NVIDIA Corp (NVDA) — Q4 2023 Earnings Call Transcript

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.

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 of $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 we expected due to 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 to the exceptional performance of the H100, which is as much as 9x faster than the A100 for training and up 30x faster than the inferencing of 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. OpenAI'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, 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 AI supercomputers, algorithms, data processing, and training methods that can bring these capabilities to enterprises. 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 models 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.

Thanks, Colette. The accumulation 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 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, 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.

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 RTX 4090, 4080, 4070 Ti desktop GPUs, with many retail and online outlets quickly selling out of stock. The flagship RTX 4090 has quickly risen 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 applications. 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 all, RTX 40 Series GPUs will power over 170 gaming and creator laptops, setting up for a great back-to-school 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 dramatically 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 the 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 the previous period 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 market. 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 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 non-affiliated 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 Investor Relations 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?

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, 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.

So I'll start and turn it over to Jensen to talk more because I believe this will be a great topic for 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.

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, validations, to inference. This body of software is completely accelerated. It runs in every cloud. It runs on-prem. It supports every framework, every model that we know of, and it's accelerated everywhere. By using NVIDIA AI, your entire machine learning operations become more efficient, and it is more cost-effective. You save money by using accelerated software. Our announcement today about putting NVIDIA's infrastructure and having it hosted 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 to 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.

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 relates 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?

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 year-over-year growth for our data center. We actually expect a great year with our year-over-year growth in data center probably accelerating past Q1.

Large language models are called large because they are quite large. However, remember that we've accelerated and advanced AI processing by a million times over the last decade. Moore's Law, in its best days, would have delivered 100 times in a decade. By coming up with new processors, new systems, new interconnects, new frameworks, and algorithms and working with data scientists and AI researchers on new models, across that entire span, we've made large language model processing a million times faster. What would have taken a couple of months in the beginning now 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, new NVLink switches, and its new InfiniBand 400-gigabits per second data rates, allows us to take another leap in the processing of large language models. By putting NVIDIA's DGX supercomputers into the cloud with NVIDIA DGX cloud, we're going to democratize the access to this infrastructure, and with accelerated training capabilities, really make this technology and this capability quite accessible. 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 their body of knowledge are different. Different fields and domains, whether it's imaging or biology or physics, each one of them needs their own domain of foundation models. Our strategy 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, that would like to create proprietary data and associated proprietary models. Regarding competition, our approach, our computing architecture, as you know, is quite different on several dimensions. Our architecture is universal, meaning you can use it for training, you can use it for inference, and it supports every framework. It supports every cloud. It's everywhere. It's cloud to private cloud, cloud to on-prem. It could be an autonomous system. This one architecture allows developers to develop their AI models and deploy them everywhere. The second very large idea is that no AI is in itself 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 two-thirds of the overall workload. By accelerating the entire end-to-end pipeline from preprocessing, data ingestion, data processing, all the way to the preprocessing, and then 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 instantaneously passed if you only accelerate half of the workload, is twice as fast. Whereas if you accelerate the entire workload, you could accelerate it 10, 20, or even 50 times faster, which is the reason why when you hear about NVIDIA accelerating applications, you routinely hear about 10x, 20x, or even 50x speed up. The universality of our accelerated computing platform, the fact that we're in every cloud and from cloud to edge makes our architecture quite accessible and very differentiated in this way.

I guess, Jensen, you talked about ChatGPT as an inflection point, similar to the iPhone. I'm curious how your conversations have evolved post-ChatGPT with hyperscale and large-scale enterprises. As you think about Hopper with the transformative engine and Grace with high-bandwidth memory, how have your outlook for growth for those two product cycles evolved in the last few months?

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. The accumulation of all the breakthroughs led to a service that surprised everybody with its versatility and capability. What people were surprised by, and this is well understood within the industry, is the surprising capability of a single AI model that can perform tasks and skills that it was never trained to do. For this language model to not just speak English or translate, but to output Python, output COBOL, a language that few people even remember, or output Python for Blender, a 3D program. This type of computer is utterly revolutionary in its application because it's democratized programming to so many people, which has excited enterprises all over the world. Every single cloud service provider, every internet service provider, and every software company is either alerted or shocked into alert or actively working on something integrated into their application or service to be like ChatGPT. Because of that reason, this is an exciting time on a global scale. The activity around the AI infrastructure that we build on Hopper and the activity around inferencing using Hopper and Ampere to inference large language models has just skyrocketed in the last 60 days. There’s no question that whatever our views of this year as we enter have been fairly drastically changed as a result of the last 60, 90 days.

Jensen, I wanted to ask a couple of questions on the DGX Cloud. 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 integrating InfiniBand with your Hopper product, with your Grace product, and selling things more on a system 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? What can that mean for the margins of that business over time?

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 connected with NVLink switches. This board represents essentially 1 GPU. It's 8 chips connected together into 1 GPU with a very high-speed chip-to-chip interconnect. 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. The cloud service providers are really excited about this 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. All of those relationships today would really love to be able to deploy both into the cloud and on-prem, often in multi-cloud. By having NVIDIA DGX and NVIDIA's infrastructure our 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. 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. This is a great model for customers, for CSPs, and for us. It lets us really run like the wind. We're going to continue to advance DGX AI supercomputers, but it does take time to build AI supercomputers on-prem. It’s hard no matter how you look at it. It takes time to build infrastructure, no matter how you look at it. Now, we have the ability to really prefetch a lot of that and get customers up and running as fast as possible.

Jensen, I had a question about what this all does to your total addressable market (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. 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 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. How do you think about what the new TAM would be?

I think those numbers are still a really good anchor. The difference is because of the incredible capabilities and versatility of generative AI and all the converging breakthroughs that happened towards the middle and 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 people develop computers happened because it was easier to use, easier to program, and more accessible. This happened with the PC revolution, and with the Internet revolution, and mobile cloud. Remember mobile cloud, because of the iPhone and App Store, 5 million applications and counting emerged. The same exact thing is now happening to AI. In no computing era did 1 computing platform, ChatGPT, reach 150 million people in 60 to 90 days. I mean, this is quite an extraordinary thing. People are using it to create all kinds of things. So I think that what you're seeing now is just a torrent of new companies and new applications emerging. There’s no question this is, in every way, a new computing era. The TAM that we explained is now even more realizable today and sooner than before.

I have a clarification and then a question both for Colette. The clarification is this: you said H-100 revenue is higher than A100. Was that an overall statement, or was that at the same point in time like after 2 quarters of shipments? For my actual question, I wanted to ask about auto, specifically the Mercedes opportunity. They had an event today discussing 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. 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 over that similar time frame? Is that how we should be modeling that?

Great. Thanks, Stacy, for the question. Let me first start with your question 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. This was a time for us to get production levels 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 H-100 was the focus of many of our CSPs within Q4, and they all wanted to get up and running in cloud instances. So we actually saw less of A100 in Q4 than we saw in H-100 at a larger amount. We tend to continue to sell both architectures going forward, but in Q4, it was a strong quarter for H-100. Your additional questions regarding Mercedes Benz—I'm very pleased with the joint connection that we have with them and the work. We've been working diligently to get ready to go to market. You're right. They talked about the software opportunity in two phases regarding what they can do with Drive as well as with Connect. They extended out to a position of probably about 10 years looking at the opportunity they see in front of us. So it aligns with what our thoughts are with a long-term partner of that and sharing revenue over time.

One of the things I could add, Stacy, is about the wisdom of what Mercedes is doing. This is the only large luxury brand that has, across the board, from every entry level all the way to the highest end of their luxury cars, equipped each of them with a rich sensor set, and every single one with an AI supercomputer, so that the entire future fleet will contribute to an installed base that could be upgradable and forever renewed for customers going forward. Just imagine what it would look like if every Mercedes on the road today could be completely programmable that you can update over the air. That would represent tens of millions of Mercedes vehicles generating revenue opportunities. That's the vision that Ola has and what they're building. I think it will be extraordinary.

I think for you, Jensen, it seems like every year a new workload comes out and drives demand for your processor or your ecosystem cycles. If I think back to facial recognition, 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? The reason I ask is that I found it interesting your comments in your script where you mentioned that your view about the demand that generative AI is going to drive for your products and now services seems to be much better than what you thought just over the last 90 days.

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. 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 NVIDIA is a multi-domain accelerated computing platform. It is not completely general-purpose like a CPU because a CPU is 95% or 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 can be highly, highly parallelized. We can do particle systems, fluid simulations, deep learning, and computer graphics. There are all kinds of different applications that we can accelerate. Our installed base is so large. This is the only accelerated computing platform, the only one 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 they have so many people to reach out to for help. So all of these reasons are why we keep attracting new applications. I also want to highlight that the rate of CPU computing has slowed tremendously. Back in the first 30 years of my career, we expected a 10x increase in performance every 5 years. But that rate has slowed, while people still have pressing applications to bring to the world. By accelerating computing, we can decrease power requirements for any workload, which is another important factor driving the adoption of accelerated computing. So I believe that increasingly new workloads will be driven by generative AI and other forms of new computing.

Colette, I have a question on the 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. Can you rank order for us the confidence in the acceleration? Is that based on your H-100 ramp, generative AI sales coming through or the new AI services model? Also, can you talk about what you're seeing on the enterprise vertical?

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 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 by our GPU computing and networking. We expect generative AI to spark interest among our customers, whether those be CSPs, enterprises, or start-ups. We expect that to support our revenue growth this year. Additionally, let’s not forget that in light of the end of Moore’s Law, there’s an era of focusing on AI and continued acceleration. As the economy improves, this becomes significant for enterprises and can be fueled by the ongoing adoption of a cloud-first approach.

No, you did great. That was excellent.

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 some of your customers with these large language models are talking about 100x the complexity over that kind of timeframe? I know Hopper is 6x better transformer performance. But what can you do to scale that up? How much of that reflects that it's going to be a much larger hardware expense down the road?

First, I'll start backwards. I believe the number of AI infrastructures are going to grow all over the world. The reason for that is AI— the production of intelligence is becoming a new form of manufacturing. There was a time when people manufactured physical goods. In the future, almost every company will manufacture soft goods. It just happens to be in the form of intelligence. Data comes in, and that data center does exactly one thing: it processes that data and produces a new updated model. Where raw material comes in, a building or infrastructure processes it, and something refined or improved comes out that is of great value—that's called the factory. So, I expect to see AI factories everywhere. Some of them will be hosted in the cloud, while some will be on-prem. Some will be large, some will be mega-large, and some will be smaller. I expect that to happen. 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, we will accelerate AI by another 1 million times. There are a lot of ways for us to do that. It's why NVIDIA is not just a chip company—because the problem we're trying to solve is too complex. We have to think across the entire stack from chip to data center across the network through software. We can innovate across that entire span. My expectation is that you're going to see really gigantic breakthroughs in AI models and platforms in the coming decade. But simultaneously, because of the incredible growth and adoption, you'll see these AI factories everywhere.

Thank you. The accumulation of breakthroughs from transformers, large language models, 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 across almost 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 toward 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 cloud service providers 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.