NVIDIA Q4 2025 Earnings Call Transcript

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Provided by AlphaStreet
February 26, 2025
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Operator

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 4th-Quarter Earnings Call. All lines have been placed on-mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. If you would like to ask a question during this time, simply press star followed by the number-one on your telephone keypad. And if you'd like to withdraw your question, again press star one.

Thank you. Stuart, you may begin your conference.

Stewart Stecker
Investor Relations at NVIDIA

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the 4th-quarter of fiscal 2025. With me today from NVIDIA are Jensen Wong, President and Chief Executive Officer; and Colette Cress, 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 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, 26, 2025, based on information currently available to us. Except as required by-law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. Can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website.

With that, let me turn the call over to Colette.

Colette Kress
Executive Vice President and Chief Financial Officer at NVIDIA

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% from 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 4th-quarter, data center revenue of $35.6 billion was a record, up 16% sequentially and 93% year-on-year. As the Blackwell ramp commenced and Hopper 200 continued 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 meet expanding customer adoption. Our Q4 data center compute revenue jumped 18% sequentially and over 2x year-on-year. Customers are racing to scale infrastructure to train the next-generation of cutting-edge models and unlock the next level of AI capabilities.

With Blackwell, it will be common for these clusters to start with 100,000 GPUs or more. Shipments have already started for multiple infrastructures of this size. Post-training and model customization are fueling demand for NVIDIA infrastructure and software as developers and enterprisers 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 OpenAISE, 03, R1 and 3. Long thinking reasoning AI can require 100x more compute per task compared to one-shot inferences. Blackwell was architected for re-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 infants. And its Envy link domain delivers 14 times the throughput of DCIE Gen5, ensuring the response time, throughput and cost-efficiency needed to tackle the growing complexity of inference at-scale.

Companies across industries are tapping into NVIDIA's full stack inference platform to boost performance and/costs. Tripled inference throughput and cut costs by 66% using NVIDIA TensorRT for its screenshot feature. Sees 435 million monthly queries and reduced its inference costs 3 times with NVIDIA, Triton Inference Server and Tensor RTLLM. Microsoft Being achieved a 5x speedup and major TCO savings for visual search across billions of images. Blackwell has great demand for inference. Many of the early GB-200 deployments are Blackwell addresses the entire AI market from pre-training post-training to inference across cloud to on-premise to enterprise.

UDA's programmable architecture accelerates every AI model and over 4,400 applications, ensuring large infrastructure investments against in current pace of innovation is unmatched. We're driven to a 200 times reduction in inference costs in just 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 GB 200 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 build-outs globally and rapidly rising demand for AI reasoning models and agents. We've launched a 100,000 GV 200 cluster-based instance with NVLink switch and Quantum 2 InfiniBand. Consumer Internet revenue grew 3 times 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, X-AI is adopting the to train and inference its next-generation of 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 3 times, enhance ad personalization and deliver meaningful jumps in monetization and ROI.

Enterprise revenue increased nearly 2x year-on accelerating demand for model fine-tuning, RAG and identic AI workflows and GPU accelerated data processing. We introduced NVIDIA Lama, Lama Numatron 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. Healthcare leaders, IQVIA, Illumina and Mayo Clinic are well as ARC 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 CS -- 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 EX, 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 US, driven by the initial ramp of Blackwell. Countries across the globe are building their AI ecosystems and demand for compute infrastructure is surging.

France's EUR200 billion AI investment and the EU's EUR200 billion 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 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 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 8 with InfiniBand to large NVLink 72 with Spectrum X has increased and represents a major new growth vector. We expect networking to return to growth in Q1. AI requires a new class of networking. NVIDIA offers switch systems, demand for HPC supercomputers and Spectrum X for Ethernet environments. Spectrum X enhances the Ethernet for, OCI, Weave and others are building large AI factories with Spectrum X. The first 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 AIPCs. 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, we expect strong sequential growth in Q1 as supply increases. The new GeForce RTX 50 Series desktop and laptop GPUs are here. Built for gamers, creators and developers, they fuse AI and graphics redefining visual computing.

Powered by the Blackwell architecture, fifth-generation Tensor cores and fourth-generation RT cores and featuring 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 VLSS4 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%. These laptops will be available starting in March from the world's top manufacturers. Moving to our professional digitalization business. Revenue of $511 million was up 5% sequential -- 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, 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 robotoxies. At CES, we announced Toyota, the world's largest automaker, will build its next-generation vehicles on NVIDIA, 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 Hyperia has passed industry safety assessments like and Riland, trade 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. GAAP gross margins was 73% and non-GAAP gross margins was 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 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 -- in the low-70s. We -- initially, we are focused on expediting the manufacturing of Blackwell systems to meet strong customer demand as they race to build-out Blackwell infrastructure. 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% and non-GAAP operating expenses were 11%, reflecting higher engineering development costs and higher compute and infrastructure costs for new product introductions. In Q4, we returned $8.1 billion to shareholders 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 2%. Continuing with its strong demand, we expect a significant ramp of Blackwell in Q1. We expect sequential growth in both data center and gaming. Within data center, we expect sequential growth from both compute and networking. GAAP and non-GAAP gross margins are expected to be 70.6% and 71%, respectively, plus or minus 50 basis-points. GAAP and non-GAAP operating expenses are expected to be approximately $5.2 billion and $3.6 billion, respectively.

We expect full-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 items. Further financial details are included in the CFO commentary and other information available on our IR website, including a new financial information AI agent.

In closing, let me highlight upcoming events for the financial community. We will be at the TD Cowen Healthcare Conference in Boston on March 3rd and at the Morgan Stanley Technology, Media and Telecom Conference in San Francisco on March 5. Please join us for our annual GTC conference starting Monday, March 17 in San Jose, California. Dinson will deliver a newspacked keynote on March 18, and we will host a Q&A session for our financial analysts the next day, March 19. 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 to questions. If you could start that, that would be great.

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Operator

Thank you. At this time, I would like to remind everyone in order to ask a question, please press star than the number-one on 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 Muse with Cantor Fitzgerald. Please go-ahead.

C.J. Muse
Analyst at Cantor Fitzgerald & Co.

Yeah, good afternoon. Thank you for taking the question. I guess for me, Johnson, as Compute and reinforcement learning shows such promise, we're clearly seeing increasing blurring in the lines between training and inference. What does this mean for the potential future of potentially inference dedicated clusters? And how do you think about the overall impact to NVIDIA and your customers? Thank you.

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

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, the 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 train AI models.

And that's post training. And 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 you have chain of thought, you have search, the amount of tokens generated, the amount of inference compute needed is already 100 times more than the one-shot examples in 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 simulation based and search-based models that could be hundreds of thousands, millions of times more compute than today is in our future.

And so the question is, how do you design such 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 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 Blackwell 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 is many times more performant, but what's really amazing is for long thinking, test time scaling, reasoning AI models were 10s 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, our architecture is fungible and easy-to-use in all of those different ways. And so we're seeing, in fact much, much more concentration of a unified architecture than ever before.

Operator

Your next question comes from the line of Joe Moore with JPMorgan. Please go-ahead.

Joseph Moore
Analyst at Morgan Stanley

Good morgan Stanley. Actually, thank you. I wonder if you could talk about GB 200 at CES. You sort of talked about the complexity of the 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 have you maintained your enthusiasm for the NVL 72 platforms.

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

Well, I'm more enthusiastic today than I was at CES. And the reason for that is because we've shipped a lot more at CES. And we have -- we have some 350 plants manufacturing the 1.5 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 by delivering some $11 billion 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 our own design teams and software teams. Core Weave has now been quite public about the successful bring-up of theirs. Microsoft has, of course, OpenAI 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.

Operator

Your next question comes from the line of Vivek Ara with Bank of America Securities. Please go-ahead.

Vivek Arya
Analyst at Bank of America Merrill Lynch

Thank you for taking my question. Colette, if you wouldn't mind confirming if Q1 is the bottom for gross margins? And then Jensen, my question is for you. What is on your dashboard to give you the confidence that this strong demand can sustain into next year and has Beep and whatever innovations they came up with, has that changed that view in any way? Thank you.

Colette Kress
Executive Vice President and Chief Financial Officer at NVIDIA

So 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 manufacturings to make sure that we can provide to customers as soon as possible. Our is 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.

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

We know several things, Vivek. We have a fairly good line-of-sight of the amount of capital investment that data centers are building out towards. We know that going-forward, the vast majority of software is going to be based on machine reasoning AI for 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 start-ups that are still coming online as new opportunities for developing the next breakthroughs in AI, whether it's physical AIs.

The number of start-ups are still quite vibrant and each one of them need a fair amount of computing infrastructure. And so I think the -- whether it's the near-term 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 another way to think about that is, is we've really only tapped a consumer AI and search and some amount of consumer generative AI, advertising, recommenders, kind of the early days of software. The next -- the next wave is coming, AI for enterprise, a physical AI for robotics and sovereign AI as different regions build-out, their are barely off the ground and we can see them. We can see them because obviously, we're in the center of much of this development and we can see great activity happening in all these different places and these will happen. So near-term, mid-term, long-term.

Operator

Your next question comes from the line of Sur with JPMorgan. Please go-ahead.

Harlan Sur
Analyst at J.P. Morgan

Blackwall 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 Blackwall 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 Blackwall ultra in the second-half of this year?

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

Yes. Blackwall 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 many people helped us recover at the speed of light. And so now we've successfully ramped production of Blackwell. But that doesn't stop the next train. The next train is on an annual rhythm and Blackwell Ultra with new networking, new memories and of course, new processors and all of that is coming online.

We've been working with all of our partners and customers and laying this out. They have all of the necessary information and we'll work with everybody to do that. Et-cetera, the system architecture is exactly the same. It's a lot harder going from Hopper to Blackwell because we went from an MVLink 8 system to a MVLink 72-based system. So 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. Blackwall ultra will slot right in.

And we've also already revealed and been working very closely with all of our partners on the click after the, 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, big, huge step-up. And so come to GTC and I'll talk to you about Blackwell Ultra, Vera Rubin and then show you show you what's the one click after that. I'm really, really exciting new products that come to GTC, please.

Operator

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

Timothy Arcuri
Analyst at UBS Group

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? Thanks.

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

In some ways completely different in some areas we intersect. We're different in several ways. One, NVIDIA's architecture is general. Whether you've optimized for auto-reggressive models or diffusion-based models or 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 most exciting innovations and algorithms. And so by definition, we're much, much more general than narrow.

We're also really good from the end-to-end from data processing, the curation of the training data to the training of the data, of course to reinforcement learning used in post-training all the way to inference with a tough time scaling. So we're general, we're end-to-end and we're everywhere. And because we're not in just one cloud, in every cloud, we could be on-prem, we could be in a world, multiple and a great target, initial target for anybody who's starting up a new company and so we're everywhere.

And then the third thing I would say is that our performance and our rhythm is so incredibly fast. But 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 datacenter, if the performance or the throughput in that 100 megawatt or that gigawatt datacenter is four times or eight times higher, your revenues for that gigawatt data center is eight times higher. And the reason -- the reason that is so different than data centers of the past is because AI factories are directly monetizable through its tokens generated.

And so the token throughput of our architecture being so incredibly fast is just incredibly valuable to all of the companies that are building these things for revenue generation reasons and capturing the fast ROIs. And so I think the -- the third reason is performance. And then -- and then the last thing that I would say is the software stack is incredibly hard. Building an ASIC is no different than what we do. We build a new architecture and the ecosystem that sits on-top of our architecture is 10 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 our 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 -- I would say that those four reasons. And then finally, I will say this, just because the chip is designed doesn't mean it gets deployed. And you've seen this over and over again, there are a lot of chips that gets built, but when the time comes, a business decision has to be made. And that business decision is about deploying a new engine, a new processor into a limited AI factory in size, 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 feint of heart as everybody knows now. And so there's a lot of different reasons why we do well, why we win.

Operator

Your next question comes from the line of Ben Reids with Malius Research. Please go-ahead.

Ben Reitzes
Analyst at Melius Research

Yeah. Hi, Ben Writes is here. Hey, thanks a lot for the question. Hey, Jensen, it's a geography related question. You did a great job explaining some of the demand underlying factors here on the strength. But US was up about $5 billion or so sequentially. And I think there is a concern about whether US 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 US 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 mix-shift towards the US? Your guidance looks like China is probably up sequentially. So just wondering if you could go through that dynamic and maybe Collect can weigh-in. Thanks a lot.

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

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 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. And if you were to buy a quarter of milk is delivered to you, you, AI was involved. And so almost everything that a consumer service provides AI is at the core of it. Every student will use AI as a tutor. A healthcare services use AI, financial services use AI. No fintech company will not use AI. Every fintech company will. I'm a climate tech company uses AI. Mineral discovery now uses AI.

The number -- 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, we're -- I do believe that we're at the beginning of this new transition. And what I mean by that in the beginning is, remember, behind us has been decades of data centers and decades of computers that have been built and they've been built for a world of hand coding and general-purpose computing and CPUs and so on and so forth.

And going-forward, I think it's fairly safe to say that 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, the future computers will be based on AI. And we're really at 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, and no technology has ever had the opportunity to address a larger part of the world's GDP than AI. That 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.

Operator

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 Lupas with Evercore ISI. Please go-ahead.

Mark Lipacis
Analyst at Wells Fargo & Company

Hi, that's. Thanks for taking the question. I had a clarification and a question, Colette for the clarification. Did you say that enterprise within the data center grew 2 times year-on-year for the January quarter? And if so, does that -- would that make it the faster-growing than the hyperscalers?

And then, Jenson, for you, the question, hyperscalers are the biggest purchasers of your solutions, but they buy equipment for both internal and external workloads, external workflows being cloud services that enterprises use. So the question is, can you give us a sense 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.

Colette Kress
Executive Vice President and Chief Financial Officer at NVIDIA

Sure. Thanks for the question regarding our enterprise business. Yes, it grew 2 times 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.

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

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 the useful life of our infrastructure is much better. If the useful life is much longer, then the TCO is also lower. And so the second part is how do we see the growth of enterprise or not CSPs, if you will, going-forward.

And the answer is, I believe long-term, it is by far larger. And the reason for that is because if you look at the computer industry today and what is not served by the computer industry is largely industrial. 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 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 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 today, there's 1 billion cars on the road. Someday, there be a billion cars on the road and every single one of those cars will be robotic cars and they'll all be collecting data and we'll be improving them using an AI factory where they -- whereas they have a car factory today in the future, they'll have a car factory and an AI factory. And then inside the car itself is a robotic system.

And so as you can see, there are three computers involved. And there's the computer that helps the people, there's the computer that builds the AI for 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 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 AI have to go learn those physical effects.

So we call that physical AI. That whole part of using AI to revolutionize the way we work inside companies, that's just starting. This is now the beginning of the agentic AI era and you hear a lot of people talking about it and 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. The world -- the world of the world's GDP is representing -- represented by either heavy industries or industrials and companies that are providing for those.

Operator

Your next question comes from the line of Aaron Rakers with Wells Fargo. Please go-ahead.

Aaron Rakers
Analyst at Wells Fargo & Company

Yeah, thanks for letting me back-in. Jonathan, 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 -- if it's 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.

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

Yeah, I appreciate it. First of all, people are still using and Pascals and. And the reason for that is because always things that because CUDA is so programmable, you could use it right well. One of the major use cases right now is data processing and data curation. You find a circumstance that an AI model is not very good at. You present that circumstance to a vision language model, let's say, let's say it's a car. You present that circumstance to a vision language model. The vision language model actually looks at the circumstances and said, this isn't -- this is what happened and I wasn't very good at it.

You then take that response the prompt and you go and prompt an AI model to go find in your whole link of data 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 to go and do data processing and data curation and machine-learning based search and then you create the training dataset, which you then present to your hopper systems for training. And so each one of these architectures are completely 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 installed-base of the past

Operator

We have time for one more question and that question comes from Atif Malik with Citi. Please go-ahead.

Atif Malik
Analyst at Smith Barney Citigroup

Hi, thank you for taking my question. I have a follow-up question on gross margins for Colette. Colette, I understand there might be and you kind of tiptoed the earlier question if April quarter is the bottom but second-half would have to ramp like 200 basis-points per quarter to get to the mid 70s range that you're giving for the end-of-the fiscal year and we still don't know much about tariff's impact to broader semiconductor trajectory in the back-half of this year?

Colette Kress
Executive Vice President and Chief Financial Officer at NVIDIA

Yeah. Thanks for the question. Our gross margins, they're quite complex in terms of the material and everything that we put together in a the opportunity to look at a lot of different pieces of that on how we can 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 understand further what the US government's plan is, both its timing, it's where we are awaiting, but again, we would of course always follow export controls and/or tariffs in that manner

Operator

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

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

Thank you.

Colette Kress
Executive Vice President and Chief Financial Officer at NVIDIA

We're going to open up to Jensen and a couple of things.

Jensen Huang
Founder, President and Chief Executive Officer at NVIDIA

I just want to thank you. Thank you, Colette. AI is evolving beyond perception and generative AI into reasoning. Was reasoning AI we're observing another scaling law, inference time or test time scaling. The more computation, the more the model thinks, the more eyes, ROT 3, R1 are reasoning models that apply inference time scaling. Reasoning models can consume 100 times more compute. Future reasoning models can consume much more compute. R1 has ignited global enthusiasm. It's an excellent innovation, but even more importantly, it has open sourced a world-class reasoning AI model.

Nearly every AI developer is applying R1 or chain of thought and reinforcement learning techniques like 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 multimodality and pre-training is still growing. But it's no longer enough. We have two additional scaling dimensions. Post-training skilling, where reinforcement learning, fine-tuning, model distillation require orders of magnitude more compute than pre-training alone.

Inference 100 times more compute. We design 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 MV-Link 72 scale-up fabric and new software technologies led Blackwell process reasoning AI models 25 times faster than is in-full production. Each Grace Blackwall NBLink 72 rack is an engineering marble, 1.5 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. Going-forward, data centers will dedicate most of CapEx through accelerated computing and AI. Data centers will increasingly become AI factories and every company will have 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 about new computing, networking, reasoning AI, physical AI products and a whole bunch more. Thank you.

Operator

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

Corporate Executives
  • Stewart Stecker
    Investor Relations
  • Colette Kress
    Executive Vice President and Chief Financial Officer
  • Jensen Huang
    Founder, President and Chief Executive Officer
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