expect to grow non-GAAP operating expenses at a similar percent as in fiscal 2022. GAAP and non-GAAP other operating, other income and expenses are both expected to be an expense of approximately $55 million, excluding gains and losses on nonaffiliated investments. Non-GAAP tax rate are expected to be 11% and 13%, plus or minus 1%, excluding discrete items. Capital expenditures are expected to be approximately $350 million to $400 million. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight upcoming events for the financial community. We will be attending the Morgan Stanley Technology, Media and Telecom Conference in person on March 7. We will also be hosting a virtual Investor Day on March 22, alongside the GPU Technology Conference. This will follow Jensen's opening keynote, which we invite you to tune into. Our earnings call to discuss the results for our first quarter of fiscal 2023 is scheduled for Wednesday, May 27. We will now open the call for questions. Operator, will you please poll for questions? Operator: Thank you. [Operator Instructions]. We'll take our first question from Toshiya Hari with Goldman Sachs & Company. Your line is open. Toshiya Hari: Great. Thank you so much for taking the question. Jensen and Colette, I wanted to ask about Data Center. Colette, based on your guidance, you're probably guiding Data Center growth on a year-over-year basis to accelerate into the April quarter. You talked about hyperscale cloud growing more than 2x and enterprise verticals growing strong double digits in the January quarter. Can you kind of speak to the drivers for April and perhaps speak to visibility into the second half of the fiscal year as well in Data Center? Thank you. Jensen Huang: Sure. Colette Kress: I'll start, and I'll turn it over to Jensen. For Q1, our guidance can include an acceleration of Data Center from where we left in terms of Q4. We will have growth across several of our market platforms within Q1, both Data Center, Gaming and probably a couple of others. But yes, there is expected to be accelerated growth as we move into Q1. I'll turn it over to Jensen to talk about the drivers that we see for the quarter and also for the full year. Jensen Huang: Yes. We have several -- first of all, Toshiya, great to hear from you. We have several growth drivers in data centers. There's hyperscale, public cloud, enterprise core and enterprise edge. We're seeing growth across the entire spectrum. There are several different use cases that are particularly exciting, large language models -- language, understanding models triggered by the invention of transformers, which is probably one of the most important AI models that's been invented in some time. And conversational AI used for customer service, chat bots, a whole bunch of customer service applications. It can be web-based. It could be point-of-sale base. It could be cloud-based. Recommender systems, deep learning-based recommender systems are making groundbreaking improvements. And cloud graphics, all of the work that we're doing and putting rendering or putting simulations up in the cloud, cloud gaming, Android cloud gaming, are really driving adoption in the cloud. And so many different use cases across all of the different platforms in data centers. Operator: Next, we'll go to C.J. Muse with Evercore ISI. Christopher Muse: Yes. Good afternoon. Thank you for taking the question. I guess another question on the data center side. Curious if you can speak to supply constraints on the wafer side and whether that played a role in terms of capping revenues in the January quarter and how you see that becoming less of a headwind for you as you proceed through the year? Colette Kress: Thanks, C.J., for the question. I'll start on the data center supply. As we discussed last quarter and discussed today, we still have some supply constraints across some of our businesses. Networking in the Data Center business has been supply constrained. We're improving every single day. And we do expect to improve supply each quarter as we enter into fiscal year '23 here. So that is probably the key area within our Data Center. But from time to time, there can be other focused on do -- so I'll turn the rest of the question to Jensen in terms of how about the rest of the year as well. Jensen Huang: Yes. Colette captured it well. We are supply constrained. Our demand is greater than our supply. As you know, our data center product line consists of GPUs and mix, Bluefield DPUs, Quantum and spectrum switches, HGX, if you will, system component, meaning that the entire motherboard or the entire GPU board is delivered in combination because it's so complicated. And so we have products that span a broad reach of use cases for data centers from training of AI models to inferencing at very large scale, to universal GPUs for public cloud, industry standard servers, commodity servers for enterprise use and supercomputing systems that use InfiniBand and quantum switches. And so the application space is quite broad. We saw demand constrained pretty much across the entire range. Our operations team did a fantastic job this year, both in executing in all of these complicated products, but also in expanding our supply base. We expect supply to improve each and every quarter going forward. And this quarter, this coming quarter, the Q1 -- the April quarter is, based on guidance that Colette just made, is consistent with an increasing supply base. We expect to still be demand constrained, but our supply base is going to increase this quarter, this next quarter and pretty substantially in the second half. Operator: Next, we'll go to Joe Moore with Morgan Stanley. Joe Moore: Great. Thank you. I wonder if you could talk a little bit more about Grace now that the strategy kind of separated from the acquisition of Arm. The -- what are your aspirations there? Is it going to be primarily oriented to the DGX and HX Systems business versus merchant chips? Just how are you thinking about that opportunity long-term? Jensen Huang: Yes. Thanks, Joe. We have a multiple-arm projects ongoing in the company from - connected from devices to robotics processors such as the new Orin that's going into autonomous vehicles and robotic systems and industrial automation, robotics and such. Orin is doing incredibly well. It started production. And as we mentioned earlier, it's going to drive an inflection point starting in Q2, but accelerating through Q3 and the several years after as we ramp into all of the electric cars and all of the robotic applications and robotaxis and such. We also have Arm projects with the CPU that you mentioned, Grace. We have Grace, and we surely have the follow-ons to Grace, and you could expect us to do a lot of CPU developments around the Arm architecture. One of the things that's really evolved nicely over the last couple of years is the success that Arm has seen in hyperscalers and data centers. And it's really accelerated and motivated them to accelerate the development of higher-end CPUs. And so you're going to see a lot of exciting CPUs coming from us. And Grace is just the first example. You're going to see a whole bunch of them beyond that. But our strategy is accelerated computing. That's ultimately what we do for a living. We, as you know, love it where there's any CPUs. If it's an x86 from any vendor. So long as we have a CPU, we could connect NVIDIA's platform to it and accelerate it for artificial intelligence or computer graphics, robotics and such. And so we love to see the expansion of CPU footprints, and we're just thrilled that Arm is now growing into robotics and autonomous vehicles and cloud computing and supercomputing and in all these different applications, and we intend to bring the full spectrum of NVIDIA's accelerated computing platform to NVIDIA Arm CPUs. Operator: Next, we'll go to John Pitzer with Credit Suisse. John Pitzer: Just on the inventory purchase obligations, I think this was the fourth quarter in a row where you've seen greater than 30% sequential growth and is the first quarter where that number is now eclipsing kind of your quarterly revenue guidance. And so I guess I'm trying to figure out to what extent is this just a reflection of how tight things are across the semi industry? To what extent is this the poker tale of kind of how bullish you are on future demand? And relative to your commentary, that supply starts to get better throughout the year, should we expect that number to start to level off? Or as the mix moves more to data center and more to longer cycle times, more complicated devices should that number continue to grow? Jensen Huang: The factors, the drivers that you mentioned in the supply chain, we expanded our supply chain footprint significantly this year to prepare us for both increased supply base and supply availability in each one of the quarters going forward, but also in preparation for some really exciting product launches. As mentioned, Orin ramping into autonomous vehicles is brand new. This is the inflection point of us growing into autonomous vehicles. This is going to be a very large business for us going forward. It was already mentioned, Grace is a brand-new product that has never been on NVIDIA's road map. And we already see great success with customers who love the architecture of it and desperately in need of the type of capability that Grace brings. And this should be a pretty exciting year for new product launches. And so we're preparing for all of that laying the foundation for us to bring all those exciting products to the marketplace. Operator: Next, we'll go to Tim Arcuri with UBS. Timothy Arcuri: Obviously, there's a lot more talk from you about software. And I think it's still kind of a little bit of a black box for live investors. And I know, Jensen, that you've talked about software as a medium to basically open up new markets. But I'm wondering maybe if you can sort of quantify how big the software licensing revenue is today and maybe when you might start to break it out like you did data center, which really got the stock moving in a huge, huge way. Jensen Huang: Yes. NVIDIA is a software-driven business. Accelerated computing is a software-driven business. It starts from recognizing what domain of applications we want to accelerate and can accelerate and then building an entire stack from the processor to the system to the system software, the acceleration engines and potentially even the applications itself, like the software that we were mentioning earlier, NVIDIA DRIVE, NVIDIA AI and NVIDIA Omniverse. These are applications that sit on top of system software and are really valuable to the marketplace. The way to think about our software licensing -- so we've always been a software-driven business. But for the very first time, we have packaged licensable software on -- available to customers. The way that we license software for NVIDIA AI Enterprise is per node of server. There's some 20 million, 25 million servers that are installed in the world today in enterprises, not including clouds. We believe that every single server in the future will be running AI software. And we would like to offer an engine that enables enterprises to be able to use the most advanced the most trusted, the most utilized AI engine in the world. And so that is essentially the target market, if you will, for NVIDIA AI. The NVIDIA Omniverse is targeting -- is designed for creators contributing content to a virtual world and connect it to robots that are contributing to content in a virtual world. And so it's based on connections. There are 40 million designers and creators around the world. There are going to be hundreds of millions of robots. Every single car will essentially be a robot someday. And those are connections that will be connected into a digital twin system like Omniverse. And those are -- so the Omniverse business model is per connection per year. And in the case of NVIDIA DRIVE, we share the economics of the software that we deliver, if it's AB software or parking software or cabin-based AI software, whatever the licensing is or whatever the service, if it's an upfront license, we share the economics of that. If it's a monthly service subscription, we share the economics of that. But basically, for the cars that we are part of, that we're developing, the end-to-end service, we will get the benefits of the economics of that for the entire life of the fleet of the car. And so you could imagine, with 10 million cars, with a modern car lifetimes of 10 to 20 years, the economics and the market, the installed opportunity is quite high. And so our business opportunity is based on those factors. But our software business really, really started several years ago with virtual GPUs, but this year was when we really stepped it up and offered for the very first time NVIDIA AI Enterprise, Omniverse and DRIVE. And so I watch the spot, I think this is going to be a very significant business opportunity for us, and we look forward to reporting on it. Operator: Next, we'll go to Vivek Arya with Bank of America. Vivek Arya: Jensen, in the past, you mentioned about 10% or so adoption rate for AI among your customer base. I was hoping you would quantify where we are in that adoption curve that you tend to differentiate between the adoption differences between your hyperscale and enterprise customers? And then kind of related to that, is there an inorganic element to your growth now that you have over $20 billion of cash on the balance sheet? How are you planning to deploy that to kind of accelerate your growth also? Jensen Huang: Yes. The applications for AI is unquestionably growing, and it's growing incredibly fast. But whether in enterprises and financial services, it could be fraud detection, in cases of consumer pointing businesses, customer service, conversational AI, where people are calling chat bots. But in the future, every website will have a chat bot, every phone number will have a chat bot, whether it's a human in the loop or not human in the loop, we'll have a chat bot. And so customer service will be heavily, heavily supported by artificial intelligence in the future. Almost every point of sales, I think, whether it's a fast food or a quick service, businesses are going to have chat bots and AI-based customer service. Retail checkouts will be supported by AI agents. And so all of this is made possible by a couple of breakthroughs, computer vision, of course, because the agents, the AIs have to make eye contact and recognize your posture and such, recognize speech, understand the context and what is being spoken about and have a reasonable conversation with people so that you could provide good customer service. The ability to have human in the loop is one of the great things about an AI much, much more so than a recording, which obviously is not intelligent and therefore it's difficult to, if you will, call your manager or call somebody to provide services that they can't. And so the number of different applications that have been enabled by natural language understanding in customer service in just the last couple of years has grown tremendously. I think we're -- we remain early days in our adoption. It's incredible how fast it has grown and how many different applications are now possible with AI. It pretty much says that almost all future software will be written with AI or by AI. And when it's done, it will be an AI. And we see it in all these different industries. And so I'm pretty certain we're in the early innings yet of AI, and this is going to be one of the largest industries of software that we have ever known. With respect to capital, we -- as you know, we had just terminated our Arm agreement. We have a regular capital strategy process, and we'll go through that, and we'll make the best judgment about how to use our capital in helping our growth and sustaining our growth and accelerating our growth, and we'll have all of those sensible conversations during those capital allocation meetings. We're just delighted to have so much capital. And so just to put it out there. Operator: And next, we'll go to Aaron Rakers with Wells Fargo. Unidentified Analyst: This is Michael on behalf of Aaron. Can you guys talk about how the launch of the RTX 3050 is going so far? And maybe more broadly, your view of where we are in the product cycle on gaming? Jensen Huang: Thanks, Michael. Let's see. We -- RTX is an unqualified home run. RTX completely reinvented modern computer graphics. It made -- it brought forward ray tracing about a decade earlier than anybody thought possible. The combination of RTX with artificial intelligence, which enabled this technology we call DLSS, is able to not only do a ton more computation using our processors, but also engage the powerful Tensor for processors that we have in our GPUs to generate images, beautiful images. RTX is being adopted by just every game developer on the planet now. It's being adopted by just about every design tool on the planet now. And if not for RTX, Omniverse wouldn't be possible. We wouldn't be able to do physically based path tracing and simulate sensors like radars and LiDARs and ultrasonics and of course, cameras and simulate these cameras physically and still be able to deliver the type of performance that we deliver. And so RTX was a game changer for the industry. It reset modern computer graphics. And it was an enabler for us to build an entire new platform from Omniverse. We're about, I think, about 1/3 of the way through upgrading an installed base that is growing. You know that video games is now the world's largest gaming genre. And Steam over the last 2 years has grown by 50%. The number of concurrent players on Steam has grown tremendously. And in just the last couple of years, a brand-new game store from Epic came on, and it's already a multi-hundred million dollar business. I think it's close to $1 billion that they're doing incredibly well. I'm so happy to see it. And so the overall gaming market is growing and it's growing quite nicely. But the thing that, in addition to resetting computer graphics for our entire installed base, the growing of our installed base because gaming is growing. There are a couple of other growth dynamics that are associated with GeForce and RTX that's really quite brand new. One of them is hybrid work. This is a permanent condition. And we now are seeing across the board people who are designers and creators now have to set up essentially a new workstation or new home workstation design studio so that they could do their work at home. In addition, the creative economy, the digital economy, the creative economy is really, really doing fantastically because everything has to be done in 3D now. Print ads are done in 3D. So 2D print is done in 3D. Video is done in 3D. In live video broadcast video, the millions of influencers now augment their broadcast with rich augmented reality and 3D graphics. And so 3D graphics is now not just for video games and 3D content, it's actually used now for all forms of digital content creation. And so RTX has all of these different drivers working behind it, and we're definitely in the early innings of RTX. Operator: Next, we'll go to Stacy Rasgon with Bernstein Research. Stacy Rasgon: So you said that the growth in the next quarter is about $450 million, give or take, driven by Data Center. Can you give us some feeling for how that growth is being driven by units versus pricing versus mix and how those drivers might differ between Gaming and Data Center, if at all, for Colette? Colette Kress: It's really early in the quarter to determine, Stacy, our exact mix that we will have based on the unit and an ASP -- our overall growth quarter-over-quarter going into Q1 will be driven by data center primarily. We will see a little bit of growth there in gaming. I think that's important to understand that even after Q4 holiday moving into Q1, we'll still probably see growth in gaming, which is different in terms of what we've seen seasonally. We will probably have growth in automotive as well sequentially between Q4 and Q1. There are still some areas that are so constrained. We are working again to try and improve that for every quarter going forward, but that's how you should look at our earnings for Q1 primarily from Data Center. Operator: Next, we'll go to Harlan Sur with JPMorgan. Harlan Sur: Congratulations on the solid results and execution. The networking connectivity portfolio addition has been pretty solid for the NVIDIA team, especially in enabling scaling of your GPU systems, improving connectivity bottlenecks in yours and your customers' accelerated compute platforms. So in a year where spending is growing 30%. You've got a strong networking upgrade cycle, which is good for your NIC products and just continued overall good attach rates, if the team can unlock more supply, will the networking connectivity business grow in line or faster than the overall Data Center business this year? And then for Jensen, have you driven synergies between Mellanox's leadership in networking connectivity? And for example, leveraging their capabilities for your internally developed NVLink connectivity and switching architectures? Jensen Huang: Yes, absolutely. If not for the work that we did so closely with Mellanox, the scalability of DGX and DGX Super Pine and the research supercomputer that was just installed in Meta would just not be possible. The concepts of overlapping networking and compute, moving some -- moving computing into the fabric, into the network, the work that we're doing with Synchronoss and Precision Timing so that we could create Omniverse computers that obey the laws of physics and space time, these things are just simply not possible. The work that we're doing to bring cloud-native secure multi-tenancy to supercomputing wouldn't have been possible. The number of innovations, that are countless. And so I am so thrilled with the combination and so through what the work the Mellanox team are doing. We've accelerated road maps as a result of the combination that we could leverage a much larger base of chip design. BlueField's road map has been accelerated probably by about a year. The switch in -- the quantum switch and the spectrum switch, the SerDes are absolutely world-class, shared between Ethernet and InfiniBand and NVLink, absolutely the best servers in the world. And so the list of opportunities or the list of combination benefits is really quite countless. And so I'm super thrilled with that. With respect to networking growth, we should be growing. If we weren't supply constrained, we should be growing faster than overall CSP growth. And the reason for that -- the reason for that is twofold. The first is because the networking leadership position of Mellanox, Mellanox is highly heavyweight in the upper end of networking, where the adoption of higher-speed networks tends to move. And so it's sensible that as new data centers are built, the first preference is to install it with higher-speed networking than the last-generation networking. And Mellanox's networking technology is unambiguously world class. The second reason is because the areas where the overall NVIDIA is strong has to do with the areas that are growing quite fast, which related to artificial intelligence or cloud AI and such. And so those different applications are growing faster than the core. And so it would be sensible that we have the opportunity as we expand our supply base to continue to grow faster than CSPs overall. Operator: Our next question will come from Matt Ramsay with Cowen. Matthew Ramsay: Yes. Jensen, I maybe wanted to expand on some of the things that you were just speaking about in your last answer with respect to the Data Center business. It's not often maybe ever that you have both x86 server vendors having new big platform upgrades in the same year, which will probably happen later this year. There's a lot going on there, PCIe, some CXL stuff. I wonder if you could talk a bit about your Data Center business broadly and what you feel might be memory and I/O constrained currently that these systems might unlock for you both in the cloud and enterprise side, but also in the DGX business. Jensen Huang: Yes. Thanks, Matt. The -- there are several bottlenecks, and let me just highlight some of them. One of the largest bottlenecks is memory speed. And memory speed, that's the reason why we use the fastest memories in the world, HBM and GDDR, et cetera, et cetera. We are the largest consumers of the fastest memories in the world and not even by -- there's not -- with no close second that I know. And so our consumption of fast memories is important to the work that we do. The second is networking performance. It is the reason why we have the fastest networks. It is also the reason why we have the most fastest networks in any system. We will have, for example, 8 InfiniBand at the highest speeds connected right into HGX or DGX server. And so the work that we do in GPU direct memory, RDMA, the work that we do with GPU direct storage, the work that we do with in-network computing and all reductions and moving data around inside the network is absolutely world-class. This is an area that we are just -- I am just incredibly proud. All of that is so that we could be less bottlenecked by the CPU. Remember, inside our DGX system is on CPU and 8 GPUs. And the fundamental goal is to offload as much as we can and utilize the resources that we have as much as we can. This year, we expect a transition in PCIe Gen 4 to Gen 5. We are constrained on Gen 4. We'll be constrained on Gen 5, but we're used to that. And that's something that we're very good at. And we'll continue to support Gen 4 well through next year, maybe well through the next couple of years. And all of the installed base of Gen 4 systems that are going to be all over the world, and we'll take advantage of Gen 5 as much as we can. But we have all kinds of new technologies and strategies to improve the throughput of systems and avert the bottlenecks that are there. Operator: Our final question comes from the line of Raji Gill with Needham & Co. Rajvindra Gill: Yes. Congrats on the good quarter and guide. Colette, question on the gross margin and to Jensen's point about really creating a software business driven by Omniverse, DRIVE and Enterprise. When you're kind of contemplating your margin profile over the next couple of years, how do we think about that? Is it really going to be driven by an increasing mix of software as a percentage of your revenue over time? Is there more margin upside on the hardware side in terms of some of your segments? The software opportunity is very exciting, but I'm just curious how that would translate to your kind of more of a longer-term margin profile. Colette Kress: Yes. Thanks for the question on gross margin and the long term. When we think about the long-term gross margin, we have incorporated software in many of our platforms even today, meaning our high-value platforms in data center or [indiscernible] of our business have really helped us with our gross margins to this point, and we've done a really solid job of managing that and the growth over the years. I believe these businesses will continue to be a growing opportunity for us, but now also with the ability to package up -- So as that scales with our Enterprise customers, in the Data center and with our already procured deals, a lot of work we've got a great opportunity in the future and [indiscernible] margin -- so we're going to work on that. We've set the stage for having been able to package it up to be able to sell it separately to create the business model, to create the partners that are helping us sell it. But yes, we do believe this will be a driver in the long term. Operator: Thank you. I'll now turn it back over to Jensen Huang for closing remarks. Jensen Huang: Thanks, everyone. The tremendous demand for our computing platforms, NVIDIA RTX, NVIDIA HPC and NVIDIA AI drove a great quarter, capping a record year. Our work propels advances in AI, digital biology, climate sciences, gaming, creative design to autonomous vehicles and robotics and some of today's most impactful fields. Our open computing platform optimized across the full stack, architecture for data center scale is adopted by customers globally from cloud to core to edge and robotics. I am proud of the NVIDIA operations team as we make substantial strides in broadening our supply base to scale our company and better serve customer demand. And this year, we introduced new software business models with NVIDIA AI Enterprise, NVIDIA Omniverse and NVIDIA DRIVE. NVIDIA DRIVE is a full stack end-to-end platform that serves the industry with AV chips, data center infrastructure for AI and simulation, mapping and the autonomous driving application service. Our data center infrastructure is used by just about anybody building AVs, robotics robotaxis, shuttles and trucks. EV companies have selected our Orin chip across the world. And our partnership with Mercedes-Benz and Jaguar Land Rover has opened up a new software and services business model for millions of cars for the life of the fleet. NVIDIA Omniverse is a world simulation engine that connects simulated digital worlds to the physical world. Omniverse is a digital twin, a simulation of the physical world. The system can be a building a factory, a warehouse, a car, a fleet of cars, a robotic factory orchestrating a fleet of robots building cars that are themselves robotic. Today's Internet is 2D and AI is in the cloud. The next phase of Internet will be 3D and AI will be connected to the physical world. We created Omniverse to enable the next wave of AI where AI and robotics touches our world. Omniverse can sound like science fiction, but there are real-world use cases today. Hundreds of companies are evaluating Omniverse. We can't wait to share more of our progress at next month's GTC, learn about new chips, new computing platforms, new AI and robotic breakthroughs and the new frontiers of Omniverse. Hear from the technologists of Deloitte, Epic Games, Mercedes-Benz, Microsoft, Pfizer, Sony, Visa, Walt Disney, Zoom and more. This GTC promises to be our most exciting developers conference ever. We had quite a year, yet nothing makes me more proud than the incredible people who have made NVIDIA one of the best companies to work for and the company where they do their lives' work. We look forward to updating you on our progress next quarter. Thank you. Operator: This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is Saydie, and I will be your conference operator today. At this time, I would like to welcome everyone to the NVIDIA's Third Quarter Earnings Call. [Operator Instructions] Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the third quarter of fiscal 2022. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the fourth quarter and fiscal year 2022. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, November 17, 2021, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. Q3 was an outstanding quarter with revenue of $7.1 billion and year-on-year growth of 50%. We set records for total revenue as well as for Gaming, Data Center and Professional Visualization. Starting with Gaming. Revenue of $3.2 billion was up 5% sequentially and up 42% from a year earlier. Demand was strong across the board. While we continued to increase desktop GPU supply, we believe channel inventories remain low. Laptop GPUs also posted strong year-on-year growth, led by increased demand for high-end RTX laptops. NVIDIA RTX technology is driving our biggest ever refresh cycle with gamers, and continues to expand our base with creators. RTX introduced groundbreaking real-time ray tracing and AI-enabled super resolution capabilities, which are getting adopted at an accelerating pace. More than 200 games and applications now supports NVIDIA RTX, including 125 with NVIDIA DLSS. This quarter alone, 45 new games shipped with DLSS. And NVIDIA Reflex Latency Reducing Technology is in top esports titles, including Valorant, Fortnite, Apex Legends and Overwatch. In addition, the Reflex ecosystem continues to grow with Reflex technology now integrated in almost 50 gaming peripherals. NVIDIA studio for creators keeps expanding. Last month at the Adobe MAX Creativity Conference, Adobe announced two powerful AI features for Adobe Lightroom and the Lightroom Classic, accelerated by NVIDIA RTX GPUs. In addition, several of our partners launched new studio systems, including Microsoft, HP and ASUS. We estimate that a quarter of our installed base has adopted RTX GPUs. Looking ahead, we expect continued upgrades as well as growth from NVIDIA GeForce users, given rapidly expanding RTX support and the growing popularity of gaming, esports, content creation and streaming. Our GPUs are capable of crypto mining, but we don't have visibility into how much this impacts our overall GPU demand. In Q3, nearly all of our Ampere architecture gaming desktop GPU shipments were lite hash rate to help steer GeForce supply to gamers. Crypto mining processor revenue was $105 million, which is included in our OEM and other. Our cloud gaming service, GeForce Now, has two major achievements this quarter. First, Electronic Arts brought more of its hit games to the server. And second, we announced the new GeForce Now RTX 3080 membership tier, priced at less than $100 for six months. GeForce Now membership has more than doubled in this last year to over 14 million gamers for streaming content from 30 data centers in more than 80 countries. Moving to Pro Visualization. Q3 revenue of $577 million was up 11% sequentially and up 144% from the year ago quarter. The sequential rise was led by mobile workstations with desktop workstations also growing, as enterprises deployed systems to support hybrid work environment. Building on the strong initial ramp in Q2, Ampere architecture sales continue to grow, leading verticals, including media and entertainment, healthcare, public sector and automotive. Last week, we announced general availability of Omniverse Enterprise, a platform for simulating physically accurate 3D world and digital twins. Initial market reception to Omniverse has been incredible. Professionals at over 700 companies are evaluating the platform, including BMW, Ericsson, Lockheed Martin and Sony Pictures. More than 70,000 individual creators have downloaded Omniverse since the open beta launch in December. There are approximately 40 million 3D designers in the global market. Moving to Automotive. Q3 revenue of $135 million declined 11% sequentially and increased 8% from the year ago quarter. The sequential decline was primarily driven by AI cockpit revenue, which has negatively been impacted by automotive manufacturers supply constraints. We announced that self-driving truck start-up, Kodiak Robotics; auto maker, Lotus; autonomous bus manufacturers, QCraft; and EV start-up, WM Motor, have adopted NVIDIA DRIVE Orin platform for their next-generation vehicles. They join a large and rapidly growing list of companies adopting and developing on NVIDIA DRIVE, including auto OEMs, Tier 1 suppliers, NAVs, trucking companies, mobile taxis and software start-ups. Moving to Data Center. Record revenue of $2.9 billion grew 24% sequentially and 55% from the year ago quarter with record revenue across both hyperscale and vertical industries. Strong growth was led by hyperscale customers, fueled by continued rapid adoption of Ampere architecture Tensor Core GPUs for both internal and external workloads. Hyperscale compute revenue doubled year-on-year, driven by the scale out of natural language processing and recommendator models and cloud computing. Vertical industry growth was also strong, led by consumer Internet and broader cloud providers. For example, Barco Cloud deployed NVIDIA GPUs for its launch of AI services, such as tech analysis, speech recognition, computer vision and anomaly detection. We continue to achieve exceptional growth and influence, which again outpaced our overall Data Center growth. We have transitioned our lineup of infant-focused processes to the Ampere architecture, such as the A30 GPU. We also released the latest version of our Triton Inference Server software, enabling compute-intensive inference workloads such as large language models to scale across multiple GPUs and nodes with real-time performance. Over 25,000 companies worldwide use NVIDIA AI inference. A great new example is Microsoft Teams, which has nearly 250 million monthly active users. It uses NVIDIA AI to convert speech to text real time during video calls in 28 languages in a cost-effective way. We reached three milestones to help drive more mainstream enterprise adoption of NVIDIA AI. First, we announced the general availability of NVIDIA AI Enterprise, a comprehensive software suite with AI tools and frameworks that enables the hundreds of thousands of companies running NVIDIA, running vSphere to virtualize AI workloads on NVIDIA-certified systems. Second, VMware announced a future update to vSphere with Tanzu that is fully optimized for NVIDIA AI. When it's combined with NVIDIA AI enterprise, enterprises can efficiently manage cloud-native AI development and deployment on main stream data center servers and clouds with existing IT tools. And third, we expanded our launch cloud program globally with ethernet as our first digital infrastructure partner. NVIDIA LaunchPad is now available in nine locations worldwide, providing enterprises with immediate access to NVIDIA software and infrastructure to help them prototype and test data science and AI workloads. LaunchPad features NVIDIA-certified systems and NVIDIA DGX systems running the entire NVIDIA AI software stack. In networking, revenue was impacted as demand outstripped supply. We saw momentum toward higher speed and new generation products, including ConnectX-5 and 6. We announced the NVIDIA Quantum-2 400 gigabit per second end-to-end networking platform, consisting of the Quantum-2 switch the ConnectX-7 network adapter and the BlueField-3 DPU. The NVIDIA Quantum-two, which is available from a wide range of building infrastructure and system vendors around the world. Earlier this week, the latest top 500 list of supercomputers showed continued momentum for our full stack computing approach. NVIDIA's technologies accelerate over 70% of the systems Muslims on the including over 90% of all new systems and 23 of the top 25 most energy-efficient systems. Turning to GTC. Last week we hosted our GPU Technology Conference, which had over 270,000 registered attendees. Jensen's keynote has been viewed 25 million times over the past eight days. While our Spring GTC is focused on new chips and systems, this edition focused on software, demonstrating our full computing stack. Let me cover some of the highlights. Our vision for Omniverse came to life at GTC. We significantly expanded this ecosystem and announced new capabilities. Omniverse replication is an engine for producing data to train robots, replicating augment real-world data with massive, diverse and physically accurate synthetic data sets to both accelerate development of high-quality, high-performance AI across computing demand. NVIDIA Omniverse Avatar is our platform for generating interactive AI avatars. It connects several core NVIDIA SDKs including switch AI, computer vision, natural language understanding, recommendation engines and simulation. Applications including automated customer service, virtual collaboration and content solution. Replicator ad avatar joined several other announced features and capabilities for Omniverse, including AI, AR, VR and simulation-based technologies. We introduced 65 new and updated software development page, bringing our total to more than 150 serving industries from gaming and design to AI, cybersecurity, 5G and robotics. One of the STKs is our first four licensed AI model, NVIDIA Riva, for building conversational AI applications. Companies using Riva during the open beta include RingCentral for video conference live captioning and Pig An for customer service chatbots. NVIDIA Riva Enterprise will be commercially available early next year, for launch. We introduced the NVIDIA's NeMo Megatron optimized for training large language models on NVIDIA DGX SuperPOD infrastructure. This combination brings together production-ready, enterprise-grade hardware and software in both vertical industries, develop language and industry-specific dropbox, personal systems, content generation and summarization. Early adopters include SiDi, JD.com and VinBrain. We unveiled BlueField DOCA 1.2, the latest version of our GPU programming lender with new cybersecurity capabilities. DOCA is to our GPUs as CUDA is to our GPUs. It enables developers to build applications and services on top of our BlueFiled DOCAs. Our new capabilities make BlueField the ideal platform for the industry to build their own zero trust security platforms. The leading cybersecurity companies are working with us to provision their next-generation firewall service on BlueField, including Checkpoint, Juniper, Borgne, F5, Palo Alto Networks and VMware. And we released Clara Holoscan, a edge AI computing platform for medical instruments to improve decision-making tools in areas such as robo-assisted surgery, interventional radiology and radiation therapy planning. Other new or expanded SDKs or libraries unveiled at GTC include ReOpt for AI optimized logistics Quantum for quantum computing, Morpheus for cybersecurity, Modulus for physical-based machine learning and Crunet Numeric, a data center scale mass library to bring accelerated computing to the large and growing Python ecosystem. All in, NVIDIA's computing platform continues to expand as a broadening set of SDK enable more and more GPU-accelerated applications and industry use cases. CUDA has been downloaded 30 million times, and our developer ecosystem is now nearing 3 million strong. The applications they develop on top of our SDK and the cloud to edge computing platform are helping to transform multitrillion dollar industries from healthcare to transportation to mental services, manufacturing, logistics and virtual. In Automotive, we announced NVIDIA DRIVE Concierge and DRIVE Chauffeur, AI software platforms that enhance a vehicle's performance, features and safety. Live Concierge build on Omniverse Avatar functioned as an AI-based in-vehicle person-assistance, but enables automatic parking, summoning capabilities. It also enhanced safety by monitoring the driver throughout the duration of the drive. DRIVE Chauffeur offers autonomous capabilities, relieving the driver of constantly having to control the car. It will also perform address to address driving when combined with DRIVE Hyperion 8 platform. For robotics, we announced Jetson AGX Orin, the world's smallest, most powerful and energy-efficient AI supercomputer for robotics, autonomous mission and embedded computing at the Edge. Built on our Ampere architecture, Jetson AGX Orin provides 6 times the processing power of its predecessor and delivers 200 trillion operations per second, similar to a GPU-enabled server that fits into the palm of your hand. Jetson AGX Orin will be available in the first quarter of calendar 2022. Finally, we revealed plans to build Earth-2, the world's most powerful AI supercomputer dedicated to confronting climate change. The system would be the climate change counterpart to Cambridge-1, the U.K.'s most powerful AI supercomputer that we built for corporate research. Earth-2 furnishes all the technologies we've invented up to this moment. Let me discuss Arm. I'll provide you a brief update on our proposed acquisition of Arm. Arm with NVIDIA is a great opportunity for the industry and customers with NVIDIA's scale, capabilities and robust understanding of data center computing, acceleration and AI. We assessed Arm in expanding their reach into data center, IOT and PCs and advanced Arm's IP for decades to come. The combination of our companies can enhance competition in the industry as we work together on further building the world of AI. Regulators at the U.S. FTC have expressed concerns regarding the transaction and we are engaged in discussions with them regarding remedies to address those concerns. The transactions has been under review by China Antitrust Authority, pending the formal case initiation. Regulators in the U.K. and the EU have declined to approve the transaction in Phase 1 of their reviews on competition concerns. In the U.K., they have also voiced national security concerns. We have begun the Phase 2 process in the EU and U.K. jurisdictions. Despite these concerns and those raised by some Arm licensees, we continue to believe in the merits and the benefits of the acquisition to Arm, to its licensees and to the industry. We believe these concerns and those raised by some Arm licensees -- we continue to believe in the merits and benefits of the ongoing acquisition. Moving to the rest of the P&L. GAAP gross margin for the third quarter was up 260 basis points from a year earlier, primarily due to higher end mix within desktop, notebook, GeForce GPUs. The year-on-year increase also benefited from a reduced impact of acquisition-related costs. GAAP gross margin was up 40 basis points sequentially, driven by growth in our Data Center Ampere architecture products, which is particularly offset by mix in gaming. Non-gaming gross margin was up 150 basis points from a year earlier and up 30 basis points sequentially. Q3 GAAP EPS was $0.97, 83% from a year earlier. Non-GAAP EPS was $1.17, up 60% from a year ago, adjusting for our stock split. Q3 cash flow from operations was $1.5 billion, up from $1.3 billion a year earlier and down from $2.7 billion in the prior quarter. The year-on-year increase primarily reflects higher operating income, particularly offset by prepayment for long-term supply agreement. Let me turn to the outlook for the fourth quarter of fiscal 2022. We expect sequential growth to be driven by Data Center and Gaming, more than offsetting a decline in CMP. Revenue is expected to be $7.4 billion plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 65.3% and 67%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $2.02 billion and $1.43 billion, respectively. GAAP and non-GAAP other income and expenses are both expected to be an expense of approximately $60 million, excluding gains and losses on non-affiliated investments. GAAP and non-GAAP tax rates are both expected to be 11%, plus or minus 1% excluding discrete items. Capital expenditures are expected to be approximately $250 million to $275 million. Further financial details are included in the CFO commentary. Other information is also available on our IR website. In closing, let me highlight upcoming events for the financial community. We will be attending the Credit Suisse 25th Annual Technology Conference in person on November 30th. We will also be at the Wells Fargo Fifth Annual TMT Summit virtually on December 1st, the UBS Global TMT Virtual Conference on December 6th, and the Deutsche Bank Virtual Auto Tech Conference on December 9th. Our earnings call to discuss our fourth quarter and fiscal year 2022 results is scheduled for Wednesday, February 16. With that, we will now open the call for questions. Operator, will you please poll for the questions. Operator: [Operator Instructions] For our first question, we have Aaron Rakers from Wells Fargo. Aaron, your line is open. Aaron Rakers: Yes. Thanks for taking the question and congratulations on the results. I guess, I wanted to ask about Omniverse. Obviously, a lot of excitement around that. I guess the simple question is, Jensen, how do you define success in Omniverse as we look out over the next, let's call it, 12 months and how do we think about the subscription license opportunity for Omniverse. I know you've talked about $40 million total 3D designers, I think that actually doubled what you talked about back in August. So I'm just curious of how we at finance line should probably think about that opportunity materializing? Jensen Huang: Yes. Thanks. Omniverse success will be defined by, number one, developer engagement, connecting with developers around the world; two, applications being developed by enterprises; three, the connection -- designers and creators among themselves. Those are the nearest term -- and I would say that in my type of definition, etc. Near term also, it should be revenues and Omniverse has real immediate applications as I demonstrated at the keynote and I'll highlight a few of them right now. One of them, of course, is that it serves as a way to connect 3D and digital design world. Think of Adobe as a world, think of the Autodesk as a world, think of Revit as a world. These are design world in the sense that people are doing things in it, they are creating things in it and they have to run database. We made it possible for these worlds to be connected for the very first time and for it to be shared like in cloud documents. That's not been possible ever before and we can now share work with each other, you can see each other's work, you can collaborate. And so in the world of remote working, Ominverse's collaboration capability is going to be really appreciated and that should happen right away. We would like to see that happen in very near term. And that drives of course, more PC sales, more GPU sales, more workstation sales, more server sales. The second use case is digital twins. And we show in these following examples of how several companies using Omniverse to create a digital twin of a city so that they could optimize radio placements and radio energy used for beamforming. You saw BMW using it for their factories. You're going to see people using it for warehouse, logistics warehouse to plan and to optimize their warehouses and deploying the robots. And so digital twin applications are absolutely immediate. And then remember, robots has several clients. There is the physical robot that you saw and a physical robot would be a self-driving car and physical robots would be the car itself, turning it into a robot, so that it could be an intelligent assistant. But I demonstrated probably the -- in my explanation, the largest application of robots in the future and its avatars. We built Omniverse Avatars to make it easy for people to integrate some amazing technology for computer vision, for speech recognition, natural language understanding, gesture recognition, facial animation and speech synthesis, recommender systems, all of that integrated into one system and running in real time. That Avatar system is essentially a robotic system and the way that you use that is, for example, with $25 million or so retail stores, restaurant, places like airports and train stations, office buildings and such, where you're going to have intelligent Avatars doing a lot of assistance. They might be doing check out, they might be doing check in, they might be doing customer support and all of that can be done with Avatars, as I've demonstrated. So the virtual robotics application, digital buys of Avatars, it is going to be likely the largest robotics opportunity. So if you look at our licensing model, the way it basically works is that inside Omniverse is one of the main users and the main users could be one of the 20 million creators or 20 million designers and the 40 million creators and designers around the world and they share Omniverse, each one of the main users would be a $1,000 per user per year. But don't forget that intelligent use or intelligent users that have been connected through Omniverse will likely be much larger as digital buyers than humans. So I mentioned 40 million, but there are 100 million cars. And these 100 million cars will have the capability to have something like in Omniverse Avatar and so those 100 million cars could be $1,000 per car per year. And in the case of the 25 million or so places where you would have a digital avatar as customer support or check out smart retail or smart warehouses or smart whatever it is, those avatars also would each individually be a new account and so they would be $1,000 per Avatar per year. And so those are the immediate tangible opportunities for us and I demonstrate the applications in related keynotes. And then of course behind all of that, call it a couple of hundred million digital agents, intelligent agents, some of them humans, some of them robots, some of them Avatars adds $1,000 per agent per year. Behind it are, NVIDIA GPUs in PC, NVIDIA GPU in cloud, and NVIDIA GPUs in Omniverse servers. And my guess would be that the hardware part of it is probably going to be about half and then the licensing part of it is probably about half of the time. So this is really going to be one of the largest graphics opportunities that we've ever seen. And the reason why it's taken so long for us to manifest is because it requires three fundamental technologies to come together, I guess four fundamentals technologies to come together. First of all, it's video graphic; second is physics simulation, because we're talking about things in world that has to be believable, so it has to obey the laws of physics; and then third is artificial intelligence, as I demonstrated and illustrated just now. And all of it runs on top of an Omniverse computer that has to do not just AI, not just physics, not just computer graphics, but all of it. And so long term why people are so excited about it is, at the highest level what it basically means is that that long-term when we engage in that, which is largely 2D today, long-term every query would be 3D and instead of just querying information, we would query and interact with people and Avatars and things and places and all of these things are in 3D. So hopefully one of these days that we will probably realize it as fast as we can every transaction that goes on to internet touches a GPU and today that's a very small percentage, but hopefully one of these days it will be a bit of a high percentage. So I hope that's helpful. Operator: For our next question, we have Mark Lipacis from Jefferies. Mark, your line is open. Mark Lipacis: Hi. Thanks for taking my question. Jensen, it seems like every year there seems to be a new set of demand drivers for your accelerated platform, accelerated processing ecosystem, there's gaming, then neural network and AI and then blockchain and then ray tracing. And five or six years ago, you guys showed a bunch of virtual reality demos, which were really exciting at your Analyst Day, excitement died down, now it seems to be resurfacing particularly with Omniverse Avatar capability and Facebook shedding light on the opportunities. So the two questions from that are, how close is your Omniverse Avatar to morphing into like a mass market technology that everybody uses daily? You talk about like -- you said that everybody is going to be a gamer, everybody is going to be a Omniverse Avatar user. And maybe the bigger picture is, is it reasonable to think about new killer app coming out every year? Is there a parallel that we should think about with previous computing markets that we could think about for the computing area that we're entering right now? Thank you. Jensen Huang: Yes. I really appreciate that. Chips are enablers, but chips don't create markets, software creates market. At this point, I explain over the years that accelerated computing is very different than general purpose computing and the reason for that is because you can't just write into compiler and compile quantum physics into a chip. I mean, it doesn't. You can't just compile Schrodinger's equation and have it distributed across multiple GPUs, multiple nodes and have a new SaaS. You just -- you can't do that for computer graphics, you can't do that for artificial intelligence, you can't do that for robotics, you can't do that for the most of the interesting applications in the world and because we really run out of steam with GPUs and that people are saying that not because it's not true, it is abundantly clear that the amount of instruction in parallel that you can squeeze out of a system is although not zero is incredibly hard, it's just incredibly hard. And there is another approach and we have been advocating accelerated computing for some time and now people really see the benefit of it, but it does requires a lot of work and yet the work basically says for every domain, for every application we have -- for every application in large domain that you'll use, you have to have a whole stack. And so whenever you want to open a new market by accelerating those applications or that domain of applications, you have to come up with a new stack and the new stack is hard, because you have to understand the application, you have to understand the algorithms, the mathematics, you have to understand computer science to distribute it across, to take something that was single threaded and make it multi-threaded and make something that we've done sequentially and make it process in parallel. You break everything, you break storage, you break networking, you break everything. And so it takes a fair amount of expertise and that's why we're saying that over the years, over the course of 30 years we have become a full-stack company, because we've been trying to solve this problem practically through decades. And so that's one. But the benefit, once you have the ability, then you can open new markets and we played a really large role in democratizing artificial intelligence and making it possible for anybody to be able to do it. Our greatest contribution is I hope when it's all said and done that we democratized scientific computing, so that researchers and scientists, computer scientists, data scientists, scientists of all kinds were able to get access to this incredibly powerful tool that we call computers to do advance research. And so every single year we're coming up with new stacks and we got a whole bunch of stacks we are working on and many of them are working on in plain sight, so that you see it coming, you just have to connect it together. One of the areas that we spoke about this time, of course, was Omniverse and you saw the pieces of it being built over time, and it took half a decade to start building Omniverse, but it built on a quarter century of work. In the case of the Omniverse Avatar, you could literally point to MERLIN, the recommender; Megatron, the language -- large language model; Riva, the speech AI, all of our computer vision AIs that have been demonstrating over the years, natural speech synthesis that we see every single year with I AM AI the opening credits, how we're using, developing an AI to be able to speak in a human way so that people feel more comfortable and more engaged with the AI. Face, eye tracking, Maxine and all of these technologies are connected together. They were all built in pieces, but we integrated it, we have the intentions of integrating it and to create what it's called Omniverse Avatar. And now you asked the question, how quickly will we deploy this? I believe Omniverse Avatar will be in drive-thrus and restaurants, fast food restaurants, check out with restaurants, in retail stores, all over the world within less than five years. And we're going to need it in all kinds of different applications, because there is such a great shortage of labor and there is such a wonderful way that you can now engage in Avatar and it could -- it doesn't make mistakes, it doesn't get tired and it's always on and we made so that it's cloud native and so when you saw the keynote, I hope you'd agree that the interaction is instantaneous and the conversational forum is so enjoyable. And so anyway, I think what you highlight is, one, accelerated computing is a full-stack challenge. Two, it takes software to open new markets. Chips can't open new markets. If you build another chip, you can steal somebody's share, but you can't open new market and it takes software to open new market. NVIDIA switch with software, and that's one of the reasons why we could integrate such large market opportunities. And then last with respect to Omniverse, I believe it's a near-term opportunity that we've be working on for some three, four, five years. Operator: For our next question, we have C.J. Muse from Evercore ISI. C.J., your line is open. C.J. Muse: Yes. Good afternoon. Thank you for taking the question. And I guess not an Omniverse question, but I guess, Jensen, I'd like your commitment that you will not use Omniverse to target the sell-side research industry. As my real question, can you speak to your Data Center visibility into 2022 and beyond? And within this outlook, can you talk to traditional cloud versus industry verticals and then perhaps emerging opportunities like Omniverse and others? Would love to get a sense of kind of what you're seeing today. And then as part of that, how you're planning to secure foundry and other supply to support that growth? Thank you. Jensen Huang: Thank you, C.J. First of all, we have secured guaranteed supply, very large amounts of it, quite a spectacular amount of it from the world's leading foundry in substrate and packaging and testing certain companies, the integral part of our supply chain. And so we have done that and feel very good about our supply situation, particularly starting in the second half of this year and going forward. I think this whole last year was a wakeup call for everybody to be much more mindful about not taking the supply chain for granted and we were fortunate to have such good partners, but nonetheless we've secured our future. With respect to Data Center, about half of our Data Center business comes from the cloud and cloud service providers and the other half comes from enterprise, what we call enterprise companies and they're in all kinds of industries. And about 1% of it comes from supercomputing centers, because so 50% or so cloud, 50% or so enterprise and 1% supercomputing centers. And we expect next year, the cloud, the cloud service providers to scale out their deep learning and their AI workloads really aggressively and we're seeing that right now. We built a really fantastic platform and -- number one. Number two, the work we've been doing with TensorRT, which has the run time that goes into the server that's called Triton is one of our best pieces of work. We're just so proud of it. And we said nearly four years ago, three-and-half years ago that Inference is going to be one of the great computer science challenges, and it's really proving to be so. And the reason for that is, because sometimes it's throughput, sometimes it's latency, sometimes interactivity on the type of models we have with Inference. It's just all over the map, it's not just computer vision or image recognition, it's all over the map. And the reason for that is there's many different types of architectures, there's so many different ways to build different applications and so the application is complicated. And finally there is just a wonderful people working. We're now on our 8th generation on that. It's adopted all over the world. Some 25,000 companies are now using NVIDIA AI and recently at GTC we announced two very, very big things. One, we remind everybody that we -- just this month before we have tried to support now just in every generation of NVIDIA GPUs, of which there are so many versions to be managing without trying how would you possibly deploy AI across the entire fleet of NVIDIA servers, NVIDIA GPU servers that are all over world and so it's almost an essential tool just to operate and take advantage of all of NVIDIA's GPU that are in datacenter. Two, we support CPUs. And so it's no longer necessary for someone to have two Inference servers. You can just have one Inference servers, because the NVIDIA version is already essential. Now everybody could just use Triton and every single server in the Data Center could be part of the Inference capacity and then we did something else that was really big deal at GTC, which is the so-called Forced Inference Library, called FIL, that basically the most popular machine learning systems in Inference models are based on trees and decision trees and boosted gradient trees and people might know it as XGBoost. And trees all over the place, in fraud detection, in recommender systems and utilized in companies all over the world, because it's just self-explanatory. You can build upon it, you don't worry about regressions. It could build bigger and bigger trees. And we -- this GTC we announced that we support that as well. And so all of the sudden, all of that workflow that runs on CPU is not only do they run on Triton, it becomes accelerated. And the last -- the next thing that NVIDIA will announce, with the tremendous interest in large language models, Triton now also supports multi-GPU and multi-node Inference, so that we could take something like an OpenAI GPT-3, NVIDIA Megatron 530B or anybody's giant model that's been developed all over the world in all these different languages, in all these different domains, in all these different fields of science and what -- in industry where we can now influence it in real time. And I demonstrated it in one of the demos, there was a question answering model that the team built and it was able to basically answer questions in real-time. And so that is just a giant venture and these are the type of workloads that's going to make it possible for us to continue to scale out and build on these, so. So back to your original question, I think next year is going to be quite a good year for Data Center. Customers are very mindful of securing their supply for their scale out and so we have a fair amount of visibility and more visibility commonly than ever at Data Center, but in addition to that, Triton is just in adoption everywhere. And then, finally, our brand-new workloads, which is built on top of AI and graphics and simulation, which is Omniverse, and we saw the examples that I gave, these are real companies doing real work and one of the areas that has severe shortages around the world is customer support, just genuine severe shortages all over the world, and we think the answer is Omniverse Avatar. And it runs in Data Center, you could easily adapt Omniverse Avatar to do drive-thrus or retail check out or customer service, and I demonstrated that with Tokyo, a parking kiosk. You can use it for a tele-operated customer service and we've demonstrated that with Maxine and we demonstrated how you could use it even for video conferencing. And then lastly, we demonstrated how we could use Omniverse Avatar for robotics, for example, to create a continues work what we call DRIVE Concierge where the car is turned into intelligent customer support, intelligent agent. I think Omniverse Avatar is going to be a really exciting driver for enterprise this next year. And so next year is going to be a pretty terrific year for Data Center. Operator: For our next question, we have Stacy Rasgon from Bernstein Research. Stacy, your line is open. Stacy Rasgon: Hi, guys. Thanks for taking my questions. I wanted to ask two of them on Data Center, both near term and then maybe a little longer term. On the near-term, Colette, you suggested guidance in the Q4 be driven by Data Center and gaming and you mentioned data center first. Does that mean that it's bigger? If you could just help us like parse the contribution of each into Q4? And then in the next year, given the commentary for the last question, again it sounds like you've got like a very strong outlook for Data Center both from hyperscale and enterprise. If I look at sort of the implied guidance you gave, Data Center for you is probably likely to grow 50% year-over-year in this fiscal year. Would it be crazy to think given all the drivers that it could grow by a similar amount next year as well? Like, how should we be thinking about that given all of the drivers that you've been laying out? Colette Kress: Okay. Thanks, Stacy, for the question. Let's first focus in terms of our guidance for Q4. Our statements that we made were, yes, about driven by revenue growth from Data Center and Gaming sequentially. We can probably expect our Data Center to grow faster than our Gaming, probably both in terms of percentage wise and in absolute dollars. We also expect our CMP product to decline quarter-on-quarter to very negligible levels in Q4. So I hope that gives you a color on Q4. Now in terms of next year, we'll certainly turn the corner into the new fiscal year. We certainly provide guidance one quarter out. We've given you some great discussions here about the opportunities in front of us, opportunities with the hyperscales, the opportunities with the verticals. Omniverse is a full stack opportunity in front of us. We are securing supply for next year, not just for the current year and Q4, to allow us to really grow into so much of this opportunity going forward. But at this time, we're going to wait until next year to provide guidance. Stacy Rasgon: Got it. That's helpful. I appreciate it. Thank you Operator: For the next question we have Vivek Arya from BofA Securities. Vivek, your line is open. Vivek Arya: Thanks for taking my question. Actually I had two quick ones. And so, Colette, you suggested the inventory purchase and supply agreements are up, I think, almost 68% year-on-year. Does that provide some directional correlation with how you are preparing for growth over the next 12 to 24 months? So that's one question. And then the bigger question, Jensen, that I have for you is, where are we in the AI adoption cycle? What percentage of servers are accelerated in hyperscale and vertical industry today and where can those ratios get to? Colette Kress: Thanks for the question. So let's first start in terms of supply or supply purchase agreement. You have noted that we are discussing that we have made payments toward some of those commitments. Not only are we procuring for what we need in the quarter, what we need next year and again we are planning for growth next year, so we have been planning that supply purchases, we are also doing long-term supply purchases. These are areas of capacity agreements and/or many of our different suppliers. We made a payment within this quarter of approximately $1.6 billion out of total long-term capacity agreement of about $3.4 billion. So we still have more payments to make and we were likely continue to be purchasing longer term to support our growth that we are planning for many years to come. Jensen Huang: Every single server will be GPU accelerated some day. Today of all the clouds and all the enterprise, less than 10%. That kind of give you a sense of where you are. In terms of the workloads, it is also consistent with that in the sense that that a lot of the workloads still only run on CPUs, which is the reason why in order for us to grow, we have to be a full stack company and we have to go find applications, we now have to find plenty of it, focus on the application that require acceleration or benefits tremendously from acceleration that if they were to get a million X speed up, which sounds insane but it's not. Mathematically I can prove it to you and historically I can even demonstrate it to you that in many areas we have seen million X speed up and has completely revolutionized those industries, computer graphics is of course one of them. Omniverse would not be possible without it. And so the work that we're doing with digital biology, protein synthesis, which is likely going to be one of the large industries of the world that doesn't exist today at all. Protein engineering and the protein economy is likely going to be very, very large. You can't do that unless you are able to get million X speed up in the simulation of protein biology. And so those are -- and not to mention some of the most imperative comps that we have to build and engage. Climate science needs million X, billion X speed up and we are at a point where we can actually tackle that. And so in each one of these cases we have performed, we have to focus our resources to go and accelerate those applications and that translates to growth. Until then, they run on GPUs. And we look at a lot of today speech synthesis and speech recognition system, it still uses fairly traditional or mixture of traditional and deep learning approaches for speech AI. NVIDIA Riva is the world's first, I believe, that is end to end deep neural network. And we've worked with many companies in helping them advance there, so that they could move their clouds to our neural-based approaches. But that's one of the reasons why we do it, so that we could provide the reference, but we can also license it to enterprises around the world, so that they could advance it for their own use cases. And so one application after another we have to get it accelerated, one domain after another we have to get it accelerated. One of the ones that we're excited about and something that we've been working on for so long is EDA, even our own industry, Electronic Design Automation, for the very first time we announced the EDA using GPU accelerated computing, whether it's because of the artificial intelligence capability, because EDA is very large combinatorial optimization program and using artificial intelligence you could really improve the design quality and design time. So we're seeing from all the major game vendors, from chip design to simulation to PCB design and optimization, design synthesis, moving toward artificial intelligence and GPU acceleration in a very significant. And then we see that with a mechanical CAD and traditional CAD application now also jumping on to GPU acceleration is getting very significant speed ups. And so I'm super excited about the work that we're doing in each one of these domains. Because every time you do it, you open up brand new market and customers that never used NVIDIA GPUs now can, because ultimately people don't buy chips, it cannot solve problems. Without a full stack, without software expertise, you can't really commence the enabling technology, what the chip provide and ultimately solve the customers' problems. Operator: Your final question comes from the line of Timothy Arcuri from UBS. Timothy, your line is open. Timothy Arcuri: Thanks a lot. Colette, I had a question about gross margin. Are there any margin headwinds maybe on the wafer pricing side that we should sort of think about normalizing out, because gross margin is pretty flat between fiscal Q2 and fiscal Q4. But I imagine that's kind of masking a strong underlying margin growth, especially as Data Center has been actually driving that growth. So I'm wondering if maybe there are some underlying factors that are sort of gating gross margin? Thanks. Colette Kress: Yes. So we have always been working on our gross margin and being able to absorb a lot of the cost changes along the way, architecture-to-architecture really. So that's always based into our gross margin. Our gross margins right now are largely stable. Our incremental revenue, for example, what we're expecting next quarter will likely align to our current gross margin levels that we finished in terms of Q3. Our largest driver always continues to be mix. We have a lot of different mix that has driven related to the high-end AI and RTX solutions, for example, and the software that is embedded in solutions have allowed us to increase our gross margin. As we look forward long-term, software if sold separately can be another driver of gross margin increases in the future. But cost changes, cost increases are -- generally been a part of our gross margin figures. Operator: Thank you. I will now turn the call over back to Jensen Huang for closing remarks. Jensen Huang: Thank you. We had an outstanding quarter. Demand for NVIDIA AI is strong with hyperscalers and cloud services deploying at scale and enterprises broadening adoption. We now help more than 25,000 companies that are using NVIDIA AI. And with NVIDIA AI enterprise software suite, our collaboration with VMware and our collaboration with Equinix to place NVIDIA LaunchPad across the world, every enterprise has an easy arm length to NVIDIA AI. Gaming and Pro Vis are surging. RTX opportunity continues to expand with the growing market of gamers, creators, designers and now professionals building home workstations. We are working hard to increase supply for the overwhelming demand this holiday season. Last week, GTC showcased the expanding universe of NVIDIA accelerated computing. In combination with AI and Data Center scale computing, the model we pioneered is on the cusp of producing million X speed ups that will revolutionize many important fields; already AI and upcoming robotics, digital biology and what I hope climate science. GTC highlighted our full stack expertise in action, built on CUDA and our acceleration libraries in data processing, in simulation, graphics, artificial intelligence, market and domain specific software is needed to solve customer problems. We also showed how software opens new growth opportunities for us. But the chips are the enablers, but it's the software that opens new growth opportunities. NVIDIA has 150 SDKs now addressed in many of the world's largest end markets. One of the major themes of this GTC was Omniverse, our simulation platform for virtual worlds and digital twin. Our body of work and expertise in graphics, physics simulation, AI, robotics and full stack computing made Omniverse possible. At GTC, we showed how Omniverse is used to reinvent collaborative design, customer service avatars and video conferencing and digital twin to factories, processing plants and even entire cities. This is just the tip of the iceberg of what's to come. We look forward to updating you on our progress next quarter. Thank you. Operator: Thank you. I will now turn over to Jensen for closing remarks. Simona Jankowski: Well, I think we just heard the closing remarks. Thank you so much for joining us. We look forward to seeing everybody at the conferences that we have planned over the next few months and I'm sure we'll talk before the end of next earnings. Thanks again, everybody. Operator: [Operator Closing Remarks]

Operator: Good afternoon. My name is Mel, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's Second Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question and answer session. [Operator Instructions] Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone. And welcome to NVIDIA's Conference Call for the Second Quarter of Fiscal 2022. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President, and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the First Quarter of Fiscal 2022. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For our discussion, the factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Form 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today August 18, 2021, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. Q2 was another strong quarter with revenue of $6.5 billion and year-on-year growth of 68%. We set records for total revenue, as well as for gaming, data center, and professional visualization. Starting with gaming, revenue was 3.1 billion, was up 11% sequentially, and up 85% from a year earlier. Demand remained exceptionally strong, outpacing supply. We are now four quarters into the Ampere architecture product cycle for gaming, and it continues to be our best effort. At COMPUTEX in June, we announced two powerful new GPUs for gamers and creators, the GeForce, RTX 3080 Ti, and RTX 3070 Ti, delivering 50% faster performance than their prior-generation with acclaimed features, such as real-time retracing, NVIDIA DLSS, AI, rendering, Reflex, and broadcast. Laptop demand was also very strong. OEMs adopted Ampere architecture GPU s in a record number of designs. From the top of the line gaming laptops to those -- to mainstream price points as low as $799 that brings the power of GeForce GPUs to gamers, students, and creators on the go. Ampere architecture-powered laptops feature our third generation Max-Q power optimization technology that enables ultrathin designs, such as the new Alienware X15, the world's most powerful sub-16-millimeter gaming laptop. NVIDIA RTX technology has reset computer graphics and spurred our biggest ever refresh cycle. Ampere's been our fastest ramping gaming GPU architecture on Steam. And the combination of Turing and Ampere RTX GPUs has only upgraded about 20% of our installed base. 80% have yet to upgrade to RTX. And the audience for global eSports will soon approach 0.5 billion people, while the number of those who live stream games is expected to reach over 700 million. The number of PC gamers on Steam is up almost 20% over the past year. More than 60 RTX games now support NVIDIA's RTX ray tracing or DLSS, including today's biggest game franchises, such as Minecraft, Fortnite, and Cyberpunk. New RTX games this quarter includes Red Dead Redemption 2, one of the top-rated games of all time, popular titles like Rainbow Six Siege and rushed, and Minecraft RTX in China with over 400 million players. To competitive gamers, NVIDIA Reflex, which includes latency, is now supported by 20 games. Let me say a few words on crypto currency mining. In an effort to address the needs of miners and direct GeForce to gamers, we increased the supply of Crypto currency Mining Processors, or CMP, and introduced low hash rate GeForce GPUs with limited Ethereum mining capability. Over 80% of our Ampere architecture base GeForce shipments in the quarter were low hash rate GPUs. The combination of crypto to gaming revenue is difficult to quantify. CMP revenue, which is recognized in OEM, was 266 million, lower than our original 400 million estimates on reduced mining profitability and we expect a minimal contribution from CMP going forward. GeForce NOW reached a new milestone this quarter, surpassing 1,000 PC games, more than any other cloud gaming service. The premium tier is available for a subscription of $10 per month, giving gamers access to RTX class performance, even on an underpowered PC, Mac, Chromebook, iOS, or Android device. Moving to pro visualization. Q2 revenue was a record 519 million, up 40% sequentially, and up 156% year-on-year. Strong sequential revenue growth was led by desktop workstations, driven by demand to outfit, design offices at home as remote work becomes the norm across industries. This is also the first big quarter of the Ampere architecture ramp for pro visualization. King verticals driving Q2 demand include automotive, public sector, and healthcare. At SIGGRAPH last week, we announced an expansion of NVIDIA Omniverse, our simulation and collaboration platform that provides the foundation of the Metaverse. Through new integrations with Blender, the world's leading open-source 3D animation tool, and Adobe, we're opening the Omniverse platform to millions of additional users. We are also collaborating with Apple and Pixar to bring advanced physics capabilities to Pixar's Universal Scene Description framework, embracing open standards to provide 3D workflows to billions of devices. Omniverse enterprise software is in the early access stage and will be generally available later this year on a subscription basis from NVIDIA's partners, including Dell, HP, Lenovo, and many others. Over 500 Companies are evaluating Omniverse Enterprise, including BMW, Volvo, and Lockheed Martin. And more than 50,000 individual creators have downloaded Omniverse since it entered open beta in December. Moving to automotive. Our Q2 revenue was 152 million, down 1% sequentially, and up 3% year-on-year Sequential revenue declines in infotainment were largely offset by growth in self-driving. Looking further out, we have substantial design winds set to ramp that we expect will drive a major inflection in revenue in the coming years. This quarter, we announced several additional wins. Self-driving startup AutoX unveiled its latest autonomous driving platform for RoboTaxis powered by NVIDIA DRIVE. The performance and safety capabilities of the software-defined NVIDIA DRIVE platform have enabled AutoX to become one of the first companies in the world to provide full self-driving mobility services without the need for a safety driver. In autonomous trucking, DRIVE's ecosystem partner, plus, signed a deal with Amazon to provide at least 1,000 self-driving systems to Amazon's fleet of delivery vehicles. These systems are powered by NVIDIA DRIVE for high-performance, energy-efficient, and centralized AI compute. An autonomous trucking startup embarked is building on NVIDIA DRIVE. The system is being developed for trucks for four major OEMs, Freightliner, Navistar International, PACCAR, and Volvo, representing the vast majority of class 8 or largest size trucks in the U.S. The NVIDIA DRIVE platform is being rapidly adopted across the transportation industry from passenger-owned vehicles to robotaxi to trucking and delivery vehicles. We believe everything that moves will be autonomous someday. Moving to Data Center. Revenue of 2.4 billion grew 16% sequentially, and 35% from the year-ago quarter. The year-ago quarter, which was our first quarter to include Mellanox. Growth was driven by both hyperscale customers and vertical industries, each of which has record revenues. Our flagship A100 continuing to ramp across hyper-scale and cloud computing customers, with Microsoft Azure announcing general availability in June, following AWS and Google Cloud Platforms' general availability in prior quarters. Vertical industry demand was strong, with sequential growth led by financial services, supercomputing, and telecom customers. We also had exceptional growth in Inference, which reached a record more than doubling year-on-year. Revenue from Inference focussed processors includes the new A30 GPU, which provides four times the Inference performance of the T4. Customers are also turning to NVIDIA GPUs to take AI to production and shifting from CPUs to GPUs, driven by the stringent performance, latency, and cost requirements of deploying and scaling deep learning AI workloads, and NVIDIA Networking products posted solid results. We see momentum across regions driven by our technology of leadership with upgrades to high-speed products, such as ConnectX-6, as well as new customer wins across cloud service providers, enterprise, and high-performance computing. We extended our leadership in supercomputing. The latest top 500 niche shows that NVIDIA technologies power 342 of the world's top 500 supercomputers, including 70% of all new systems and eight of the top 10, to help companies harness the new industrial high-performance computing revolution. We deliver a turnkey AI Data Center solution with the NVIDIA DGX SuperPOD, the same technology that powers our new Cambridge-1 supercomputer in the UK and a number of others in the top 500. We expanded our AI software and subscription offerings, making it easier for enterprises to adopt AIs from the initial development stage through to deployment and operations. We announced NVIDIA Base Command, our software-as-a-service offering for operating and managing large-scale, multi-user, and multi-team AI development workloads on DGX SuperPOD. Base Command is the operating and management system software for distributed training customers. We also announced the general availability of NVIDIA Fleet Command, our managed edge AI software-as-a-service offering. Fleet Command helps Companies solve the problem of securely deploying and managing AI applications across thousands of remote locations, combining the efficiency and simplicity of central management with the cost performance and data sovereignty benefits of real-time processing at the edge. Early adopters of Fleet Command include some of the world's leading retail, manufacturing, and logistics companies and the specialty software Companies that work with them. The new NVIDIA Base Command and Fleet Command software and subscription offerings followed last quarter's announcements of the NVIDIA AI Enterprise software suite, which is early access with general availability expected soon. Our enterprise software strategy is supported by the NVIDIA certified system program with the server OEMs, which are bringing to market over 55 systems ready to run on NVIDIA's AI software out of the box to help enterprises simplify and accelerate their AI deployment. The NVIDIA ecosystem keeps getting stronger. NVIDIA Inception, our acceleration platform for AI startups, just surpassed 8,500 members with cumulative funding of over 60 billion and numbers in 90 countries. Inception is one of the largest AI startup ecosystems in the world. CUDA now has been downloaded 27 million times since it launched 15-years ago, with 7 million in the last year alone. TensorRT for inference has been downloaded nearly 2.5 million times across more than 27,000 Companies. And the total number of developers in the NVIDIA ecosystem now exceeds 2.6 million, up 4 times in the past 4 years. Let me give you a quick update on Arm. In nearly one year since we initially agreed to combine with Arm, we have gotten to know the Company, its business, and its people much better. We believe more than ever in the power of our combination, and the benefits it would deliver for Arm, for the UK, and its customers across the world in the era of AI. Arm has great potential. We love their business model and commit to keeping its open licensing approach. And with NVIDIA's scale and capabilities, Arm will make more IP, and sooner, for their mobile and embedded customers while expanding into Data Center, IOT, and other new markets. NVIDIA accelerates computing, which starts with the CPU. Whatever new markets are open with the CPU and our accelerated computing opportunities. We've announced accelerated platforms for Amazon Graviton, Ampere Computing, MediaTek, and more about expanding cloud computing, AI, cloud gaming, Supercomputing, Edge AI, to Chrome PCs. We plan to invest in the U.K. and we have with the Cambridge-1 supercomputer. And through ARM making U.K. a global center in science, technology, and AI. We are working through the regulatory process, although some Arm licensees have expressed concerns and objected to the transaction, and discussions with regulators are taking longer than initially thought. We are confident in the deal and that regulators should recognize the benefits of the acquisition to Arm, its licensees, and the industry. Moving to the rest of the P&L, the GAAP gross margin of 64.8% for the second quarter was up 600 basis points from the year earlier, reflecting the absence of certain Mellanox acquisition-related costs. GAAP gross margins were up 70 basis points sequentially, non-GAAP gross margins were 66.7% up 70 basis points from a year earlier and up 50 basis points sequentially, reflecting higher ASPs within the desktop, G4s, GPUs, our continued growth in high end and pure architecture products, partially offset by a mix shift within Data Center. Q2 GAAP EPS was $0.94 up 276% from a year earlier. Non-GAAP EPS was $1.04, up 89% from the year-earlier, adjusting for the 4 to 1 stock split effective this quarter. Q2 cash flow from operations was a record 2.7 billion. Let me turn to the outlook for the Third Quarter of Fiscal 2022. We expect another strong quarter with sequential growth driven largely by accelerating demand in Data Center. In addition, we expect sequential growth in each of our three other market platforms. Gaming demand is continuing to exceed supply as we expect channel inventories to remain below target levels as we exit Q3. The contribution of CMP to our revenue outlook is amongst. Revenue is expected to be 6.8 billion plus or minus 2%, GAAP and non-GAAP gross margins are expected to be 65.2% and 67% respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately 1.96 billion and 1.37 billion, respectively. GAAP and non-GAAP other income and expenses are both expected to be an expense of approximately 60 million, excluding gains and losses on equity securities. GAAP and non-GAAP tax rates are supposed to be expected 11%, plus or minus 1% excluding discrete items. Capital expenditures are expected to be approximately 200 million to 225 million. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight upcoming events for the financial community. We will be attending the following virtual events. The BMO Technology Summit on August 24th, the New Street Big Ideas in Semiconductors Conference on September 9th, the Citi Global Tech Conference on September 13, the Piper Sandler Global Technology Conference on September 14th, and the Evercore ISI Auto Check and AI Forum on September 21st. Our earnings call to discuss the third-quarter results are scheduled for Wednesday, November 17th. We will now open the call for questions. Operator, would you please pull for questions? Operator: Thank you. [Operator Instructions]. Your first question comes from the line of Vivek Arya of Bank of America. Your line is now open. You may ask your question. Vivek Arya: Thanks for taking my question. I actually had a near and longer-term question on the data center. I think near-term you mentioned the possibility of accelerating data center growth from the 35% rate. I was hoping if you could give us some more color around that confidence and visibility. And then longer-term, Jensen, we've seen a lot of announcements from NVIDIA about your Enterprise software opportunity. I honestly don't know how to model that. It sounds very promising, but how should we model it? What problem are you trying to solve? Is it cannibalizing demand you might have otherwise seen from your public Cloud customers, or is this incremental to growth? So just any guidance or any just insights into how to think about NVIDIA's Enterprise software opportunity longer-term? Thank you. Jensen Huang: Yes, thanks for the question. We are seeing accelerated -- as we've already reported that we have record revenues in both hyperscale cloud and industrial enterprise this last quarter. And we're seeing accelerated growth. The exploration in hyperscale and cloud comes from the transitioning of the cloud services providers in taking their AI applications, which are now heavily deep learning-driven into production. There were some things that we've spoken about in the past that we'll make Vinny the ideal partner to scale up with. And there are several elements of our platform. Number one, Ampere GPU, which is now a universal GPU for AI, for training, but incredibly good for [Indiscernible] it’s terrific and its throughput, it's terrific, and it's a fast response client as well. And therefore, the cost of deployment, the cost of operating the AI applications is the lowest. The second is the introduction of TensorRT, which is our optimizing compiler that makes it possible for us to compile and optimize any AI application to our GPUs. And whether its computer vision or natural language understanding conversational AI, recommender systems, the type of applications that are deploying AIs is really quite vast. And then lastly, this software Inference server that we offer is called Triton, which supports every one of our GPUs. It supports CPU s as well as GPUs still. So every internet service provider could operate their entire data center using Triton. These several things are really accelerating our growth. So the first element is the deployment and transition of deep learning AI applications into large-scale deployment. In the Enterprise, the application that is driving AI, as you know, every enterprise wants to get a race towards being a tech company and take advantage of connected clouds and connected devices and artificial intelligence to achieve it. And they have an opportunity to deploy AI services out of the edge. And in order to do so, there are several things that have to happen. First, we have to create a computing platform that allows them to do training in the IT environment that they understand, which is virtualized, which is largely managed by VMware. And our collaboration with VMware, creating a new type of system that could be integrated into Enterprise has been quite a significant effort, and it's in volume production today. The second is a server that allows the enterprise customers to deploy their AI models out to the Edge. And the AI engine, the software suite that we've been developing over the last 10 years, now has been integrated into this environment and allows the enterprises to basically run AI out of the vast. There are three elements of our stuff of product there. For instance, NVIDIA AI Enterprise. And that basically puts all of the state-of-the-art AI solvers and engines and libraries that we've industrialized and perfected over the years, have made available to Enterprise license. The second is an operating system platform called Base Command that allows for distributed Gala software development for training and developing models, and then the third is Fleet Command, which is an operating system software product that lets you operate and deploy and manage the AI models out to the Edge. These three software products, in combination with the server called NVIDIA Certified, taken out through our network of partners is our strategy to accelerate the adoption of AI by the Enterprise customers. So we're really enthusiastic about entering into the software business model. This is an opportunity that could represent, of course, tens of millions of servers, we could link all of them with the GPU accelerated, and we believe that Enterprises will be deploying and taking advantage of AI to revolutionize the industry. And using a credit traditional enterprise software licensing business model. This could represent billions of dollars of profit inputs. Operator: Thank you. The next question comes from the line of Stacy Rasgon of Bernstein. Your line is now open. You may ask your question. Stacy Rasgon: Hi, guys. Thanks for taking my questions. I wanted to go back, Colette, the sequential guidance, you gave a little bit of colour by segments. I've been looking at your gaming revenues. It's like three quarters in a row, you've been up, call it ballpark, 10% or 11%. And my understanding is that was a function of your ability to bring on supply. So I guess what does the supply issue look like as you're going from Q2 into Q3? And do you think you can still maintain that kind of sequential growth or does it dial down because I also need to -- I also would play that against your other commentary suggesting that the sequential growth -- and I assume on a dollar basis would be driven primarily by Data Center. So how do I think about the interplay within those comments of sequential growth of gaming, especially given the trajectories out of the last several quarters? Colette Kress: Yeah. Let me start and I'll let Jensen add a bit, Stacy, to your question. I guess we're providing the guidance for Q3 of 6.8 billion in revenue. Now excluding CMP, we expect our revenue to grow over 500 million sequentially. A lion's share of that sequential revenue increase will be coming from Data Center. We do expect gaming to be up slightly on a sequential basis but remember we are still supply constrained. Automotive and ProBiz are also expected to be up slightly quarter-over-quarter. And from the CMP perspective, we'll probably just have minimal amounts in Q3. So our Q3 results don't have seasonality with them for gaming and are really about the supply that we believe we can have for Q3. I'll see if Jensen wants to add any more colours. Jensen Huang: Yes, thanks for your questions. As you know RTX is a fundamental recap of computer graphics. This is a technology called ray tracing that has been the holy grail of computer graphics for quite a long time for 35 years, and our NVIDIA research for 10 years, we finally made it possible to do real-time ray tracing with RTX. RTX's demand is quite incredible, and as you know, we have a large [Indiscernible] that uses an architecture called GTX based on programmable shelters that we invented some 20 years ago. And now, we've reset the entire installed base, and Ampere is off to just the incredible starting the best-selling GPU architecture in the history of our Company. And yet, we've only upgraded some 20% -- less than 20% of our total installed base. There's another 80% of the world's PC gaming market that we have yet to upgrade to RTX. Meanwhile, the number of PC gamers in the world grew substantially. Steam grew 20% this last year. And so I think the -- we're right at the beginning of our RTX transition. Meanwhile, computer graphics has expanded into so many different new markets. RTX, we've always believed would re-invent the way that people did design. And we're seeing that happening right now as we speak as Workstations is growing faster than ever and has achieved record revenues. And at the same time, because of all of our work with cloud gaming, we now see public clouds putting in cloud graphics, whether its workstations or PC s or private gaming consoles up in the Cloud. So we're seeing strong demand in PCs, in laptops, in workstations, in mobile workstations, in the cloud. And so RTX is really doing great. Our challenge there is that demand is so much greater than supply. And again, as Colette said, [Indiscernible]. Operator: Thank you. The next question comes from the line of Matt Ramsay of Cowen. Your line is now open. Matt Ramsay: Yes. Thank you very much. Good afternoon, everybody. Before my questions, Jensen, I just wanted to say congrats on the Noyce Award, that's a big honor. For my question, I wanted to follow on Stacy's question about supply. And Colette, maybe you could give us a little bit of commentary around supply constraints in gaming in the different tiers or price tiers of your gaming cards. I'm just trying to get a better understanding of how you guys are managing supply across the different price tiers. And I guess it translates into a question of, are the gaming ASPs that we're seeing in the October quarter guidance, are those what you would call sustainable going forward, or do you feel like that mix may change as supply comes online? Thank you. Colette Kress: I'll start here. Thanks for the question on our overall mix as we go forward. First, our supply constraint in our gaming business is largely attributed to our desktop and notebook. That can mean a lot of different things from our components that are necessary to build so many of our products. But our mix is really important. Our mix as we are also seeing many of our gamers very interested in our higher-end, higher performance products. We will continue to see that as a driver about overall lifts both our revenue and can lift our overall gross margins. So there are quite a few different pieces into our supply that we have to think about, but we are going to try and make the best solutions for our gamers at this time. Operator: Thank you. For the next question, we have the line from C.J. Muse from Evercore. Your line is now open. C.J. Muse: Yes, thank you. Good afternoon. I guess a follow-up question on the supply constraints. When do you think that they'll ease? And how should we think about gaming into the January quarter vis-a-vis typical seasonality, given -- I would assume you would continue to be supply constrained. Thank you. Jensen Huang: Colette, I can take it or you can. Either one of us. Colette Kress: Go ahead Jensen and I'll follow it up if there are some other things, Jensen Huang: Okay. We're supply constraint in graphics and we're supplying constraining graphics while we're delivering record revenues in graphics. Cloud gaming is growing, cloud graphics is growing. RTX made it possible for us to address the design in the creative workstations. Historically the rendering of ray tracing and photorealistic images have largely been done on CPUs. And for the very first time, and you could actually accelerate it with NVIDIA GPUs and with RTX GPUs. And so the workstation market is really doing great. The backdrop of that of course is that people are building offices in their homes. And for many of the designers and creators of our worlds and 20 million of them, they have to create -- they have to build a workstation or an office at home as well as build one at work [Indiscernible] And meanwhile, of course, RTX has reached that -- all of our consumer graphics with the 200 million installed base of PC gamers, and it's time to upgrade. And so there's a whole bunch of reasons when choosing referenced revenues while we're on supply constraints. We have enough supply to meet our second-half Company growth plans. And next year, we expect to be able to achieve our Company's growth plans for next year. Meanwhile, we're having and are securing pretty significant long-term supply commitments as we expand into all these different marketing initiatives that we've set ourselves up for. And so I would expect that we will see a supply-constrained environment for the vast majority of next year is my guess at the moment. But a lot of that has to do with the fact that our demand is just too great. RTX is really a once-in-a-generation reset of the computer -- modern computer graphics. Nothing like this has happened [Indiscernible] computer graphics. And so the invention is really [Indiscernible] and you could see its impact. Operator: Thank you. The next question comes from the line of Harlan Sur of JP Morgan. Your line is now open. Harlan Sur: Good afternoon and congratulations on the strong results outlook and execution. The Mellanox networking franchise -- this has been a really strong and synergistic addition to the video compute portfolio. I think kind of near to midterm the team is benefiting from the transition to 200 and 400-gig networking connectivity and Cloud and Hyperscale. And then I think in addition to that, you guys are getting some good traction with the Bluefield smart tech products. Can you just give us a sense of how the business is trending year-over-year, and do you expect continued quarter-over-quarter networking momentum into the second half of this year, especially as the cloud and hyperscalers are going through a server and Capex spending cycle? Jensen Huang: Yeah, I really appreciate that question. Now, the heaviest solid growth quarter, and the Mellanox networking business have really grown incredibly. There are three dynamics happening all at the same time. The first is the transition that you're talking about. You know that the world's data center [Indiscernible] centers are users for computing costs just aggregated, which basically means with a single application is running on multiple servers at the same time. This is what makes it possible for them to scale up. The more users for an AI application or service, you just have to add more servers. And so the ease of scale-out that this aggregated computing provides also puts enormous pressure on the networking. And that Mellanox has the world's lowest latency and the highest bandwidth and performance networking on the planet. And so the ability to scale out and the ability to provide this aggregate in applications are really much, much better with Mellanox networking. So that's number one. Number two, almost every Company in the world has to be a high-performance computing Company now. You see that the cloud service providers, one after another, are building effective supercomputers. Historically was [Indiscernible] and supercomputing firms, the cloud-service providers have to build supercomputers themselves. And the reason for that is because artificial intelligence entombs gigantic models. The rate of growth of network sizes AI models buys this is doubled every two months. It's doubling not every year or 2 years, it's doubling every 2 months. And so you can imagine the size when I'm talking about trending AI models that are 100 trillion parameters large. The human brain has a 150 plus trillion synopsis, and so for nuance. And so that gives you a sense of the scale of AI models that people are developing. And so you're going to see supercomputers that are built out of Mellanox, InfiniBand, and their high-speed networking, along with NVIDIA GPU computing in more and more cloud service providers. You're also seeing it in enterprises or used in the discovery of [Indiscernible] there is our digital mileage and revolution going on as a computation statement. The large-scale computing that we're able to do now and AI, better understand biology and better understand chemistry, and bringing both of those skills into the field of Information Sciences. And so you're seeing large supercomputers growth in enterprises around the world as well. And so the second dynamic has to do with our incredibly great networking InfiniBand networking, which was the de facto standard in high-performance computing. And the third dynamic is the Data Center’s storing software. In order to orchestrate and run a data center, with just a few people essentially running their entire data center, hundreds of thousands of servers it is just one computer in front of you. That entire data center is software-defined. And the amount of software that goes into that software-defined data center running on today's GPUs is the networking stack, the storage stack, and now, because of zero trusts, the security stack. All of that is putting enormous pressure on the available computing capacity for applications, which is ultimately what data centers are designed to do. And so for the software-defined data center needs to have a place to take infrastructure software and accelerate it, to offload it, to accelerate it. And very important to isolate it from the application plan, so that intruders can't jump into the operating system with your all -- of your data center, establish of your data center. And so the answer to that is detailed, the ability to offload, accelerate, and isolate this -- the data center software infrastructure, and to fully up to all the -- your CPUs through to run what they're supposed to run, which is the application. Now, just about every data center in the world is moving towards a zero-trust model and Bluefield is just incredibly well-positioned. For these three dynamics, this aggregated computing, which needs really strong and fast networking, every Company needing high-performance computing, and then lastly, software decline in data centers going zero trusts. And so these are really important dynamics and I appreciate the opportunity to tell you all that. And you can just tell how super excited I'm about the prospects in the networking business and in the importance that they have in building modern data centers. Operator: Thank you. The next question comes from the line of Aaron Rakers of Wells Fargo. Your line is open. Aaron Rakers: Yeah. Thanks for taking my question. I think you hit on a lot of my questions around the Data Center in that last. So maybe I will just ask the kind of on a P&L basis, one of the things that I see in the results, or more importantly, the guide is you're now collect guiding over a 67% gross margin, potentially. I'm curious as we move forward, how do you think about the incremental operating gross margin upside still from here, and how you're thinking about the operating merchant leverage for the Company from here through the P&L. Thank you. Jensen Huang: Let me take that and then you could just follow up -- Colette Kress: Go forth. Jensen Huang: -- With details, that'll be great. I think at the highest level -- I really appreciate the question. At the highest level, the important thing to realize is that artificial intelligence, is the single greatest technology force that the computer industry has ever seen and potentially the world's ever seen. The automation opportunities -- automation opportunity, which drives productivity, which translates directly the cost savings to companies, is enormous. And it opens up opportunities for technology and computing Companies like it's never happened before. And let me just give you some examples. The fact that we could apply so much technology to warehouse logistics, retail automation, customer call center automation, is really quite unprecedented. The fact that we could automate truck driving and [Indiscernible] delivery, providing an automated chauffeur. Those kinds of services and benefits and products are never imaginable before. And so the size of the IT industry if you will, the industry that computer companies like ourselves are part of, has expanded from them before. And so -- the thing that we want to do is to invest as smartly, but as quickly as we can to go after the large business opportunities, where we can make a real impact. And while doing so, to do so in a way that is architecturally sensible. One of the things that is really an advantage of our Company is the nature of the way that we build products, the nature of the way that we build software, our discipline around the architecture, which allows us to be so efficient while addressing climate science, on the one hand, digital biology on the other, artificial intelligence and robotics and self-driving cars. And of course, we always talked about computer graphics and video. Using one architecture and having the ability to -- and having the discipline now for almost 30 years has given us incredible operating leverage. That's where the vast majority of our operating leverage comes from, which is architectural. The technologies are architectural, our products are architectural in that way, and the Company is even built architecturally in that way. And so hopefully, as we go after these large, large market opportunities that AI has provided us, and we do so in a smart and disciplined way, with great leverage through our architecture, we can continue to drive really great operating leverage for the Company and for our shareholders. Operator: Thank you. We have the next question, which comes from the line of John Pitzer of Credit Suisse. Your line is open. John Pitzer: Yeah. Good afternoon, guys. Thanks for letting me ask a question. I apologize for the short-term nature of the question, but it's what I get asked most frequently. I want to return to the impact of crypto or the potential impact of crypto. Colette or Jensen, is there any way to gauge the effectiveness of the low hash rate GeForce, why only 80% and not 100%? And how confident are you that the CMP business being down is a reflection of crypto cooling-off versus perhaps LHR not being that effective? And I bring it up because there's a lot of blogs out there that would suggest that there -- as much as you guys are trying to limit the ability of miners to use GeForce, there are some workarounds. Jensen Huang: Yes. There -- go ahead. Colette Kress: Let me start there and answer a couple of the questions about our strategy that we've put in place in this last couple of quarters. As you recall, what we put in place was the low hash rate cards, as well as putting industry [Indiscernible] cards. The low hash rate cards were to provide for more supply for our GeForce gamers that are out there. We articulated one of the metrics that we were looking at is what percentage of those cards in Ampere, we were able to sell with low-rate hash cards. Almost all of our cards in Ampere are low hash rates, but also we're selling other types of cards as well. But at this time, as we move forward, we're much higher than 80%, but just at the end of this last quarter, we were approximately at 80. So yes, that is moving up, so the strategy is in place and we'll continue as we move into Q3. I'll move it to Jensen here to see if he can discuss it further. Jensen Huang: There's the question about the strategy of how we're scaling GeForce supply to games. We moved incredibly fast this time with CMPs, and with our LHR settings for GeForce. And our entire strategy is about steering GeForce supply to the industry. And we have every reason to believe that because of the drive-in gaming, which is really a measure of gamers, the rate of growth of theme adoption of our GPUs, there's some evidence that that was successful. But there are somewhat reasons why it's just different this time. The first reason, of course, is that the LHR, which is new, and the speed at which we responded with CMPs, steered GeForce to fight the damage. The second is where at the very beginning of the Ampere and RTX graphics. As I mentioned earlier, RTX was a complete re-invention of computer graphics. Every evidence is that gamers are incredible -- and game developers are incredibly excited about ray tracing, this form of computer renderings. Graphics rendering is just dramatically more beautiful. And we're at the beginning of that cycle, and only 20% has been upgraded so far. We have 80% to go in a market that is already quite large and an installed base that's quite low but also growing. Last year, the gamers grew 20% and just [Indiscernible]. The third reserves that our demand is strong and our channel weak, and you can see what we're doing with the shortage of supply as quickly as we're shipping it. It's virtually all of the worlds. And then lastly, and we just have more growth drivers today because of RTX whenever. We have the biggest wave of NVIDIA laptops, just the laptops are our fastest-growing segment of computing and we have the largest wave of laptops coming. The demand for RTX and Workstation, whereas previously the Workstation market was a slow-growing market is now a fast-growing market and its achieved record. And after more than a decade of working on cloud graphics, our cloud graphics is in great demand. And so all of these segments are seeing high demand while we continue to supply them [Indiscernible] so I think the situations are very different and RTX is making a huge difference. Operator: Thank you. We have the next question, which comes from the line of Chris Casper (phon) of Raymond James. Your line is now open. Chris Casper: Thank you. Good evening. My question is about the split between the Hyperscale in the vertical customers in the Data Center business and the trends you see in each. I think in your prepared remarks, you said both would be up in the October quarter. But I'm interested to see if you're seeing any different trends there, particularly in the vertical business, as perhaps business conditions normalize and Companies return to the office, and they adjust their spending plans accordingly. Colette Kress: Yes. Let me start to outlook the question and I'll let Jensen [Indiscernible]. So far with our Data Center business, with our Q2 results, our vertical industry is still quite a strong percentage. Essentially, 50% of our data center business is going to our vertical industries. Our Hyperscale s makes up the other portion of that, slightly below the 50%. And then we also have our supercomputing business with a very small percentage of it doing quite, quite well. As we move into Q3, as we've discussed, we will see an acceleration of both our vertical industries and our Hyperscales as we move into Q3. With that backdrop, we'll see if Jensen has additional commentary. Jensen Huang: There is a fundamental difference in the Hyperscale use of HPC or AI versus the industrial use of HPC Internet. In the world of hyperscalers and Internet service providers, they're making recommendations on movies, and songs, and articles, and search results, and so on, so forth. And the improvement that I can see that deep learning and artificial intelligence large recommender systems that can provide is really working for them. In the world of industry, the reason why artificial intelligence is transformative. Recognizing that most of the things that I just mentioned earlier, it's not moving on dynamics in the world's largest industries, whether it helps healthcare or in logistics or transportation or retail. The vast majority of the reasons why, and some of the physical sciences industries, whether it's energy or transportation, and also for healthcare. The simulation of physics, the simulation of the world, was not achievable using traditional first principle simulation approaches. But artificial intelligence or data-driven approaches have completely shaken that up and put it on its head. Some examples, whether it's fusing artificial intelligence so then you could feed up the simulation or the prediction of the approaching structure or the 3D structure approaching, which was recently achieved by a couple of very important networks, it's ground-breaking. And by understanding the approaching structure, 3D structure, we understand -- that we can better understand its function and how it would adapt to other approach and other [Indiscernible] And it's a fundamental step of the process in drug discovery and that has just taken a giant leap forward. In the areas of chronic science, it is now possible to consider using data-driven approaches to create models that overcome this -- not overcome but accelerate and make it possible for us to simulate much larger simulations of Multi-Physics Geometry aware simulations, which is basically climate science. These are really important fields of work that wouldn't have been possible for another decade at least. And just as we've made possible, using artificial intelligence, the realization of real-time ray tracing in every field of science, whether its climate simulation, energy discovery, drug discovery. We're starting to see that the industry recognizing that the fusion of the first principle simulation and data-driven artificial intelligence approaches, it's going to get a giant leap up. And that is a second dynamic. The other dynamic for the industry is for the very first time they can deploy AI model out to the edge to do a better job with agriculture, to do a better job with asset protection and warehouses, to do a better job with automating retail, AI is going to make it possible for all of these types of automation to finally be realized. And so the dynamics are all very different. That last one has to do with Edge AI, which was just made possible by putting AI right at the point of data and right at the point of action because you need to be low cost, you need to be high performance, and instantly responsive, and you can't afford to stream all of the data to the cloud all the time. And so each one of them has a partially different understanding. Operator: Thank you. Your final question comes from the line of Silgan Stein (phon) of [Indiscernible] Your line is open. Silgan Stein: Great, thanks so much for taking my question. Jensen, I'm wondering if you can talk for a moment about Omniverse. This looks like really cool technology, but I tend to get very few questions from investors about it, but It looks to me like this could be a potentially very meaningful technology for you longer-term. Can you explain perhaps what capabilities and what markets this is going after? It looks like, perhaps this is going to position you very well in augmented and virtual reality, but maybe it's a different market or group of markets. It's a bit confusing to us, so if you could maybe help us understand it, I think we'd really appreciate it. Thank you. Jensen Huang: I really appreciate the question. And it's one of the most important things we're doing. The Omniverse, first of all, just what is it? Is a stimulator. It's a simulator that's physically accurate and physically based. And it was made possible because of two fundamental technologies we invented. One of them is, of course, RTX, the ability to physically stimulate light behaviour in the world, which is very fixing. The second is the ability to compute or simulate the physics of -- simulate the artificial intelligence behaviour of agents and objects inside a world. So we have the ability now to simulate physics in a realistic way and to create a new architecture that allows us to do it in the cloud, distribute it a computed way, and to be able to scale it out to a very large [Indiscernible] So the question is what would you do with such a thing. The simulator, there is a simulation of virtual worlds with portals -- we call them connectors -- portals based on an industry-standard, open standard that was pioneered by Pixar, and as we mentioned earlier, that we're partnering with Pixar and Apple to make it even more broadly adopted. It's called USD, Universal Scene Description. They're basically portals or wormholes into virtual worlds. And this virtual world will simulate -- to be simulating -- it could be a concert for consumers, it could be a theme park for consumers. In the world of industries, you could use it for simulating robots so that robots could learn how to be robots inside these virtual worlds before they're downloaded from the simulator to the real world. You could use it to simulate factories, which is one of the early works that we've done with BMW. We've got a shared GTC Factory of the Future that is designed completely in Omniverse, robots trading Omniverse with goods and materials that are its original CAD data put into the battery. The logistics plan, like an ERP system, except this an ERP system of physical grids and physical simulation simulated through this Omniverse world, and you could plan the entire factory in Omniverse. This entire factory now becomes what is called the digital twin. In fact, it could be a factor, it could be a stadium, it could be an airport, it could be an entire city, it could even include the cars. The digital twin would allow us to simulate new algorithms, new AIs, new and optimization algorithms before we deploy them into the physical world. And so what is Omniverse? Well, Omniverse is going to be an overlay, if you will, of virtual worlds, that increasingly people call the Metaverse. And we've now heard several companies talk about the Metaverse. They all come from different perspectives. Some of them from social perspectives, some are here from a gaming perspective, some of them, in our case, from an industrial and design and engineering perspective. But the Omniverse is essentially an overlay of the Internet -- an overlay of the physical world and it's going to fuse all these different worlds together long-term. And you'll be able to -- you mentioned VR, and now you'll be able to go into the Omniverse worlds using virtual reality. And so you wormhole into the virtual worlds using VR. You could have an AI or an object portal into our world using augmented reality so you could have a beautiful piece of art that you've somehow purchased and belongs to you because of [Indiscernible] and it's only enjoyed in the virtual world and you can overlay it into your physical world using AI. I'm fairly sure at this point that Omniverse or the Metaverse is going to be a new economy that is larger than our current economy. And we'll enjoy a lot of our time in the future in Omniverse and the Metaverse, and we'll do a lot of our work there, and we'll have a lot of robots. They're doing a lot of the work on our behalf. We've got the [Indiscernible] they show the results. Omniverse to us is an extension of our AI strategy, is an extension of our high-performance computing strategy, and it makes it possible for companies and industries to be able to create digital tools that simulate their physical version before they deploy it or while they are operating. Operator: Thank you. I will now turn the call over back to Mr. Jensen Huang for closing remarks. Jensen Huang: Thank you. We had an excellent quarter fueled by surging demand for NVIDIA computing. Our pioneering work in accelerated computing continues to vent graphics, scientific computing with AI. Enabled by NVIDIA accelerated computing, developers are creating the most impactful technologies of our time. From natural language understanding, invest in many systems, to autonomous vehicles, and in the logistics centers, to digital biology and climate science research, the Metaverse world that obeys the laws of physics. This quarter we announced NVIDIA Base Command and Fleet Command to develop deploy, scale, and orchestrate the AI workflow that runs on the NVIDIA AI Enterprise software suite. With our new Enterprise Software, a wide range of NVIDIA-powered systems, and a global network of system and integration partners, we can accelerate the world's largest industry as they raise the benefits from the transformative power of AI. We are thrilled to have launched NVIDIA Omniverse, a simulation platform nearly five years in the making that runs physically realistic virtual worlds and connects to other digital platforms. We imagine engineers, designers, and even autonomous machines connecting to Omniverse to create digital twin simulated worlds that help train robots, operating autonomous factories, simulate fleets and autonomous vehicles, and even predict the human impact on Earth's climate. The future will have artificial intelligence augmenting our own and the Metaverse augmenting our physical world. It will be populated by real and AI visitors, and open new opportunities for artists, designers, scientists, and even businesses, a whole new digital economy [Indiscernible] Omniverse is a platform for building the Metaverse vision. We're doing some of our best work and most impactful work in our history. I want to thank all of NVIDIA's employees for the remaining work and the exciting future we're inventing together. Thank you. See you next time. Operator: Thank you. This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is Sumitra, and I will be your conference operator today. At this time, I would like to welcome everyone to the NVIDIA's Financial Results Conference Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. [Operator Instructions] Thank you. Simona Jankowski, you may begin your conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2022. With me on the call today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2022. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 26, 2021, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. Q1 was exceptionally strong with revenue of $5.66 billion and year-on-year growth accelerating to 84%. We set a record in total revenue in gaming, data center and professional visualization driven by our best ever product lineups and structural tailwinds across our businesses. Starting with gaming. Revenue of $2.8 billion was up 11% sequentially and up 106% from a year earlier. This is the third consecutive quarter of accelerating year-on-year growth, beginning with the fall launch of our GeForce RTX 30 Series GPUs. Based on the Ampere GPU architecture, the 30 Series has been our most successful launch ever, driving incredible demand and setting records for both desktop and laptop GPU sales. Channel inventories are still leading, and we expect to remain supply constrained into the second half of the year. With our Ampere GPU architecture now ramping across the stack in both desktop and laptops, we expect the RTX upgrade cycle to kick into high gear as the vast majority of our GPU installed base needs to upgrade. Laptops continue to drive strong growth this quarter as we started ramping the Ampere GPU architecture across our lineup. Earlier this month, all major PC OEMs launch GeForce RTX 30 Series laptops based on the 3080, 39 -- 3070 and 3060 as part of their spring refresh. In addition, mainstream versions based on the 3050 and 3050 Ti will be available this summer, just in time for back-to-school starting at price points as low as 799. This is the largest ever wave of GeForce gaming laptops over 140 in total, as OEMs address the rising demand from gamers, creators and students for NVIDIA's powered laptops. The RTX 30 Series delivers our biggest generational leap in performance ever. It also features our second-generation ray tracing technology and frame rate boosting, AI-powered DLSS. The RTX is a reset for graphics with over 60 accelerated games. This quarter, we added many more, including Call of Duty, Modern Warfare, Crysis Remastered and Outriders. We also announced that DLSS is now available in Unreal Engine 4 and soon in the Unity game engine, enabling game developers to accelerate frame rates with minimal effort. The RTX 30 Series also offers NVIDIA Reflex, a new technology that reduces system latency. Reflex is emerging as a must-have feature for e-sports gamers who play competitive titles like Call of Duty: Warzone, Fortnite, Valorant and Apex Legends. We estimate that about 75% of GeForce gamers play e-sports games and 99% of e-sports pros compete on GeForce. We believe gaming also benefited from crypto mining demand, although it's hard to determine to what extent. We've taken actions to optimize GeForce GPUs for gamers, while separately addressing mining demand with Cryptocurrency Mining Processors, or CMPs. Last week, we announced that newly manufactured GeForce RTX 3080, RTX 3070 and RTX 3060 Ti graphic cards will have their Ethereum mining capabilities reduced by half and carry a low hash rate or LHR identifier. Along with the updated RTX 3060, this should allow our partners to get more GeForce cards into the hands of gamers at better prices. To help address mining demand, CMP products launched this quarter, optimized for mining performance and efficiency because they don't meet the specifications required of a GeForce GPU, they don't impact the supply of GeForce GPUs to gamers. CMP revenue was $155 million in Q1 reported as part of the OEM and other category. And our Q2 outlook assumes CMP sales of $400 million. Our GeForce NOW cloud gaming platform passed 10 million registered numbers this quarter. GFN offers nearly 1,000 PC games from over 300 publishers more than any other cloud gaming service, including 80 of the most popular free to game play games. GFN expands the reach of GeForce to billions of under powered Windows PCs, Macs, Chromebooks, Android devices, iPhones and iPads. GFN is offered in over 70 countries with our latest expansions, including Australia, Singapore and South America. Moving to Pro Vis. Q1 revenue was $372 million, up 21% both sequentially and year-on-year. Strong notebook growth was driven by a new sleek and powerful RTX-powered mobile workstations with Max-Q technology and the enterprises continue to support remote workforce initiatives. Desktop workstations rebounded as enterprise resumed the spending that has been deferred during the lockdown with continued growth likely as offices open. Key verticals driving Q1 demand include manufacturing, healthcare, automotive and media and entertainment. At GTC, we announced the coming general availability of NVIDIA Omniverse Enterprise, the world's first technology platform that enables global 3D design teams to collaborate in real-time in a shared space working across multiple software speeds. This incredible technology builds on NVIDIA's entire body of work and is supported by a large, rapidly growing ecosystem. Early adopters include sophisticated design teams and some of the world's leading companies such as BMW Group, Foster + Partners and WPP. Over 400 companies have been evaluating Omniverse and nearly 17,000 users have downloaded the open beta. Omniverse is offered as a software subscription on a per user and a per server basis. As the world becomes more digital, virtual and collaborative, we see a significant revenue opportunity for Omniverse. We also announced powerful new Ampere architecture GPUs for next-generation desktop and laptop workstation. The new RTX-powered workstations will be available from all major OEMs. Moving to Automotive. Q1 revenue was $154 million, up 6% sequentially and down 1% year-on-year. Growth in AI cockpit revenue was partially offset by the expected decline in legacy infotainment revenue. We extended our technology leadership with the announcement of the next-generation NVIDIA DRIVE Atlan SoC. Atlan will deliver an unrivalled 1,000 trillion operations per second of performance and integrate data center class NVIDIA BlueField networking and security technologies to enhance vehicle performance and safety, making it a true data center on wheels. Atlan, which targets automakers’ 2025 models, will follow the NVIDIA DRIVE Orin SoC, which delivers 254 TOPS that has been selected by leading vehicle makers for production timelines starting next year. The NVIDIA DRIVE platform has achieved global adoption across the transportation industry. Our automotive design win pipeline now exceeds 8 billion through fiscal 2027. Most recently, Volvo Cars announced that it will use NVIDIA DRIVE Orin building on our next great momentum with some of the largest automakers including Mercedes Benz, SAIC and Hyundai Motor Group. In robotaxi's we added GM Cruise to the growing number of companies adopting the NVIDIA DRIVE platform, which includes Amazon Zoox and DiDi. We’ve also had a great traction with new energy vehicle makers. Our latest wins include Faraday Future, R Auto, IM Motors and VinFast, which join previously announced wins with SAIC, NIO, XPeng and Li Auto. And in trucking, Navistar is partnered with TuSimple in selecting NVIDIA DRIVE for autonomous driving, joining previously announced robo autonomous solutions and plus. NVIDIA is helping to revolutionize the transportation industry. Our full stack software defined AV and AI cockpit platform spans silicon, systems, software and AI data center infrastructure, enabling over the air upgrades to enhance safety and the joy of driving throughout the vehicles lifetime. Starting with our lead partner Mercedes Benz, NVIDIA DRIVE can transform the automotive industry with amazing technologies delivered through a new software and services business models. Moving to data center. Revenue topped $2 billion for the first time, growing 8% sequentially and up 79% from the year-ago quarter, which did not include Mellanox. Hyperscale customers led our growth this quarter, as they built infrastructure to commercialize a AI in their services. In addition, cloud providers have adopted the A100 to support growing demand for AI for enterprises, startups and research organizations. Customers have deployed NVIDIA's A100 and DGX platforms to train deep neural networks with rising computational intensity led by two of the fastest growing areas of AI; natural language understanding and deep recommendators. In March, Google Cloud Platform announced general availability of the A100 with early customers including square for its cash application and Alphabet’s DeepMind. The A100 is deployed across all major hyperscale and cloud service providers globally. And we see strengthening demand in the coming quarters. Every industry is becoming a technology industry and accelerating investments in AI infrastructure, both through the cloud and on-premise. Our vertical industries grew both sequentially and year-on-year led by consumer internet companies. For example, NAVER, a leading internet technology company in Korea and Japan is training giant AI language models at scale on DGX SuperPOD to pioneer new services across e-commerce, search, entertainment and payment applications. We continue to gain traction in France with hyperscale and vertical industry customers across a broadening portfolio of GPOs. We had record shipments of GPUs used for inference. Inference growth is driving not just the T4, which was up strongly in the quarter, but also the universal A100 Tensor Core GPU as well as the new Ampere architecture based A10 and A30 GPUs, all excellent at training as well as inferencing. Customers are increasingly migrated from CPUs to GPUs for AI inference for two chief reasons. First, GPUs can better keep up with the exponential growth in the size and the complexity of deep neural networks and respond with the required low latency. In April's MLPerf AI inference benchmark, NVIDIA achieved the top results across every category that include computer vision, medical imaging, recommender systems, speech recognition and natural language processing. And second, NVIDIA's full stack inference platform including tritons and inference server software simplifies the complexity of deploying AI applications by supporting models from all major frameworks, and optimizing for different query types including batch, real time and streaming. Triton is supported by several partners in the cloud services, including Amazon, Google, Microsoft and Tencent. Examples of how customers use NVIDIA's inference platform include Microsoft for grammar checking in office, the United States Postal Service for real time package analytics, T-Mobile for customer service, Pinterest for image search, and GE Healthcare for heart disease detection. We also have strong results with Mellanox networking products. Like our compute business, strong growth was driven by hyperscale customers across both Ethernet and InfiniBand. We achieved key design wins and proof-of-concept trials for the NVIDIA BlueField-2 DPU, the cloud service providers and consumer internet companies. We also unveiled BlueField-3, the first DPU built for AI and accelerated computing, with support from VMware, Splunk, NetApp, Cloudflare and others. BlueField-3 is the industry's first 400 gig DPU and delivers the equivalent data center services of up to 300 CPU cores. It transforms traditional server infrastructure into zero trust environments in which every user is authenticated by offloading and isolating data center services for business applications. With BlueField-3, our DPU roadmap will deliver an unrivalled 100x performance increase over a 3-year period. As we look back at the first full year since closing the Mellanox acquisition, we are extremely pleased with how the business has performed. It has not only exceeded our financial projections, but it has been instrumental in key new platforms like the DGX SuperPOD and the BlueField DPU, enabling our data center scale computing strategy. In April, we held our largest ever GPU Technology Conference with more than 200,000 registrants from 195 countries. Jensen's keynote has over 14 million views. At GTC, we announced our first data center CPU, NVIDIA Grace, targeted at processing massive next generation AI models with trillions of parameters. The arm-based processor will enable 10x the performance and energy efficiency of today's fastest servers. With Grace, NVIDIA has a three-chip strategy with GPU, DPU and now CPU. The Swiss National Supercomputing Center and the US Department of Energy's Los Alamos National Laboratory are the first to announce plans to build Grace powered supercomputers. Grace will be available in early 2023. GTC is first and foremost for developers. We announced NVIDIA developed and optimized pre-trained model availability on the NVIDIA GPU cloud registry. Developers can choose a pre-trained model and adapt it to fit their specific needs using NVIDIA TAO, our transfer learning software. TAO fine tunes the model with customers own small dataset to get models accustomed without the cost, fine and massive data sets required to train a neural net -- a neural network from scratch. Once a model is optimized and ready for deployment, users can integrate it with an NVIDIA application framework that fits their use. For example, the NVIDIA Jarvis framework for interactive conversational AI is now generally available and used by customers such as T-Mobile and Snap and the NVIDIA Merlin framework for deep recommendators is an open beta with customers such as Snap and Tencent. With the chosen application framework, users can launch NVIDIA Fleet Command software to deploy and manage the AI application across a variety of NVIDIA GPU powered devices. For enterprise customers, we unveiled a new enterprise grade software offering available as a perpetual license or subscription. NVIDIA AI Enterprise is a comprehensive suite of AI software that speeds development and deployment of AI workloads and simplifies management of enterprise AI infrastructure. Through our partnership with VMware hundreds of thousands of vSphere customers will be able to purchase NVIDIA AI Enterprise with the same familiar pricing model that IT managers use to procure VMware infrastructure software. We also made several announcement at GTC about accelerating the delivery of both NVIDIA AI and accelerated computing to enterprises and edge users among the world's largest industries. Leading server OEMs launched NVIDIA certified systems, which are industry standard servers based on the NVIDIA EGX platform. They run NVIDIA AI enterprise software and are supported by the NVIDIA A30 and A10 GPUs. Initial customers including Lockheed Martin and Mass General Brigham. In addition, we announced the NVIDIA AI on 5G platform supported on NVIDIA EGX servers to enable high performance 5G Ram and AI applications. The AI on 5G platform leverages the NVIDIA Aerial software and the NVIDIA BlueField-2 A100 converged card which combines our GPUs and DPUs. We are teaming with Fujitsu, Google Cloud, Mavenir, Radisys and Wind River in developing solutions based on our AI on 5G platform to speed the creation of smart cities and factories, advanced hospitals and intelligent stores. Another highlight at GTC was the announcement of a broad range of initiatives to strengthen the Arm ecosystem across cloud data centers, HPC, enterprise and edge and PCs. In the cloud, we are bringing together AWS Graviton2 processors and NVIDIA DPUs to provide a range of benefits including lower costs, support for virtual game streaming experiences and greater performance for Arm based workloads. In HPC, we are bringing together an Ampere Altra CPU with NVIDIA GPUs, DPUs and NVIDIA HPC software development kit. Initial supercomputing centers deployed include Oak Ridge and Los Alamos National Labs. In the Enterprise and Edge, we're bringing together Marvell Arm-based OCTEON processors and the NVIDIA GPUs to accelerate video analytics and cybersecurity solutions. And in PCs, we are bringing together MediaTek's Arm-based processors with NVIDIA RTX GPUs to enable realistic ray trace graphics and cutting-edge AI in a new class of Arm-based laptops. On our Arm acquisition, we are making steady progress in working with the regulators across key regions. We remain on track to close the transaction within our original timeframe of early 2022. Arm's IP is widely used, but the company needs a partner that can help it achieve new heights. NVIDIA is uniquely positioned to enhance Arm's capabilities, and we are committed to invest in developing the Arm ecosystem, enhancing R&D, adding IP and turbo charging its development to grow into new markets in the data center, IoT and embedded devices, areas where it only has a light footprint or in some cases, none at all. Moving to the rest of the P&L. GAAP gross margin for the first quarter was down 100 basis points from a year earlier and up 100 basis points sequentially. Non-GAAP gross margin was up 40 basis points from a year earlier and up 70 basis points sequentially. The sequential non-GAAP increase was largely driven by a more favorable mix within data center and the addition of CMP products. Q1 GAAP EPS was $3.03, up 106% from a year earlier. Non-GAAP EPS was $3.66, up 103% from a year-ago. Q1 cash flow from operations was $1.9 billion. Let me turn to the outlook for the second quarter of fiscal 2022. We expect broad base sequential year-on-year revenue growth in all of our market platforms. Our outlook includes $400 million in CMP. Aside from CMP, the sequential revenue increase in our Q2 outlook is driven largely by data center and gaming. In data center, we expect sequential growth in both compute and networking. In gaming, with the move to low hash rate G4 CPUs, and increasing the amount of CMP products, we are making a significant effort to serve miners with CMPs and provide more GeForce cards to gamers. If there is additional CMP demand, we have supplied flexibility to support it. We believe these actions combined with strong gaming demand will drive an increase in our core gaming business for Q2. Now to look at our outlook for Q2. Revenue is expected to be $6.3 billion plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 64.6% and 66.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $1.76 billion and $1.26 billion, respectively. GAAP and non-GAAP, other income and expenses are both expected to be an expense of approximately $50 million. GAAP and non-GAAP tax rates are both expected to be 10%, plus or minus 1%, excluding discrete items. Capital expenditures are expected to be approximately $300 million to $325 million. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight that Jeff Fisher and Manuvir Das will keynote Computex on the evening of May 31 U.S time, as well as several upcoming events for the financial community. We will be virtually attending the Evercore TMT Conference on June 7, the BofA 2021 Global Technology Conference on June 9, and the NASDAQ Virtual Investor Conference on June 16. Our earnings call to discuss our second quarter results is scheduled for Wednesday, August 18. With now, we will open the call for question. Operator, would you please poll for questions. Operator: [Operator Instructions] And your first question comes from Timothy Arcuri with UBS. Timothy Arcuri: Thanks a lot. Colette, I was wondering if you can double click a little more on the guidance. I know of the 600 to 650 in growth, you said 250 is coming from CMP and both gaming and data center will be up. Can we assume that they're up about equally, so you're getting about 200 roughly from each of those? And I guess second part of that is, within data center, I'm wondering can you speak to the networking piece. It sounds like maybe it was up a bit more modestly than it's been up the past few quarters. I'm just wondering what the outlook is there. Thanks. Colette Kress: Yes. Thanks so much for the question on our guidance. So I first want to start off with, we see demand really across all of our markets. All of our different market platforms we do plan to grow sequentially. You are correct, that we are expecting an increase in our CMP. And outside of our CMP growth, we expect the lion share of our growth to come from our data center and gaming. In our data center business, right now, our product lineup couldn't be better. We have a strong overall portfolio, both for training and for inferencing and we're seeing strong demand across our hyperscales and vertical industries. We've made a deliberate effort on the gaming perspective to supply to our gamers the cards that they would like, given the strong demand that we see. So that will also support the sequential growth that we are receiving. So you are correct, that we do see it - growth sequentially coming from data center and gaming, both contributing quite well to our growth. Timothy Arcuri: Thanks a lot, Colette. Colette Kress: Oh, I didn't answer your second question, my apologies, on Mellanox. Additionally, Mellanox is an important part of our data center. It is quite integrated with our overall products. We did continue to see growth this last quarter and we are also expecting them to sequentially grow as we move into Q2. They are a smaller part of our overall data center business, but again, we do expect them to grow. Operator: And your next question comes from C.J. Muse with Evercore ISI. C.J. Muse: Yes, good afternoon. Thank you for taking the question. In your prepared remarks, I think I heard you talk about a vision for acceleration in data center as we go through the year. And as you think about the purchase obligations that you reported up 45% year-on-year, how much of that is related to long lead time data center and how should we interpret that in terms of what kind of ramp we could see in the second half, particularly as you think about perhaps adding more growth from enterprise on top of what was hyperscale-driven growth in the April quarter? Thank you. Colette Kress: Let me take the first part of your question regarding our purchasing of inventory and what we're seeing in just both our purchase commitments and our inventory. The market has definitely changed to where long lead times are required to build out our data center products. So we're on a steady stream to both commit longer-term, so that we can make sure that we can serve our customers with the great lineup of products that we have. So yes, a good part of those purchase commitments is really about those long lead times, the components to create the full systems. I will turn the second part of the question over to Jensen. Jensen Huang: What was the second part of the question, Colette? Colette Kress: Second part of the question was, what do we see in the second half as it relates to the lineup of enterprise. And we articulated in our pre-remarks regarding - that we see an acceleration. Thank you. Jensen Huang: Yes. We're seeing strength across the board in data centers, and we're seeing strengthening demand. C.J., our data center, as you know, is accelerated with a range of applications. From scientific computing, both physical and life sciences, data analytics, and classical machine learning, cloud computing and cloud graphics, which is becoming more important because of remote work. And very importantly, AI both for training as well as inferencing for classical machine learning models, like XGBoost all the way to deep learning-based models like conversational AI, natural language, understanding, recommender systems, and so on. And so we have a large suite of applications and our NVIDIA AI and NVIDIA HPC as the case, accelerate these applications and data centers. They run on systems that range from HGX for the hyperscalers to DGX for on-prem to EGX for Enterprise and Edge, all the way out to AGX autonomous systems. And this quarter, at GTC, we announced one of our largest initiatives and it's taken us several years. You've seen working on it in open -- on the open over the course of the last several years, and it's called EGX, it's our Enterprise AI platform. We're democratizing AI, we're bringing it out in cloud, we're bringing it to enterprises, and we're bringing it out to the Edge. And the reason for that is because the vast majority of the world at the automation that has to be done has data that has data sovereignty issues, or data rate issues that can't move to the cloud easily. And so we have to move the computing to their premise and oftentimes all the way up to the edge. The platform has to be secure, has to be confidential, it has to be remotely manageable. And of course, it has to be high-performance and it has to be cloud-native. And that's the built -- be built like the cloud, the modern way of doing cloud data centers. And so these stacks has to be modern on the one hand, it has to be integrated into classical enterprise systems on the other hand, which is the reason why we’ve worked so closely with VMware and accelerated VMware's operating system, data center operating system, software-defined data center, stacks on BlueField. Meanwhile, we ported NVIDIA AI, NVIDIA HPC on to VMware, so that they could run distributed large scale accelerated computing for the very first time. And that partnership, that partnership was announced at VMworld. It was announced at GTC and we're in the process of going to market with all of our enterprise partners, their OEMs, their value-added resellers, their service -- their solution integrators all over the world. And so, this is a really large endeavor and the early indications of it are really exciting. And the reason for that is because as you know, our data center business is more than 50% vertical industry enterprise already. It's more than 50% vertical industry enterprises already and then by creating this easy to adapt and easy to integrate stack, it's going to allow them to move a lot faster. And so this is the next major wave of AI. This is a very exciting part of our initiative. And it's something that I've been working on for -- we've been working on for quite a long time. And so I'm delighted with the launch this quarter at GTC. The rest of the data centers do agree to. As Colette mentioned, hyperscale demand is strengthening. We're seeing that for computing and networking. You know that the world's cloud data centers are moving to deep learning, because every small percentage that they get out of predictive inference drives billions and billions of dollars of economics for them. And so the movement towards deep learning shifts the data center workload away from CPUs, because accelerators are so important. And so hyperscale, we're seeing great traction and great demand. And then lastly, supercomputing. Supercomputer centers all over the world are building out. And we're really in a great position there to fuse for the very first-time simulation-based approaches as well as data driven based approaches what is called artificial intelligence. And so across the board, our data center is gaining momentum. And we see -- we just see great strength right now and its growing strength. And we're really set up for years of growth in data center. This is the largest segment of computing as you know, and this segment of computing is going to continue to grow for some time to come. Operator: And your next question comes from Aaron Rakers with Wells Fargo. Aaron Rakers: Yes, thanks for taking the questions. Congratulations on the results. I'm going to first slip in two of them here. First of all, Colette, I think in the past you talked about how much of your gaming install base is kind of on the pre-race ray tracing platforms are really kind of the context behind the upgrade cycle that's still part of us. That's kind of question one. And then, on the heels of the last question, I was just curious things like VMware's project model ray as we think about the BlueField-2 product and BlueField-3, how should we think about those starting to become or when should they become really material incremental revenue growth contributors for the company? Thank you. Colette Kress: So, yes, we have definitely discussed in terms of the great opportunity that we have in front of us of folks moving to our ray traced GPUs. And we're in the early stages of that. We've had a strong cycle already, but still we probably have approximately 15% moving up a little bit from that at this time. So it's a great opportunity for us to continue to upgrade a good part of that install base, not only just with our desktop GPUs, but the RTX laptops are also a great driver of growth and upgrading folks to RTX. Jensen Huang: Colette, do you want me to take the second one? Colette Kress: Yes, please. Jensen Huang: Aaron, a good -- great question on BlueField. First of all, the modern data center has to be rearchitected for several reasons. There are several fundamental reasons that makes it very, very clear that the architecture has to change. The first insight is cloud-native, which means that a data center is shared for everybody. [Indiscernible] you don't know who's coming and going and it's exposed to everybody on the internet. Number two, you have to assume that it's a Zero Trust environment because you don't know who's using it. It used to be that we have perimeter security, but those days are gone because it's cloud-native, it's remote access, it's multi tenant, it's public cloud, the infrastructure is used for internal and external applications. So number two has to be -- it has to be zero trust. The third reason is something that started a long time ago, which is software defined in every way, because you want -- you don't want a whole bunch of bespoke custom gear inside a data center, you want to avoid the data center with software. You want to be software defined. The software defined data center movement enabled this one pane of glass, a few IT managers orchestrating millions and millions of nodes of computers at one place. And the software runs what used to be storage, networking, security, virtualization and all of that -- all of those things have become a lot larger and a lot more intensive. And it's consuming a lot of the data center. In fact, the estimate depending on how you want to think about it, how much security you want to put on it, if you assume that it's a zero trust data center, probably half of the CPU cores inside the data center is running not applications. And that's kind of strange, because you created the data center to run services and applications, which is the only thing that makes money. The other half of the computing is completely soaked up running the software defined data center just to provide for those applications. And that you could imagine even accepting, if you like, as the cost of doing business. However, it commingles the infrastructure, the security plane and the application plane and exposes the data center to attackers. And so you fundamentally want to change the architecture as a result of that. To offload that software defined virtualization and the infrastructure operating system, if you will, and the security services to accelerate it because Moore's law has ended and moving software that was running on one CPU -- one set of CPUs, which is really, really good already to another set of CPUs is going to make it more effective, separating it doesn't make more effective. And so you want to offload that and take the -- take that application software and accelerated using accelerators, a form of accelerated computing. And so that's -- these things are fundamentally what BlueField is all about. And we created the processor that allows us to -- BlueField-2 replaces approximately 30 CPU cores. BlueField-3 replaces approximately 300 CPU cores, which just -- put it give you a sense of it. And BlueField-4, we're in the process of building already. And so, we've got a really aggressive pipeline to do this. Now, how big of this market, the way to think about that is every single networking chip in the world will be a smart network [indiscernible] -- it will check. It will be a programmable accelerated infrastructure processor. And that's what the DPU is, it's a data center on a chip. And I believe every single server node will have it. It will replace today's mix with something like BlueField, and it will offload about half of the software processing that's consuming data centers today. But most importantly, it will enable this future world where every single packet, every single application is being monitored in real time all the time for intrusion. And so, how big is that application? How big is that market? Just, 25 million servers a year. That's the size of the market. And we know that servers are growing, and so those give you a feeling for that. And then in the future servers are going to move out to the Edge. And all of those Edge devices will have something like BlueField. And then how are we doing? We're doing PLCs now with just about every internet company. We're doing really exciting work there. We've included it in high performance computing, so that it's possible for supercomputers in the future to be cloud-native, to be zero trust, to be secured and still be a supercomputer. And then we expect next year to have meaningful, if not significant revenues contribution from BlueField, and this is going to be a really large growth market for us. You can tell, I'm excited about this. And I put a lot of my energy into it. The company is working really hard on it. And this is a form of accelerated computing that's going to really make a difference. Operator: And your next question comes from Vivek Arya with Bank of America Securities. Vivek Arya: Thanks for taking my question. Jensen, is NVIDIA able to ring fence this crypto impact in your CMP product? So even if, let's say crypto goes away, for whatever reason, the decline is a lot more predictable and manageable than what we saw in the 2018, '19 cycle. And then kind of part B of that is, how do you think about your core PC gamer demand? Because when we see these kind of 106% year-on-year growth rate, it brings questions of sustainability. So give us your perspectives on these two topics, just how does one ring fence kind of the crypto effect? And what do you think about the sustainability of your core PC gamer demand? Thank you. Jensen Huang: Sure. Thanks a lot. First of all, it's hard to estimate exactly how much and where crypto mining is being done. However, we can only assume that the vast majority of it is contributed by professional miners, especially when the amount of mining increases tremendously like in-house [ph]. And so we created the CMP. And CMP and GeForce are not fungible. You could use GeForce for mining, but you can't use CMP for gaming. CMP is yields better and producing those doesn't take away from the supply of GeForce. And so it protects our GeForce supply for the gamers. And the question that you have is what happens when on the tail end of this? There's several things that we hope. And we learned a lot from the last time, but you never learn enough about this dynamic. What we hope is that that the CMPs will satisfy the miners at work will stay in mines, in the professional mines. And we're trying to produce a fair amount of them and we have secured a lot of demand for the CMPs and we will fulfill it. And what makes it different this time is several things. One, we're in the beginning of our RTX cycle, whereas Pascal was the last GTX. And now exactly was at the tail end of the GTX cycle, because the last GTX and it was the tail end of GTX cycle. We're at the very beginning of the RTX 30 cycle. And because we reinvented computer graphics, we reset the computer industry. And after 3 years, the entire graphics industry has followed. Every game developers need to do ray tracing, every content developer and every content tool has moved to ray tracing. And so if you lose ray tracing, these applications are so much better. And they simply run too slow on GTX's and so we're seeing a reset of the install base, if you will. And at a time when the gaming market is the largest ever, we've got this incredible install base of GeForce users. We've reinvented computer graphics and reset the install base and create an upgrade opportunity that's really exciting at a time when the market is the gaming market, the gaming industry is really large. And what's really exciting on top of that is that gaming is no longer just gaming. And it's infused into sports, e-sports. It's infused into art. It's infused into social. And so gaming is -- it has such a large cultural impact now, it's the largest form of entertainment. And I think that the experience we're going through is going to last a while. And so, one I hope that crypto will -- the CMP will steer our GeForce supply to gamers. We see strong demand and I expect to see strong demand for quite some time because of the dynamics that I described. And hopefully in the combination of those two, we'll see strong growth and through strong growth in our core gaming business through the year. Operator: And your next question comes from John Pitzer with Credit Suisse. John Pitzer: Yes. Good afternoon, guys. Thanks for let me ask the question. Jensen, I had two hopefully quick questions. First, I hearken back to the monitor you guys put out at couple of analyst days ago, the more you spend, the more you save. And you've always been very successful as you brought down the cost of doing something to really drive penetration growth. And so I'm curious with the NVIDIA Enterprise AI software stack, is there a sense that you can give us is how much that brings down the cost of deployment and AI inside the Enterprise? And do you think whether COVID lockdown related or cost related, there's pent up demand that this unlocks? And then my second question is just around government subsidies. A lot of talks out of Washington about subsidizing the chip industry, a lot of that goes towards building fabs domestically. But when I look at AI, I can't think of anything more important to maintain sort of leadership in relative to national security. How do we think about NVIDIA and kind of the impact that these government subsidies might have on either you or your customers or your business trends? Jensen Huang: The more you buy, the more you [indiscernible], there's no question about that. And the reason for that is because we're in the business of accelerated computing, we don't accelerate every application. However, for the applications we do accelerate, the acceleration is so dramatic. And because we sell a component, the entire system, the TCO, the TCO of the entire system, and all the services and all the people and the infrastructure and the energy cost has been reduced by X factors, sometimes 10x, sometimes 15x, sometimes 5x. And so the -- so when we set our mind on accelerating a certain class of applications and recently we worked on true [ph] quantum so that we could help the quantum industry, quantum computing industry it's already there simulators so that they could discover new algorithms and invent future computers, even though it won't happen until 2030. For the next 20 years, that we're going to have 15 years, we're going to have some really, really great work that we can do, using NVIDIA GPUs to do quantum simulations. We recently did a lot of work in natural language understanding in computational biology so that we could decode biology and understand how biology is to infer to understand it and to predictively improve upon it and design new proteins. Those words are so vital. And that's what accelerated computing all about. Our Enterprise software, and I really appreciate the question. Our Enterprise software used to be just about the BGP, which is virtualizing GPU inside the VMware environment, or inside the Red Hat environment and makes it possible for multiple users to use one GPU, which is the nature of Enterprise virtualization, but now with NVIDIA AI, NVIDIA Omniverse, NVIDIA Fleet Command, whether you're doing collaboration or virtual simulations for robotics and digital twins, design your factory or you're doing data analytics, learning what the predictive features are that could create an AI model, predictive model that you can deploy out at the Edge using Fleet Command. We now have an intense suite of software that is consistent with today's enterprise service agreements. It's consistent with today's enterprise business models, and allows us to support customers directly, and provide them with the necessary service promises that they expect, because they're delivering -- they're trying to build a mission critical application on top. And, more importantly, by creating this -- prioritizing our software, we provide the ability for our large network of partners, OEM partners, value added resellers, system integrators, solution providers for this large network of hundreds of thousands of IT sales professionals that we are connected to through our network, we give them a product that they can take to market. And so the distribution channel, the sales channel, VMware, the sales channel of Cloudera, the sales channel of all of our partners [indiscernible] and design, Autodesk, [indiscernible] so on so forth, all of these sales channels and all of these partners are now partners and taking our stacks to market. And we have a fully integrated system that are open to the OEM, so that they could create systems of run the stack. And it's all certified, all tested, all benchmark and, of course, very importantly, all supported. And so this new way of taking our products to market, whereas our cloud business is going to continue to grow, and that part of AI is going to continue to grow that business is direct. We sell components directly to them, we support them directly. But there are 10 of those customers in the world. For Enterprises, there are thousands industries far and wide. And so I think this -- we now have a great stack and a great software stack that allows us to take it to the world's market so that everybody could buy more and save more. Operator: And your final question comes from Stacy Rasgon with Bernstein. Stacy Rasgon: Hi, guys. Thanks for taking my questions. [Indiscernible] Colette. So Colette, last quarter you had kind of suggested that Q1 would be the trough for, I guess, for gaming as well as the rest of the company beginning in particular, and it would grow sequentially through the year. I guess given the strength we're seeing in the first half, do you still believe that that is the case? And I kind of heard you guys, I think kind of dance around that point a little bit in response to one of the other questions. But could you clarify that? Is that still your belief that that core gaming business can grow sequentially through the rest of the year? And I guess same question is for data center, especially since sounds like hyperscale is now coming back, like after a few quarters of digestion and then all of the other tailwinds you've talked about. I mean, is there any reason to think that data center itself shouldn't also grow sequentially, like through the rest of the year? Colette Kress: Yes, Stacy, thanks for the question. So I first of all start with when we talked about our Q1 results. And when we're looking at Q1, we were really discussing a lot about what we expected between Q4 and Q1. Given what we knew was still high demand for gaming. We believed we would continue to grow between Q4 and Q1, which often we don't. And we absolutely have the strength and overall demand to grow. What that then lead was, again, continued growth from Q1 to Q2 as we are working hard to provide more supply for the strong demand that we say. We have talked about that we have additional supply coming. We expect to continue to grow as we move into the second half of the year as well for gaming. Now, we only guide one quarter at a time, but our plan is to take the supply, serve the overall gamers, work on building out the channel, as we know the channel is quite lean. And so yes, we do and still expect growth in the second half of the year, particularly when we see the lineup of games, the holiday overall coming, the back-to-school, all very important cycles for us. And there's a great opportunity to upgrade this, RTX install base. Now, in terms of data center, will work in terms of our guidance here. We have growth from Q1 to Q2 planned in our overall guidance. And we do see as things continue to open up a time to accelerate in the second half of the year for data center. We have, again a great lineup of products here. It couldn't be a better lineup now that we've also added the inferencing products and the host of overall applications that are using our software that we have. So this could be an opportunity as well to see that continued growth. We will work in terms of serving the supply that we need for both of these markets. But yes, we can see definitely growth in the second half of the year. Operator: There are no further questions at this time. The CEO, Jensen Huang, I'll turn the call back over to you. Jensen Huang: Well, thank you. Thank you for joining us today. NVIDIA computing platform is accelerating. Launched at GTC, we are now ramping new platforms and initiatives. There are several that I mentioned. First, enabled by the fusion of NVIDIA RTX, NVIDIA AI and NVIDIA [indiscernible]. We built Omniverse, a platform for virtual collaboration and virtual worlds to enable tens of millions of artists and designers to create together in their own metaverse. Second, we lay the foundation to be a three check data center scale computing company with GPUs, DPUs and CPUs. Third, AI is the most powerful technology force of our time. We partner with cloud and consumer internet companies to scale out and commercialize AI powered services. And we're democratizing AI for every enterprise and every industry. With NVIDIA AGX certified systems, the NVIDIA Enterprise AI Suite pre-train models for conversational AI, language understanding, recommender systems and our broad partnerships across the IT industry, we are removing the barriers for every enterprise to access state-of-the-art AI. Four, the work of NVIDIA Clara in using AI to revolutionize genomics and biology is deeply impactful for the health care industry, and I look forward to telling you a lot more about this in the future. And fifth, the electric self driving and software defined car is coming. With NVIDIA DRIVE, we are partnering with the global transportation industry to reinvent the car architecture, reinvent mobility, reinvent driving and reinvent the business model of the industry. Transportation is going to be one of the world's largest technology industries. From gaming, metaverses cloud computing, AI, robotics, self driving cars, genomics, computational biology, NVIDIA is doing important work and innovating in the fastest growing markets today. As you can see, on top of our computing platforms that span PC, HPC, Cloud, Enterprise to Autonomous Edge, we've also transformed our business model beyond chips. NVIDIA vGPU, NVIDIA AI Enterprise, NVIDIA Fleet Command and NVIDIA Omniverse adds enterprise software license and subscription to our business model. And NVIDIA GeForce Now and NVIDIA DRIVE with Mercedes Benz as the lead partner, our end-to-end services on top of that. I want to thank all of the NVIDIA employees and partners for the amazing work you're doing. We look forward to updating you on our progress next quarter. Thank you. Operator: This concludes today's conference call. You may now disconnect.

Operator: Good afternoon. My name is Mariama, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's Financial Results Conference Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. [Operator Instructions] Thank you. I will now turn the call over to Simona Jankowski, NVIDIA's Vice President of Investor Relations and Strategic Finance to begin the conference. Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the fourth quarter of fiscal 2021. With me on the call today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the first quarter of fiscal 2022. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, February 24, 2021, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Colette Kress: Thanks, Simona. Q4 was another record quarter with revenue exceeding $5 billion and year-on-year growth accelerating to 61%. Full-year revenue was also a record at $16.7 billion, up 53%. Our Gaming business have reached record revenue of $2.5 billion in Q4, up 10% sequentially, and up 67% from a year earlier. Full-year gaming revenue was a record at $7.8 billion, up 41%. Demand is incredible for our new GeForce RTX 30 Series products based on the NVIDIA Ampere GPU architecture. In early December, we launched the GeForce RTX 3060 Ti, which joined the previously launched RTX 3090, 3080, and 3070. The entire 30 Series lineup has been hard to keep in stock and we exited Q4 with channel inventories even lower than when we started. Although we are increasing supply, channel inventories will likely remain low throughout Q1. GeForce RTX 30 series graphics cards were a holiday sensation do not just do their amazing performance, but also to the rich features, including our second-generation RTX ray tracing technologies and DLSS, AI-powered performance accelerator, which massively boost frame rates in graphically demanding titles. Three dozen games now all support RTX, including the top Battle Royale game, Fortnite; the top role-playing game, Cyberpunk 2077; the top massively multiplayer online game, World of Warcraft; and the best-selling game of all time, Minecraft. RTX has clearly set the new standard in gaming. Building on this momentum at CES in January, we introduced a wave of Ampere architecture gaming products, including our biggest ever laptop launch powered by GeForce RTX 3060, 3070, and 3080 laptop GPUs and with our third-generation Max-Q technology. These new thin and lightweight gaming laptops increased performance and energy efficiency by up to 2x from the prior generation. RTX 3060 laptops start $999 and are faster than the previous generation laptops, which sold for $2,500. The incredible performance, design and price points of these new laptops will delight the growing universe of gamers and creators as well as students and professionals. The gaming laptop market has grown seven-fold in the past seven years and momentum is building. With top OEMs bringing to market a record 70-plus laptop models based on the GeForce RTX 30 series. GeForce laptops as a whole are the fastest-growing and one of the largest gaming platforms. Also at CES, we announced the GeForce RTX 3060 GPU, priced at $329, extending the 30 series desktop liner further into the mainstream. We expect strong demand when it launches this Friday as 60-class GPUs have traditionally been our most popular products. Starting with the 3060, we're taking an important step to maximize the supply of GeForce GPUs for gamers. Users are constantly discovering new applications for our powerful programmable GPUs and cryptocurrency mining is one of them. With rising Ethereum prices, there are indications that minors are behind GPUs. We would like GeForce GPUs to end up with gamers. So we have created a new special software drivers that will detect the Ethereum mining algorithm cutting in half the mining efficiency of the GeForce RTX 3060. We suspect the significant increase in the Ethereum network hash rate observed over the past few months was driven by a combination of previously installed mining capacities that was reactivated as well as new sales of GPUs and ASICs. Since our GPUs are sold to graphics card manufacturers and then onto distribution, we don't have the ability to accurately track or quantify their end use. Analyst estimate suggest that cryptomining contributed 100 million to 300 million to our Q4 revenue, a relatively small portion of our gaming revenue in Q4. Cryptocurrencies have recently started to be accepted by companies and financial institutions and show increased signs of staying power to address industrial Ethereum mining demand, last week we announced a new line of NVIDIA CMPs or cryptomining processors. Shipments will start in March. CMPs lack display outputs and have other optimizations that improve cryptomining power efficiency. CMP products will let us gain some visibility into the contribution of cryptomining to our overall revenue. For Q1, we estimate that CMP will contribute approximately 15 million. We plan to sell these products to industrial miners. We will quantify their contribution each quarter for transparency. Over the past year, it has become clear that we've entered a new era in which gaming is an integral part of global culture. The number of concurrent users on Steam has more than doubled since 2018 and continues to hit new records. In 2020 alone, more than 100 billion hours of gaming content was seen on YouTube and 0.5 billion people watched e-sports. Increasingly, we aren't just gaming. We're also watching sports, attending concerts, creating content and connecting with our friends in virtual environments. Additionally, we are excited about the new experiences like VR. Significantly more content is now available, including arguably the first VR killer app, Beat Saber, and there is now almost 2 million VR users on Steam. And with these powerful structural shifts, we expect our gaming business to remain on a robust growth trajectory. The GeForce RTX 30 Series GPUs have kicked off a powerful upgrade cycle and we estimate only around 15% of GeForce gamers on an RTX cost GPU, which is needed to experience the beautiful ray trace graphics of modern games. Moreover, the universe of gamers is rapidly expanding and the reach of GeForce has extended beyond gamers to some 45 million creators. In addition, gaming revenue continues to benefit from a favorable mix shift as gamers and creators keep moving to higher-end GPUs, we expect another great year for GeForce. Earlier this month, we celebrated the one year anniversary of the GeForce NOW cloud gaming platform, which is now over 6 million members strong. GeForce now offers 800 PCs from over 300 publishers, more than any other cloud gaming service including any of the most played free to play games, starting with support for windows PCs, Macs, and Android devices we added support in recent months to Chromebooks, iPhones and iPads. GFN has grown globally with more than 65 countries on our service and more – added regularly by our GeForce NOW alliance partners. Moving to Pro Vis, Q4 revenue was $307 million, up 30% sequentially and down 10% year-on-year and ahead of our expectations full-year revenue was $1.1 billion was down 13%. Strong sequential growth was driven primarily by a recovery in desktop workstations as some customers returned to the office and enterprises resumed purchases that had been deferred by the pandemic. Notebook GPUs grew sequentially to a record as enterprises continue to support remote workforce initiatives. Looking ahead, the reopening of businesses will benefit desktop workstations, but longer-term workforce trends will likely shift our mix to notebook GPUs and cloud offerings. Healthcare was a standout vertical in the quarter with significant orders from GE Siemens and Oxford Nanopore technologies, public sector and automotive also so strength. Omniverse are real time 3D collaboration and simulation platform is now an open beta. Over 500 creators and professionals have tested Omniverse through our early access program. Omniverse is one of our most important and exciting platforms. We are divided by its initial acceptance and look forward to sharing more details on its long-term growth opportunity in the coming months. Moving to Automotive; Q4 revenue was $145 million, up 16% sequentially and down 11% year-on-year. Full-year revenue of $536 million, declined 23%. Sequential growth was driven by continued recovering in the global automotive production volumes and growth in AI cockpit revenue. Year-on-year decline reflects the expected ramp down of legacy infotainment. Nvidia has emerged as the industry's leading end-to-end full stack technology provider for self-driving and AI enabled vehicles or in the SOC that drive self-driving platform is built on delivers an unrivaled 254 trillions of operations per second of performance on industry leading power efficiency, helping to revolutionize the transportation industry. Our technology leadership has driven a robust rapidly growing set of opportunities. We have great momentum with an expanding list of electric vehicle OEMs, including NIO, SAIC, Li Auto and Xpeng which are all using the Nvidia drive platform to power their next generation of vehicles. We look forward to growing with them as they continue to scale. Our software defined platforms is the only solution that spans from the data center for training deep neural net and running physically accurate simulations to a full stack in car solutions scaling for ADAS to Level 5 fully autonomous functionality. Autonomous vehicle companies are harnessing this technology. Zoox recently unveiled its Level 5 bi-directional robotaxi powered by Nvidia, and wide launched its next generation countless autonomous truck using NVIDIA Drive Orin. And earlier this year, Mercedes announced a 56-inch wide MBUX Hyperscreen powered by Nvidia AI cockpit technology. This will builds on our momentum with Mercedes first-generation MBUX system, which is now in 1.8 million cars. We are in the early innings of a significant opportunity. We have built a multi-billion dollar design win pipeline for our self-driving the AI cockpit solutions, which will drive a material inflection in revenue over the next few years. Our transformational partnership with Mercedes announced last June demonstrates the power of our evolving business model as we expand our addressable market and layer in software revenue. We are exceptionally well positioned to capitalize on the significant opportunity that lies ahead. Moving to data center; revenue was $1.9 billion, which exceeded our expectations was comparable to last quarter and up 97% from the year ago period, which did not include Mellanox. Data center compute revenue was up 45% year-on-year. Full-year data center revenue rose 125% to a record $6.7 billion, including almost 70% growth from data center compute. From a sequential perspective, the data center computes stronger than expected double-digit growth more than offset the anticipated decline in Mellanox revenue, which included a large non-reoccurring network sale to a single OEM in Q3. Compute growth was lead by vertical industries where OEM partners continued ramping up their 100 – day 100 base servers and our own DGX system sales were strong. Vertical industries were well over 50% of data center revenue across compute and networking with particular strength in super computing, financial services, higher education and consumer internet verticals. Additionally, hyperscale customers continued to deploy the A100 driving both sequential growth and exceptionally strong year-on-year growth in data center compute. The A100 has been adopted by all major cloud customers globally and is being deployed by hyperscale customers for internal workloads. Still we are in the early stages of adoption and expect continued growth this year. The ramp of the A100 has been smoother and accomplished by better visibility than prior generation. It's universal AI training and inference capabilities as well as support for wider set of applications and outstanding performance are driving five customer utilization; a clear sign of the A100's value. Turning to Mellanox. We are seeing continued strong traction and robust momentum across our customer set. Its revenue was up over 30% from Mellanox's Q4 revenue in calendar 2019 when it was still a standalone company. Year-on-year growth in the quarter was led by hyperscale and large consumer internet customers, which grew over 60% from last year with several contributing record revenues. Consistent with our outlook Mellanox had a sequential decline impacted by a non-reoccurring sales to a China OEM in Q3. We expect to return to sequential growth in Q1 driven by strong demand for our high speed networking products, including the ramp of ConnectX adapters with CSPs and all major server OEMs in their upcoming refresh. We also see strong momentum in high-performance computing with HDR InfiniBand products. For example, we won six of the seven supercomputers awarded over the past few months by Euro HPC. Starting next quarter, we will continue to provide color on networking as part of the data center market platform, but we will no longer break out Mellanox revenue separately. Looking forward, we are incredibly excited about the opportunities and data center. Accelerated computing is not only delivering super more loss, gains and performance but there's also an energy efficient and cost effective method of computing and virtually every industry is adopting technology with greater urgency as companies adapt to the new world of more distributed workers and customers. As industries embark on this journey, they are also increasingly focused on combating climate change. To that end, the A100 performed AI computations with one-twentieth the power consumption of CPU's. And powers are saline supercomputer, which is number one on the Green500 list of the world's most efficient supercomputers. Indeed Nvidia's powered machines recently captured 25 of the top 30 spots on the Green500 list. Accelerated computing is not only serving the exponential growth and demand for compute; it can also help bend the power consumption curve. With accelerated computing Nvidia is pioneering a [indiscernible] forward the computing industry. Before I moved to the P&L and outlook, let me give you an update on our proposed acquisition of Arm. In September, we announced plans to acquire Arm from SoftBank Group and a transaction that will create the premier computing company for the age of AI. At that time, we said it would take approximately 18 months to secure regulatory approvals in the U.S., the UK, the EU, China and other jurisdictions. Thorough reviews are typical with the deal of this size. This process is moving forward as expected. We are in constructive dialogue with the relevant authorities and are confident that regulators will see the benefits to the entire tech ecosystem. As we have said, this combination will spur competition together Arm and Nvidia will provide greater choice to the data center ecosystem, a compelling alternative CPU architecture for the market and further enhance Arms' offering in mobile and embedded. Our intention is to increase investment in Arm's existing roadmap, adding resources to stimulate growth in new markets. We love and intent to maintain Arm's open licensing model, a commitment guaranteed both by long-term legally binding contracts, as well as our own interest in ensuring this investment is a profitable one for us. We are on the cusp of new age in which AI fuels industries ranging from healthcare to scientific research to the environment. With this transaction, our vision is to boost Arm's potential, so it can thrive in this new era and grow into promising new markets. Moving to the rest of the P&L. Q4 GAAP gross margins were 63.1% and non-GAAP gross margins were 65.5%. GAAP gross margins declined year-on-year due to amortization of developed technology acquired from Mellanox, partially offset by product mix. The sequential increase was due to higher margins for gaming GPU and lower IP related costs, partially offset by lower margin mix in our data center portfolio. Non-GAAP gross margins increased by 10 basis points year-on-year and was flat sequentially in-line with our expectations. Q4 GAAP EPS was $2.31, up 51% from a year earlier. Non-GAAP EPS was $3.10, up 64% from a year ago. Q4 cash from operations was a record $2.07 billion. With that, let me turn to the outlook for the first quarter of fiscal 2022. Revenue is expected to be $5.3 billion plus or minus 2% with most of the sequential growth driven by gaming. GAAP and non-GAAP gross margins are expected to be 63.8% and 66% respectively plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $1.67 billion, and $1.2 billion respectively. For the full-year we expect to grow non-GAAP OPEX in the mid-20% range. GAAP and non-GAAP, other income and expenses are both expected to be an extent of approximately $50 million. GAAP and non-GAAP tax rates are both expected to be 10% plus or minus 1%, excluding discrete items. Capital expenditures are expected to be approximately $300 million to $325 million. Further financial details are included in the CFO commentary and other information on our IR website. In closing, let me highlight upcoming events for the financial community. We will be virtually attending to Raymond James Institutional Investors Conference on March 1st. The Morgan Stanley Technology, Media and Telecom Conference on March 3rd; and the Arete Virtual Semis Conference on March 3rd. In addition, we will be hosting a virtual Investor Day on Monday, April 12th following a live stream of Jensen's opening keynote at our GPU Technology Conference. Our earnings call to discuss our first quarter and full – our first quarter is scheduled for Wednesday, May 26. We will now open the call for questions. Operator, would you please poll for questions. Thank you. Operator: [Operator Instructions] Your first question comes from the line of C.J. Muse with Evercore ISI. Your line is open. C.J. Muse: Good afternoon. Thank you for taking the question. I guess, Jensen, higher-level question for you on the enterprise side. You're now a couple of quarters into the ramp of A100.And curious if you could speak to whether you've seen any surprises here, any areas of specific strength worth calling out? And any changes to how you're thinking about the size of this opportunity? Jensen Huang: Yes. Thanks a lot. It's Jensen. As you know, A100 is a very different type of GPU. This is our first universal computing GPU. It's great at high-performance computing. It's great at data analytics. It's great at training. And also for our highest GPU, it’s also the first time that is incredible for imprint. It's some 20 times faster than previous generation. We may introduce this some really exciting new computational formats like TF32, TensorFloat-32 for training. And with a multi-instance GPU, turning our GPU 1, GPU to a whole bunch of smaller GPUs, autonomous GPU to improve performance and reducing latency. And so the capability is really quite exciting. We're seeing strength in hyperscalers as they continue to accelerate their adoption of AI. Some of the new applications we've spoken about a couple of times before, the transition to deep learning, the conversational AI, speech recognition to natural language understanding all the way to speech synthesis, which is now based on AI – based on deep learning. The other area that's growing incredibly fast is the deep learning recommender models. Just about everything that you do on the Internet is based on recommenders. There are hundreds of different recommenders out there, whether you're shopping or recommending music or recommending news or recommending search, and so all the recommending ads. And so all of these different types of applications are driving that. For the first time, we saw our industrial application - industrial data center growing to be larger than hyperscale. And we're seeing industrial applications across scientific computing where simulation-based approaches are now being fused with AI approaches for weather simulation, genomics, molecular dynamics simulation, quantum chemistry, even simulating quantum computing, which is one of the really exciting areas. We're seeing AI being deployed for big data analytics, RAPIDS, which is NVIDIA’s created open source platform for data analytics of Spark 3.0, which NVIDIA really led and is now GPU accelerated. So now you could have big data in the cloud, while doing big data analytics in the cloud on all of the CSP platforms. You could – we're seeing a lot of excitement around financial services and consumer Internet services are all really growing nicely. And so A100 adoption is just starting. I mean, we're going to see several – couple of years of continued growth ahead of us, while as AI gets adopted in clouds and industries. Operator: Your next question comes from the line of Vivek Arya with BofA Securities. Your line is open. Vivek Arya: Thanks for taking my question. Just a clarification and then a question for Jensen. On the clarification, Colette, I was hoping if you could give a little more color around Q1? Do you still expect data center to grow sequentially in Q1? I know you said that most of the growth will come from gaming, but any color on the data center would be useful? And then, Jensen, the question for you is, in your press release you used the phrase AI driving the smartphone moment for every industry. Could you help us quantify what that means? And where I'm going with that is, is there a number in terms of what percentage of servers are shipping today with your accelerators? And where can that ratio go over time? Is that a fair way of looking at the adoption of your technology and AI? Colette Kress: So thank you, Vivek. Your question regarding the guidance as we beat into Q1. We had indicated that, yes, a good percentage of our growth between Q4 and Q1 those come from gaming, but we also do expect data center to grow. Most of our sequential growth coming from gaming, but keep in mind, we also expect all of our market platforms will likely be able to grow quarter-over-quarter. Jensen Huang: Because we are entering in the third phase of AI. The first phase of AI was when we invented the computing platforms, the new chips, the new systems, the new system software, the new middleware, the new way of working, the new way of developing software, which the industry, the world is now starting to call ML Ops. The way that software is developed and the way that is deployed is completely different in the past. In fact, I heard a great term Software 2.0, and it makes a lot of sense. It's a computer that is writing software. The way that you develop software is completely different; the way you compute is different. And that was our first phase, and that that started in a journey that was some eight, nine years ago now. The second phase was the adoption of using this in an industrial way for clouds. And we saw it revolutionize new services; whether it's speech-oriented services or search-oriented services, recommended services, the way you shop, the way you use the Internet is completely different today. And so that's really the second phase and those two phases are still continuing to grow, and you're still seeing the growth associated with that. The third phase is the industrialization of AI. And some of the great examples when I say in terms of smartphone moment, I meant that it's a device with AI, it’s autonomous and it's connected to a cloud service, and it's continuously learning. So some of the exciting example that I saw, that I've seen and we're working with companies all over the world, we have some 7,000 AI startups that we're working with, and almost all of them are developing something like this. And large industrial companies whether it's John Deere or Walmart, they're all developing application kind of like this. And basically it's an autonomous system, autonomous machine. In our case it's called Jetson. It's a robotics machine. That robotics machine is a car and it’s called DRIVE. And it's running an autonomous - an AI application on top and AI is still on top, and this could be – they could be moving device – moving things around, it can be picking and placing. It could be just watching a warehouse and monitoring traffic and keeping traffic flow going. It could be connected to a car. And whenever the car – whenever the fleet of cars needs to be retrained because of a new circumstance that was discovered, the cloud service would do the relearning and then we’d deploy into all of the autonomous devices. And so in the future, we're seeing these industries, whether you're in retail or in logistics or transportation, or farming, ag tech to lawnmowers – consumer lawnmowers. They're not going to just be products that you buy and use from that point forward, but it likely be a connected device with an AI service that runs on top of it. And so these industries I'm so excited about because it gives an opportunity to change the way that they interact with their customers. Rather than selling something once, they sell something and provide service that's on top of it. And they can stay engaged with the customers. The customers could get a product that's improving all of the time, just like your smartphone. And that's kind of like – that's kind of the reason – that's the reason why I've been calling it a smartphone moment for all these industry. And we saw what happened to the smartphone revolution. And then we saw what happened to the smart microphone, the smart speaker revolution, and you're going to see smart lawnmowers, smart tractors, smart air conditioners, smart elevators, smart building, smart warehouses, robotic retail storage and entire store – the entire retail store is like a robot. And they will all have autonomous capability that are being driven by AI. And so what’s new for the industry therefore. Is that all if the enterprises in the world use to have computers for IT to facilitate our employees and their supply chain. But in the future, all of these industries whether in medical imaging or lawnmowers, you're going to have data centers that are hosting your products just like the CSP. And so that's a brand new industry and we have a platform that we call EGX, which is the 5G Edge AI systems. And we have the autonomous systems we call AGX, which is what goes into adjusting and drives. And between those two systems and the software stack that we have on top of it we're in a great position to help these industries one at a time transformed their business model from a object oriented business model, a thing based business model to a connected device. Operator: Your next question comes from the line of Stacy Rasgon with Bernstein Research. Your line is open. Stacy Rasgon: Hi guys. Thanks for taking my question. First, I don't want to be pedantic, I suppose, but I guess on the Q1 guide, you say the gaming is the majority of the growth. Was that an absolute statement or was that a percentage statement? Can you give us some idea of how you'd sort of rank the sequential percentage growth of say gaming versus data center versus other, especially since it sounds like you've got 50 OEMs in crypto specific stuff that will go into the other? And then I guess just briefly, could you give us some indication of where your supply situation and lead times are on your ampere parts within data center? I think you'd said last quarter, they were many months on six months plus, are they still looking like that? And is that sort of a limiting factor at this point in terms of what you can actually ship on the compute side and data center? Jensen Huang: Colette, you will take one and I'll take the one. Colette Kress: Sure. Let me start off Stacy in terms of our guidance for Q1, as you know, we're still in the early innings of our Ampere Architecture. Our Ampere Architecture, as it relates to gaming, as well as what it relates to data center. As we articulated in our call we have been really seeing continued uplift of folks adoption of A100 and it's going quite smoothly than what we had seen in prior overall versions. So when we think about our guidance for Q1, there's many different types of conclusions that will happen at the end of the quarter in terms of what we should put all of our platforms can grow. But the majority of the majority of the growth from Q4 to Q1 will likely be gaming. Jensen Huang: Thanks, Colette. You asked the question about normal time, our company – at the company level where supply constraint, our demand is greater than our supply. And however for data center, so long as the customers more closely with us, and we do a good job planning between my companies, there should be a supply issue for data center. We just had to do a good job planning and we have direct relationships with each one of the royalty of fees and we have direct relationships with all the young ones. And we could do excellent planning between us. We shouldn't have a supplier. We shouldn't be supply constrained there. But at the company level, we're supply constrained. Demand is greater than supply. And yes, we have enough supply. Yes, we usually have enough supply to achieve that even the outlook. And we had that situation in Q4. We expect that situation in Q1. And we have enough supply to grow through the year. But supply against constraint and demand is really, really great. And so we just have to do a really good job planning. And meanwhile, one of the things that, that really came through for us is we have the world's best operations team. Our company is really, really have had an amazing operations team. We build the most complex products in the world, the most complex chips, the most complex packages, the most complex systems. And during Q4, they improved our cycle time and during Q1 we expecting them to improve our cycle time again. And we really are blessed to have such an amazing operations team. And so during these times it really comes in handy. But overall at the company level where we expected them to be greater than supply, we have enough supply to do better in the outlook. And we have enough supply to grow each quarter throughout the year. Operator: Your next question comes from the line of Timothy Arcuri with UBS. Your line is open. Timothy Arcuri: Hi, thanks. I had a question on crypto, I guess Jensen, I know that the CMP stuff and the software drivers stuff that you're doing for the 36. And that's going to help a lot, but I think there is like four or five the big currency was in a move or at least a moving or there on a faster move from a proof of work to proof of stake, which is going to be a lot less compute intensive. So I guess the question that I get a lot is how do you assess the degree to which that drives GPUs back into the secondary market? Is there any way to get treated, get kind of a handle on that? Thanks. Jensen Huang: Yes. If you look at the recent cash rates first of all, the transitions is going to takes some time. It can't happen overnight. And people have to build trust in all of the new versions. And so they'll take a little bit of time, but I hope it does. I hope that people use our proof of stake over time. And a little bit of these questions don't have to be answered. However I don’t have the optimism either that it will be all proof of stake, I think that proof of work is a very legitimate way of securing the currency. And in the beginning while any currency is building its reputation, then take something like a group of work to do so. And so I think proof of work is going to be around for a bit. We developed CMP for this very reason, just so that there are different versions. We have different versions of our products for gaming, for professional visualization, for high performance computing, for deep learning. It stands to reasoning that you have to do a different version for CMP and we can solve it directly the way that we go-to-market would be to go directly to the industrial miners to – and it’s a great benefit to them. So they don’t have to chasing around spot markets. It’s a great benefit to the gamers. And because they want a game, and the game demand is just probably is off a chart. And so this is visibility really beneficial to everybody. The recent hash rate growth was really a result of several dynamics. The first dynamic is the install base. Most people thought that the – once the mining – the GPUs come back into the aftermarket, a small part does that, some people do that, but the vast majority don't keep them. And the reason for that is because obviously they believe in Ethereum and so – they're industrial miners that's what they do. And so, they're keeping around for when the profitability returns and they could kick start the mining gear. We saw – that's what we saw in the latter part of last year. We saw the hash rates starting to grow. Most of that was result of the installed miners reactivating their equipment. It wasn't until earlier this year that we started to see a demand in our own GPUs. And when that starts to happen, there are some different dynamics. There's – the primary source these days come from powerful ASICs. And then there's some that that comes from our GPU and other GPUs in the marketplace. And so, I think that this is going to be a part of our business. It won't grow extremely large, no matter what happens. And the reason for that is because when it starts to grow large more ASICs comes into market, which kind of needs it. And when the market becomes smaller, it's harder for ASICs to sustain the R&D. And so the spot miners, industrial miners come back. And then we'll create CMPs. And so, we expect that to be kind of a – to be a small part of our business as we go forward. Now, one of the important things is to realize that in the near-term, the – because we're in the beginning parts of our Ampere ramp, only two quarters into a multi-year cycle. This is also the first time that we've completely changed computer graphics. RTX using ray tracing is completely different than virtualization. And so this is a fundamental change in the way we do computer graphics and the results have been spectacular. There are some 200 million install base in desktops and 50 million in laptop. And the vast majority of them, we've only upgraded approximately I think it's something like 50% of the install base has been upgraded to RTX. And so there is a giant install base and the install base is growing that we need to upgrade to the next generation of computer graphics. Operator: Your next question comes from the line of John Pitzer with Credit Suisse. Your line is open. John Pitzer: Yes, guys. Thanks for letting me to ask questions. I want to go back to data center. You've been very kind over the last couple of quarters to call out Mellanox both when it was a positive driver and when it was a headwind. I'm kind of curious as you do – when you look into the fiscal first quarter, is there anything of distinction to mention around Mellanox versus core data center? And I guess as a follow on, the key metric that a lot of investors were looking at is when does the core data center business year-over-year growth starts to reaccelerate? And some of that is just simple math where you're just comping very hard compares from last year. But Jensen how would you think about data center year-over-year growth in the context of a reopening trade or any sort of new applications out there? I mean, what happened – what helped last time around was the move to natural language AI. Is there another big sort of AI application we should be thinking about as we think about data center growth reaccelerating? Jensen Huang: We're expecting – Mellanox was down this last quarter. And our compute business grew double digit and offset – more than offsets the decline in Mellanox. We expect Q1 to be a growth quarter to Mellanox and we expect this coming year to be quite an exciting year of growth for Mellanox. The business is growing and ethernet is growing for CSPs is growing in InfiniBand for high performance computing and the Switch – the Switches have grown. Switch business grew 50% year-over-year. And so, we're seeing really terrific growth there. One of the new initiatives and we're going to see success towards the second half because the number of adoptions, the number of engagements has grown as our BlueField DPUs. It's used for virtualization for hyperscalers. It's also used for security. As you know quite well the future of computing and cloud and it's multi-tenant cloud and there's no VPN front door to the cloud. You've got millions of people who are using every aspect of computing. So you need to have distributed firewalls and you can't have it just in one place. The intense focus of security across all of the data centers around the world is really creating a great condition for BlueField, which is really perfect then. And so, I expect our Mellanox networking business to grow very nicely this year. And we expect Q1 to be a great growth quarter for compute as well as Mellanox. The Killer, great driving application for AI are several – last year you're absolutely right that it with natural language understanding and the transformer model and what is the – what was the core of – and other versions like that really, really made it possible for us to enable all kinds of new applications. So you're going to see a natural language understanding do text completion, and it's going to be integrated – I think it was just announced today that it was going to be integrated into Microsoft Word, and we've been working with them on that for some time. And so there are some really exciting applications out there, but the new ones that came – that emerged recently are deep learning based conversational AI, where the ASR, the speech recognition as well as the speech synthesis are now based on deep learning, it wasn't before. And they were based on models that ran on CPUs, but now with these deep learning models, the accuracy is much, much higher and it has the ability to also mimic your voice and be a lot more natural. And so, the ability – these models are much more complex and much larger. The other big huge driver is the recommenders. This is something really worthwhile to take a look at is called deep learning recommender models, and recommenders have historically – whether it's for shopping and or personalizing websites are personalizing your store, recommending your basket, recommending your music. Historically, it's been – and use the traditional machine learning algorithms, but because of the accuracy and – just the extraordinary economic impact that comes from an incremental 1% in accuracy for most of the – mostly the world's large internet businesses people are moving very rapidly to deep learning based models. And these models are gigantic. They're utterly gigantic. And this is an area that is really driving high-performance computers. And – so we – I expect us to see a lot of momentum there. And the last one is the one that I just spoken out, which has to do with industrial 5G and edge, IoT type of applications for all of the different industries whether it's retail or logistics or transportation, agriculture or warehouses to factories. And so, we're going to see AI and robotics in a very large number of applications in industries and we're just seeing so much excitement there. Operator: Your next question comes from the line of Aaron Rakers with Wells Fargo. Your line is open. Aaron Rakers: Yes. Thanks for taking the questions. I wanted to go back again on the data center business that you just mentioned, Jensen, that the BlueField-2 product poised to kind of ramp and materialize in the back half of the calendar year. How do you see that – is it an attach rate? I think there has been discussions in the past about – all servers could potentially over time incorporate this layer of acceleration. How quickly should we think about that ramp? And then the second question is, can you just at a high level talk about how CPU – how a CPU strategy you're thinking about that in the context of the broader data center market? Jensen Huang: Sure. If I could just work backwards, I believe that every single data center node will be outfitted with a GPU someday. And that someday is probably call it five years from now. And the fundamental driver of it is going to be security. Every single application in the data center and every single node in the data center has to be individually secured, zero trust computing, zero or confidential computing or zero trust computing. These initiatives are going to cause every data center to have every single application and every single node would be secured, which means every one of those computers have to have a control plane that is isolated from the application plane. And all the applications cannot share the same resources because that application could be malware, that application could be an intruder. No application could have access to the control plan. And yet today the software defined data centers, the software defined networking, software defined storage, all of the security agents are running in the same processors as the applications and that hasn't changed. You're seeing the cloud – the CSPs in the world moving in this direction. Every single data center will have to move in that direction. So every node will be a DPU process for the software, for the infrastructure. You're essentially going to see the data center infrastructure would be offloaded from the application point and it will be something like a Bluefield. So I think this is our next multi-billion dollar opportunity, CPS. We support every CPU in the world. And we're the only accelerated computing platform that accelerates every CPU. Ironically, the only CPU that we don't accelerate for AI is ARM, but we want to change them. ARM has such an exciting future because the nature of their business model and the nature of their architecture is perfect for the future of hyperscalers and data centers. You wanted – you want the most energy efficiency and in every single data center, because every data center is power constrained. We are going to be power constrained in every aspect of computing going forward. And so we would love to build around the own processor and invest in building a great ecosystem around it. And so that all the world's peripherals and all the world's applications can work and – work on any one of the CPUs that we know today. And I want to start – we're going to start with high-performance computing and start with – all the areas that we have a lot of expertise in to build out our platform. And so, you're starting to see one industry leader after another embraced on and I think that's terrific, but now we've got to energize it with all of the ecosystem support. It can't just be on vertical applications, but we want to create a broad general ARM ecosystem. Operator: Your next question comes from the line of Mark Lipacis with Jefferies. Your line is open. Mark Lipacis: Hi, thanks for taking my question. A question for Jensen, I think. Jensen, if you look at the past computing eras, typically it's one ecosystem that that captures 80% of the value of that computing era and mainframes as IBM and many computers with stack PCs, Wintel, cell phones, Nokia and then Apple. So, if you don't get the ecosystem right then you're splitting 20% of the market with a handful of players. So in this next era of computing parallel processing or AI, I think you've articulated the most compelling architectural vision of the data center of the future with data center scale computing devices with CPUs, GPUs, DPUs integrating to the same box serving all workloads in machine virtualized environment. Can you help us understand where is the market in embracing that vision and where is NVIDIA in building out that the ecosystem for that data center scale competing vision. And then maybe as part of that to what extent is CUDA of the kernel for that ecosystem? Thank you. Jensen Huang: Yes, we're – I think we've done a great job on building out the platforms for several ecosystems around the world. And the domain that we do incredibly well out on – the domains that I have to do with accelerated computing, we pioneered this approach. And we brought it to high-performance computing at first and we accelerated scientific computing and we democratized supercomputing for all researchers, anybody who wants to have a supercomputer now can. And computing, it will simply not be the obstacle that somebody's discovery. We did the same for artificial intelligence. We did the same for visualization. We brought – we expanded the nature of gaining tremendously. Our GeForce today is the largest gaming platform. It’s the largest single largest body of computers that are used for gaming. And in each case, we expanded the market tremendously. We would like to do the same for data center scale computing, as it applies to virtualizing these applications, these applications are also in the process. They've historically required dedicated systems, but they're moving into a virtualized data center environment. And we are best at doing that. They run on our platform today. We have the ability to virtualize it and put it into the data center and make it remotely available. And so these applications, these domains are some of the most important domains in the world. And so we're in the process of getting them. By doing so and making our architecture available to CSPs and OEMs, we could create this accelerated computing platform available to everybody. And so that's we're seeing our journey doing them. First, creating an architecting this platform, and then putting it literally into every single data center in the world. But we would also like to the next step of our journey is there's the Phase 3 of AI and has to do about – it has to do with turning every end point into a data center, whether it's a 5G tower, a warehouse or retail store, a self-driving car, a self-driving truck, these are going to be – they're all going to be essentially autonomous data centers and/or they're going to run AI, but they're going to run a lot more. They're going to do security in real time. Its networking is going to be incredible. It's going to run software to 5G and GPO accelerating 5G, we call areal. And so these platforms are going to become data centers. There'll be secure. The software is protected and we can't tamper with. It if you tamper with it, of course won't run. And so the capability of these clouds will move all the way out to the edge. And we're in the best position to be able to do that. So I did the – in this new world of post close to Moore's law post in arts gaming in this new world where AI and software that writes software in this new world, where data centers are going to be literally everywhere and they're unprotected. There's no giant building with a whole bunch of people that secured. And in this new world where a software is going to enable this autonomous feature, I think we are a perfectly positioned for it. Operator: This is all the time we have for Q&A today. I will now turn the call back to CEO, Jensen Huang. Jensen Huang: Thanks for joining us today. Q4 have the truly breakout year for Nvidia. The two biggest engines of our business gaming and data center posted powerful growth. Gaming has become the world's largest media and entertainment industry, and will grow to be much larger. And again gamers will create, will play, they'll learn, they'll connect. The medium of gaming can host any type of game and eventually evolve with the countless metaverses. So I'm for placed on for work. Gaming at simultaneously a great technology and a great business driver for our company. This year, we also closed our Mellanox acquisition and successfully united the amazing talent of our companies. Combined we possess deep expertise in all aspects of computing and networking to drive the architecture of modern data centers. Crowd computing and hyperscalers have transformed the data center into the new unit of computing. Chips on servers are just elements of the data center scale computers now. With our expertise in AI computing full stack accelerated computing, our deep network to computing expertise and cloud to edge platforms, Nvidia is helping to drive a great computer industry transformation. And our planned acquisition of Arm, the world's most popular and energy efficient CPU company will help position and video to lead in the age of AI. This year was extraordinary. The pandemic will pass, but the world has been changed forever. Technology adoption is accelerating across every industry. Companies and products need to be more remote and autonomous. This will drive data centers, AI and robotics. This underlies the accelerated adoption of Nvidia’s technology. The urgency to digitize, automate and accelerate innovation has never been higher. We are ready. We look forward to updating you on our progress next quarter. Thanks a lot. Operator: This concludes today's conference call. You may now disconnect.