Good afternoon. My name is Krista, and I will be your conference operator today. At this time, I would like to welcome everyone to 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. 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'd like to withdraw your question, again, press star one. Thank you. Stuart Stecker, you may begin your conference.

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the fourth quarter of fiscal 2025. With me today from NVIDIA are Jensen Wong, 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 2026. The content of today's call is NVIDIA'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. For 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 if it's 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, Stuart. 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 percent 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 data center revenue of 35.6 billion was a record of 16 sequentially and 93 percent year-on-year as the Blackwell ramp commenced and Hopper 200 continued sequential 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 a hundred thousand GPUs or more. Tipments have already started for multiple infrastructures of this size. Post-training and model customization are fueling demand for NVIDIA 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 created from the Lama Foundation model. The scale of post-training and model customization is massive and can collectively demand orders of magnitude more compute than pre-training. Our inference demand is accelerating, driven by test time scaling and new reasoning models like OpenAI's O3, DeepSeq R1, and Gronk3. 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 mixture of experts inference. And it's NVLink domain delivers 14X the throughput of BCIE Gen 5, ensuring the response time, throughput and cost efficiency needed to tackle the growing complexity of inference of scale. Companies across industries are tapping into NVIDIA's full-stack inference platform to boost performance and slash costs. NAP tripled inference throughput and cut costs by 66% using NVIDIA TensorRT for its screenshot feature. Perplexity sees 435 million monthly queries and reduced its inference costs 3x with NVIDIA Triton Inference Server, and TensorRT LLM. Microsoft Bing achieved a 5x speedup and major TCO savings for visual search across billions of images with NVIDIA, TensorRT, and Acceleration Libraries. Blackwell has great demand for inference. Many of the early GB200 deployments are earmarked for inference, a first for a new architecture. Blackwell addresses the entire AI market from pre-training, post-training, to inference across clouds, to on-premise, to enterprise. CUDA's programmable architecture accelerates every AI model and over 4,400 applications, ensuring large infrastructure investments against obstinance in rapidly evolving markets. Our performance and pace of innovation is unmatched. We're driven to a 200x reduction in inference cost in just the last two years. We deliver the lowest TCO and the highest ROI. And full stack optimizations for NVIDIA 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 GB200 systems to cloud regions around the world to meet surging customer demand for AI. Regional cloud hosting NVIDIA GPUs increased as a percentage of data center revenue, reflecting continued AI factory buildouts globally and rapidly rising demand for AI reasoning models and agents. Or we've launched a 100,000 GV200 cluster-based instance with NVLink Switch and Quantum2 InfiniBand. Consumer Internet Revenue Group 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 GB200 to train and inference its next generation of GROG AI models. Meta's cutting-edge Andromeda advertising engine runs on NVIDIA'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, enhance ad personalization, and deliver meaningful jumps in monetization and ROI. Enterprise revenue increased nearly 2x year on the accelerating demand for model fine-tuning, RAG, and agentic AI workflows, and GPU-accelerated data processing. We introduced NVIDIA LAMA Pneumatron Model Family NIMS 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. leaders IQVIA, Illuminum, and Mayo Clinic as well as AHRQ Institute are using NVIDIA AI to speed drug discovery, enhance genomic research, and pioneer advanced healthcare services with generative and agentic AI. As AI expands beyond the digital world, NVIDIA 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 where virtually every AV company is developing on NVIDIA in the data center, the car, or both. NVIDIA's automotive vertical revenue is expected to grow to approximately $5 billion this fiscal year. At CES, Hyundai Mortar Group announced it is adopting NVIDIA 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 CEX, we announced the NVIDIA Cosmo World Foundation Model Platform. Just as language foundation models have revolutionized language AI, Cosmos is a physical AI to revolutionize robotics. The new 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 U.S., driven by the initial ramp of black wealth. Countries across the globe are building their AI ecosystems and demand for compute infrastructure is surging. France's 200 billion euro AI investment and the EU's 200 billion euro Invest AI initiatives offer a glimpse into the build-out to set redefined 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 on the onset of export controls. Absent any change in regulations, we believe that China shipments will remain roughly at the current percentage. The market in China for data center solutions remains 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 with Spectrum X. Spectrum X and NVLink switch revenue increased and represents a major new growth sector. We expect networking to return to growth in Q1. AI requires a new class of networking. NVIDIA offers NVLink switch systems for scale-up compute. For scale out we offer Quantum InfiniBand for HPC super computers and Spectrum X for Ethernet environments. Spectrum X enhances the Ethernet for AI computing and has been a huge success. Microsoft Azure, OCI, CoreWeave and others are building large AI factories with Spectrum X. 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 ethernet to every industry now moving to gaming and AI 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 remained 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, 5th generation Tensor Cores and 4th 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 ZLSS-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 for unprecedented visual fidelity. We also announced a wave of GeForce Blackwell laptop GPUs with new NVIDIA Max-Q technology that extends battery life by up to an incredible 40%. 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 healthcare. NVIDIA 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 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 Auron, running the safety-certified NVIDIA Drive OS. We announced Aurora and Continental will deploy driverless trucks at scale powered by NVIDIA Drive 4. Finally, our end-to-end autonomous vehicle platform, NVIDIA Drive Hyperion, has passed industry safety assessments by TUSUD and TOO-RYLAND, two of the industry's foremost authorities for automotive-grade safety and cybersecurity. NVIDIA is the first AV platform to receive a comprehensive set of third-party assessments. Okay, moving to the rest of the P&L. gross margins was 73% and non-gap 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 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 70s. Initially, we are focused on expediting the manufacturing of Blackwell systems to meet strong customer demand 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-70s late this fiscal year. Sequentially, GAAP operating expenses were up 9 percent and non-GAAP operating expenses were 11 percent, reflecting higher engineering development costs and higher compute and infrastructure costs for new product introductions. In Q4, we returned 8.1 billion to shareholders in the form of share repurchases and cash dividends. Let me turn to the outlook in the first quarter. Total revenue is expected to be 43 billion plus or minus two percent. 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 will grow 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 respectively. We expect full of year, fiscal year 26 operating expenses to grow to be in the mid-30s. GAAP and non-GAAP other income and 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 guidance. 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 Cowan 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 GEDC 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 the 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 28 2025. we are going to open up the call operator uh to questions if you could start that that would be great thank you at this time i would

like to remind everyone in order to ask a question please press star then the number one on your your telephone keypad. I also ask that you please limit yourself to one question. For any additional questions, please re-queue. And your first question comes from CJ Mews with Cantor Fitzgerald.

Please go ahead. Yeah, good afternoon. Thank you for taking the question. I guess for me, Johnson, as TECCON compute and reinforcement learning shows such promise, we're clearly seeing an increasing blurring of 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 and your customers? Thank you.

Yeah, I appreciate that, CJ. There are now multiple scaling laws. There's the pre-training scaling law, and that's going to continue to scale because we have multimodality. We have data that came from reasoning that are now used to do pre-training. And then the second is post-training scaling law, using reinforcement learning human feedback, reinforcement learning AI feedback, reinforcement learning verifiable rewards. amount of computation you use for post training is actually higher than pre training and it's kind of 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 training I models and that's post training and the third the third part this is the part that you mentioned is test time compute or reasoning long thinking inference scaling they're all 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 times and even hopefully extremely thoughtful and a simulation based and search based models that could be hundreds of thousands millions of times more compute than today is is in our future and so so that the question is how do you design such a such an architecture some of it some of the models are auto regressive some of the models are diffusion based some of it some of the times you want your data center to have disaggregated inference sometimes it's this is compacted and so it's hard to it's hard to figure out what is the best configuration of a data center which is the reason why NVIDIA'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 Gladwell takes all of that to a new level we designed Blackwell with the idea of reasoning models in mind. And when 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 were tens of times faster, 25 times higher throughput. And so Blackwell is going to 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 pre-training now post training now or scaling out your inference 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

Your next question comes from the line of Joe Moore with J.P. Morgan. Please go ahead.

Morgan Stanley, actually, thank you. I wonder if you could talk about GB200. 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. 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 for the nbl 72 platforms

well i'm more enthusiastic today than i was at ces and the reason for that is because we've shipped a lot more since we have we have some 350 plants um manufacturing the one and a half million components that go into each one of the blackwell racks grace blackwell racks yes it's extremely complicated and we successfully and incredibly ramped up Grace Blackwell delivering some 11 billion dollars in revenues last quarter. We're going to have to continue to scale as demand is quite high and customers are anxious and impatient to get their Blackwell systems. You've 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 blackwells for our own engineering and in our own design teams and and software teams core weave has has now been quite public about the successful bring up of theirs Microsoft has of course open AI has and you're starting to see many, many come online. And 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. Your next question comes from the line of Vivek Ara with Bank of America Securities.

Please go ahead. Thank you for taking my question. Collective you wouldn't mind confirming if Q1 is the bottom for gross margin. 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. Jensen Wilson- Well, let me first take the first part of the question there regarding the gross margin. During our Blackwell ramp, our gross margins will be in the low 70s. At this point we are focusing on expediting our manufacturing, expediting our manufacturing to make sure that we can provide to customers as soon as possible. Our Blackwell has fully ramped and once it does, I'm sorry, once our Blackwell fully ramps we can improve our cost and our gross margin. So we expect to probably be in the mid 70s later this year. You know walking through what you heard Johnson speak about the systems and their complexity. They are customizable in some cases. They've got multiple networking options. They have liquid cooled and water cooled. So we know there is an opportunity for us to improve these gross margins going forward. But right now we are going to focus on getting the manufacturing complete and to our customers as soon as possible we know several things is that

we have a fairly good line of sight of the amount of capital investment that data centers are building building out towards we know that going forward the vast majority of software is going to 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 forecasts and plans from our top partners and we also know that there are many innovative really exciting startups that are still coming online as new opportunities for or developing the next breakthroughs in AI, whether it's agentic AIs, reasoning AIs, or physical AIs. The number of startups are still quite vibrant and each one of them need a fair amount of computing infrastructure. And so I think whether it's the near-term signals or the mid-term signals, Near-term signals, of course, are 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 has to do with the fact that we know fundamentally software has changed from hand coding that runs on CPUs to machine learning and AI-based software that runs on GPUs and accelerated computing systems. And 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 tapped consumer AI and search and some amount of consumer generative AI. Advertising, recommenders, of the early days of software the next the next wave is coming agentic ai for enterprise uh physical ai for robotics and um sovereign ai as as different regions build out their their ai for their own ecosystems and so each one of these are fairly 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 midterm long term your next question comes from the line of harlan sir with jp morgan please go ahead yeah good afternoon thanks for taking my question

your next generation blackwell ultra 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? Yes, Blackwell Ultra is second half. As you know, the first Blackwell was, we had a hiccup that probably cost us a couple of months. We're fully recovered, of course. The team did an amazing job recovering, and all of our supply chain partners and just so many people helped us uh 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 is on on an annual rhythm and blackwell ultra with new networking new new memories and of course new new processors and all of that is coming online uh we've we've uh i've been been uh 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 and blackwell ultra the system architecture is exactly the same it's a lot harder going from hopper to blackwell because we went from an Envy Envy link 8 system to a Envy link 72 base system so the the chassis the architecture of the system the hardware the power delivery all of that had to change this was a this was quite a challenging transition but the next transition will slot right in brace black blackwell ultra will slot right in we've also uh already revealed and and uh been working very closely with all of our partners on the click after that and the click after that is called vera rubin and 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 going to provide a big, huge step up. And so come to GTC, and I'll talk 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.

Your next question comes from the line of Timothy Arcuri with UBS. Please go ahead.

Thanks a lot. Jensen, we hear a lot about custom ASICs. Can you kind of speak to the balance between custom ASIC and merchant GPU? We hear about some of these heterogeneous superclusters to use both GPU and ASIC. Is that something customers are planning on building, or will these infrastructures remain fairly distinct?

Well, we build very different things than ASICs. in in some ways completely different in some areas we intercept we're different in several ways one NVIDIA's architecture is general you know whether you're you've optimized for our regressive models or diffusion based models or vision based models or multimodal models or text models we're great at all of it we're great at all of it because our software stack is so our architecture is flexible 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 uh the training of the data of course to reinforcement learning uh used in post training all the way to uh inference with uh with um tough time scaling so you know we're we're general we're end-to-end and um 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 so um we're everywhere and then the third thing i would say is that our performance and our rhythm is so incredibly fast uh 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 in 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 our lives and so I think the the third reason is is a performance and then and then um 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's just 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 that the world is 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, you know, over and over again. There are a lot of chips that get spilled. 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, in power, and in time. And our technology is 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.

why we win. Your next question comes from the line of Ben Reitz with Malias Research. Please

go ahead. Yeah, hi, Ben Reitz is 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 U.S. was up about 5 billion or so sequentially. And I think, you know, there is a concern about whether U.S. can pick up the slack if there's regulations towards other geographies. And I was just wondering, as we go throughout the year, if this kind of surge in the U.S. continues and it's going to be, whether that's okay. And if that underlies your growth rate, how can you keep growing so fast with this mixed shift towards the U.S.? Your guidance looks like China is probably up sequentially. So I'm just wondering if you could go through that dynamic and maybe collect and weigh in. Thanks a lot.

China is approximately the same percentage as Q4 and as previous quarters. It's about half of what it was before the export control, but it's approximately the same percentage. with respect to 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 if you were to buy, you know, a quarter of milk is delivered to you, AI was involved. And so almost everything that a consumer service provides AIs at the core of it. Every student will use AI as a tutor. Healthcare services use AI. Financial services use AI. No fintech company will not use AI. Every fintech company will. Climate tech company use AI. Mineral Discovery now uses AI. The number of, every higher education, every university uses AI. And 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 society. And with that, 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 going to be part of improving software and services and that the future computers will be accelerated. that future computers will be based on AI. And we're really two 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.

Your next question comes from the line of Aaron Rakers with Wells Fargo. Please go ahead. Aaron, your line is open. Your next question comes from Mark Lupisis with Evercore ISI. Please go ahead.

Hi, that's Mark Lupisis. Thanks for taking the question. I had a clarification and a question. Colette, up 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 uh would that make it the fast faster going than the hyperscalers and then jensen for you the question um hyperscalers are the biggest purchasers of your uh 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 of how that hyperscaler spend splits between that external workload and internal. And as these new AI workloads 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. Sure. Thanks for the question regarding our enterprise business. Yes, it grew 2X and 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 in 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, correct, growing quite

well. 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 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 so the the useful life of our of our infrastructure is much better. If the useful life is much longer, then the TCO is also lower. And so the second part, 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. So 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 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 share with you guys at GTC, those agentic systems are for employees to make employees more productive, to design, to market, to 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 a billion cars on the road someday there'll 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 of 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 the machineries. It could be, of course, it could be a tractor. It could be a lawnmower. It could be a human or 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 type of things that are common sense to you and I, but, you know, AIs have 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. We've got some really great things going on. And then there's the physical AI after that, and then there are 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 the larger of them all, which kind of makes sense. You know, the world's GDP is represented by either heavy industries or industrials and companies that are

providing for those. Your next question comes from the line of Aaron Rikers with Wells Fargo.

please go ahead. 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, and 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, you know, GB300 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 yeah i appreciate it first of all

um people are still using voltus and pascals and amperes and the reason for that is because they're always um things that because because cuda is so programmable you could use it right well one of the one of the major use cases right now is uh data processing and data curation you you 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 that that circumstance to a vision language model the vision language model actually looks at the circumstances this is a 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 holes lake 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 data set, which you then present to your hopper systems for training. And so each one of these architectures are completely, you know, they're all CUDA compatible. and so everything runs on everything but if you have infrastructure in place then you can put the less intensive workloads onto the install base of the past all of our cpus are very well

employed we have time for one more question and that question comes from atif malik with city

please go ahead hi thank you for taking my question i have a follow-up question gross margins for colette so let i understand there are many moving parts the blackwell yields and relink 72 and ethernet mix and you kind of tiptoed the earlier question if april quarter is the bottom but a second half would have to ramp like 200 basis points per quarter to get to the mid 70s range that you're giving uh for the end of the fiscal year and we still don't know much about tariffs impact to broader semiconductors so what kind of gives you the confidence in that trajectory in the back half this year.

Yeah, thanks for the question. Our gross margins, they're quite complex in terms of the material and everything that we've 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 going to probably start as soon as possible if we can and 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 We understand further what the U.S. government's plan is, both its timing, its where, and how much. So, at this time, we are awaiting, but again, we would, of course, always follow export controls and or tariffs in that manner.

Ladies and gentlemen, that does conclude our question and answer session. I'm sorry. Thank you.

No, no. I just wanted to thank you. up to uh Jensen and I believe he has a couple things I just wanted to thank you thank you

Colette the 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 open ai's brought three deep seek r1 are reasoning models that apply inference time scaling 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 for chain of thought and reinforcement learning techniques like R1 to scale their models performance we now have three scaling laws as i mentioned earlier driving the demand for ai computing the traditional scaling laws of ai remains intact foundation models are in being enhanced with multi-modality and pre-training 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 pre-training alone inference time scaling and reasoning where a single query can demand 100 times more compute we designed blackwell for this moment a single platform that can easily transition from pre-training post training and test time scaling blackwell's fp4 transformer engine and mvlink 72 scale up fabric and new software technologies let blackwell process reasoning ai models 25 times faster than hopper blackwell and all of its 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 100,000 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 them, either rented or self-operated. I want to thank all of you for joining us today. Come join us at GTC in a couple of weeks. We're going to be talking about Blackwell Ultra, Rubin, and other new computing, networking, reasoning AI, physical AI products, and a whole bunch more. Thank you.

This concludes today's conference call. You may now disconnect.