NVIDIA Corp (NVDA) — Q3 2022 Earnings Call Transcript

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.

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 support 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 into 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 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 the 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 the general availability of Omniverse Enterprise, a platform for simulating physically accurate 3D worlds 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 startup, Kodiak Robotics; automaker, Lotus; autonomous bus manufacturers, QCraft; and EV startup, WM Motor, have adopted the 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 startups. 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 recommendation 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 in 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 enable 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 mainstream 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-2, 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, 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 and augmenting real-world data with massive, diverse, and physically accurate synthetic data sets to both accelerate the 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 speech AI, computer vision, natural language understanding, recommendation engines, and simulation. Applications include automated customer service, virtual collaboration, and content solutions. Replicator and 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 kits, bringing our total to more than 150, serving industries from gaming and design to AI, cybersecurity, 5G, and robotics. One of the SDKs is our first fully 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 Pigeon for customer service chatbots. NVIDIA Riva Enterprise will be commercially available early next year. For launch, we introduced 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, developing language and industry-specific dropboxes, 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 language 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 BlueField DOCA. Our new capabilities make BlueField the ideal platform for the industry to build their 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. We also released Clara Holoscan, an 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 SDKs enables 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 reality. In Automotive, we announced NVIDIA DRIVE Concierge and DRIVE Chauffeur, AI software platforms that enhance a vehicle's performance, features, and safety. Live Concierge builds on Omniverse Avatar and functions as an AI-based in-vehicle personal assistant, enabling automatic parking and summoning capabilities. It also enhances 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 the 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 missions, 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 centers, 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 transaction has been under review by the 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 for Arm, its licensees, and the industry. We believe these concerns raised by some Arm licensees do not invalidate the merits 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, and 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 was particularly offset by the 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 agreements. 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.

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 how we at finance line should probably think about that opportunity materializing?

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 between designers and creators among themselves. Those are the nearest-term metrics. And I would say that in my definition, etc. Near term also 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 the 3D and digital design world. Think of Adobe as a world, think of Autodesk as a world, think of Revit as a world. These are design worlds in the sense that people are doing things in them, they are creating things in them and they have to run databases. 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, Omniverse's collaboration capability is going to be really appreciated, and that should happen right away. We would like to see that happen in the 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. We show in these following examples 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 to plan and to optimize their warehouses and deploying the robots. And so digital twin applications are absolutely immediate. And then remember, robots have 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 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 you use that is, for example, with $25 million or so retail stores, restaurants, 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 checkout, 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, are 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 $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 an 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 checkout smart retail or smart warehouses or 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 that it requires three fundamental technologies to come together, I guess four fundamental technologies to come together. First, it’s video graphics; second is physics simulation, because we're talking about things in a 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 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 realize it as fast as we can every transaction that goes on to the 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.

Hi, thanks for taking my question. Jensen, every year it seems like there are new demand drivers for your accelerated platform and processing ecosystem, such as gaming, neural networks, AI, blockchain, and ray tracing. Five or six years ago, you presented some exciting virtual reality demos at your Analyst Day. While the excitement diminished, it appears to be returning, especially with the capabilities of Omniverse Avatar and Facebook highlighting the potential. My two questions are: how close is your Omniverse Avatar to becoming a mass-market technology that everyone uses daily? You mentioned that everyone is going to be a gamer and an Omniverse Avatar user. Also, is it reasonable to expect new killer apps to emerge every year? Should we draw parallels with previous computing markets for the current computing landscape we are entering? Thank you.

Yes, I really appreciate that. Chips enable functionality, but they do not create markets; that role belongs to software. Over the years, I've explained that accelerated computing vastly differs from general-purpose computing. You cannot simply input quantum physics into a compiler and expect it to function correctly on a chip. It's not as straightforward as compiling Schrödinger's equation across multiple GPUs or nodes to generate a new SaaS. The same applies to fields like computer graphics, artificial intelligence, and robotics—most innovative applications cannot be simplified into that model. We've reached a limit with GPUs. People recognize that while it’s not impossible to extract parallel instructions from a system, it's extremely challenging. There is an alternative approach, which we've been promoting for some time, and now more people are beginning to see its advantages. However, it requires substantial effort. For each application within a large domain, a comprehensive stack must be developed. To launch a new market by accelerating these applications, a new stack must be created, which is a daunting task requiring a deep understanding of the application, algorithms, mathematics, and computer science. Transforming a single-threaded process into a multi-threaded one disrupts various components like storage and networking, making it a complex challenge. Over the years, we have evolved into a full-stack company to address these issues. Once expertise is acquired, new markets can be explored. We have played a significant role in democratizing artificial intelligence, allowing broader access to technology for researchers and scientists to enhance their work. Each year, we introduce new stacks, with many developments visible as they come together. One focus area we've discussed is Omniverse, which took half a decade to develop while building on 25 years of foundational work. In the case of Omniverse Avatar, you can directly connect it to technologies like MERLIN for recommendations, Megatron for language processing, and Riva for speech AI, among others. These technologies, while developed separately, have been integrated to create Omniverse Avatar. Regarding deployment, I anticipate that Omniverse Avatar will be utilized in fast-food restaurants and retail locations worldwide within five years. This technology addresses the labor shortage effectively, providing a reliable, tireless, and always-available solution. Our cloud-native approach, as demonstrated in our keynote, enables instantaneous interaction and a pleasant conversational experience. To summarize, accelerated computing presents a complex, full-stack challenge. It is software that opens new markets, not chips. While new chips may capture market share, they do not create new opportunities. At NVIDIA, we leverage software to access significant market prospects. Lastly, I view Omniverse as a near-term opportunity that we've been developing for three to five years.

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.

Thank you, C.J. We have secured a substantial guaranteed supply from the world's leading foundry, which is a crucial part of our supply chain. We feel confident about our supply situation, especially starting in the second half of this year and moving forward. Last year served as a reminder for everyone to be more aware of the importance of the supply chain, and we are fortunate to have excellent partners that have helped ensure our future. Regarding our Data Center business, approximately half comes from cloud service providers and the other half from various enterprise companies, with just 1% coming from supercomputing centers. We anticipate that next year, cloud service providers will aggressively expand their deep learning and AI workloads, a trend we are already witnessing. We have developed an exceptional platform. Our work with TensorRT and its integration into the Triton server is one of our most significant achievements, and we take great pride in it. Nearly four years ago, we highlighted that inference would pose major challenges in computer science, and this has proven to be true due to varying needs for throughput, latency, and interactivity across different models. This complexity stems from the diverse architectures and application types involved. We are now on our 8th generation of this technology, which is widely adopted globally, with around 25,000 companies utilizing NVIDIA AI. Recently at GTC, we announced two major developments. First, we introduced tools to manage AI deployment across all generations of NVIDIA GPUs, making it essential for operating NVIDIA servers worldwide. Secondly, we now support CPUs, allowing customers to streamline their inference servers to just one, with Triton being a fundamental element. Additionally, we unveiled the Forced Inference Library (FIL), which supports widely used machine learning systems based on decision trees, such as XGBoost. This approach is prevalent in areas like fraud detection and recommendation systems due to its extensibility. Furthermore, Triton will now support multi-GPU and multi-node inference, enabling real-time interaction with large language models like OpenAI's GPT-3 and NVIDIA's Megatron 530B. I showcased a model capable of answering questions in real time during a demonstration. These advancements will enable us to continue scaling and innovating. To return to your initial inquiry, we expect Data Center growth to be robust next year. Customers are increasingly focused on securing their supply chains for expansion, and our visibility within the Data Center space has improved significantly. Triton is experiencing widespread adoption, and we are also working on new AI-based workloads within our Omniverse platform. We identified customer support as an area facing significant shortages globally, presenting an opportunity for Omniverse Avatar. This platform can be adapted for various applications, such as drive-thrus and customer service, which I demonstrated using a Tokyo parking kiosk. Omniverse Avatar has potential in tele-operated customer service and robotics, exemplified by our DRIVE Concierge service that transforms vehicles into intelligent assistant agents. I believe Omniverse Avatar will be a significant driver for enterprise growth next year, leading to a promising outlook for the Data Center sector.

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?

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.

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?

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 a total long-term capacity agreement of about $3.4 billion. So we still have more payments to make, and we are likely to continue to be purchasing longer term to support our growth that we are planning for many years to come.

Every single server will eventually be GPU-accelerated. Currently, less than 10% of clouds and enterprises utilize this technology. Many workloads still rely solely on CPUs, which is why we need to be a full-stack company and actively seek out applications. We need to identify numerous applications that can either require or greatly benefit from acceleration. Achieving a million times speed up may sound extreme, but it's mathematically feasible and has historically been demonstrated in many sectors, including computer graphics, which is essential for Omniverse. Our work in digital biology and protein synthesis is set to become a significant industry that doesn't exist yet, and protein engineering will be substantial. Accomplishing this requires million times speed up in protein biology simulations. Climate science also needs massive speed up, and we are at a stage where we can address that. In each scenario, we must focus our resources to accelerate applications, which will drive growth. Currently, many speech synthesis and recognition systems still rely on traditional or a mix of traditional and deep learning approaches. NVIDIA Riva is the first end-to-end deep neural network for this purpose. We have helped numerous companies transition to our neural-based approaches, providing references and licensing so they can apply it for their own cases. We need to accelerate one application after another, and one area we are particularly excited about is Electronic Design Automation. For the first time, we've announced EDA utilizing GPU-accelerated computing, thanks to artificial intelligence capabilities. EDA involves significant combinatorial optimization, and AI can greatly enhance design quality and speed. Major game vendors, chip design firms, and PCB design industries are increasingly adopting AI and GPU acceleration. Traditional CAD applications are now also benefiting from considerable speed improvements. I'm thrilled about our progress in these areas, as every advancement opens up new markets and attracts customers who have never previously utilized NVIDIA GPUs. Ultimately, people don't just buy chips; they seek solutions to problems. Without a complete stack and software expertise, you can't fully leverage the technology that chips provide to solve customer challenges.

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.

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 has 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 and cost increases are generally a part of our gross margin figures.

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 easy access to NVIDIA AI. Gaming and Pro Visualization are surging. The 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; AI, upcoming robotics, digital biology, and what I hope is 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, and market and domain-specific software 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 addressing 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 twins. 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, video conferencing, and digital twins for 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.

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.