NVIDIA - Earnings Call - Q4 2025

Executive Summary

• NVIDIA delivered record Q4 FY25 revenue of $39.3B, up 12% q/q and 78% y/y, with GAAP and non-GAAP diluted EPS of $0.89; Data Center revenue hit a record $35.6B, up 16% q/q and 93% y/y.
• Management guided Q1 FY26 revenue to $43.0B ±2% with GAAP/Non-GAAP GM at 70.6%/71.0% ±50bps, and expects both Data Center and Gaming to grow sequentially; gross margin is expected to return to mid-70s later in FY26 as Blackwell costs normalize.
• The quarter was propelled by a faster-than-expected Blackwell ramp ($11.0B revenue in Q4, “fastest product ramp” in company history), while Gaming declined on supply constraints; Networking dipped slightly as the company transitions from NVLink 8/InfiniBand to NVLink 72/Spectrum‑X.
• Estimates context: S&P Global Wall Street consensus could not be retrieved due to rate-limit constraints; based on company guidance, NVIDIA materially exceeded its prior Q4 revenue outlook of $37.5B.
• Catalysts: strong Q1 guide, accelerating Blackwell adoption and reasoning AI narrative, expected networking growth in Q1, and management’s confidence in margin normalization to mid-70s later this fiscal year.

What Went Well and What Went Wrong

• What Went Well

  • Record Data Center revenue ($35.6B) on Blackwell ramp and accelerating inference workloads; “We delivered $11.0B of Blackwell architecture revenue in the fourth quarter… fastest product ramp in our company’s history”.
  • Broad customer adoption across hyperscalers (Azure, AWS, GCP, OCI) and enterprises; early GB200 deployments earmarked for inference; “up to 25x higher token throughput and 20x lower cost vs Hopper 100” for reasoning models.
  • Strong Automotive momentum ($570M, +27% q/q, +103% y/y) with marquee wins at Toyota, Hyundai and autonomous deployments; ProViz up 5% q/q, 10% y/y.

• What Went Wrong

  • Gross margin compressed to 73.0% GAAP (73.5% non-GAAP), down 150–160bps q/q, primarily due to more complex/higher-cost Data Center systems during Blackwell ramp.
  • Gaming revenue fell to $2.5B, down 22% q/q and 11% y/y, driven by supply constraints despite healthy demand; management expects recovery as supply improves.
  • Networking declined 3% q/q and 9% y/y as the company transitions architectures (NVLink 8/InfiniBand to NVLink 72/Spectrum-X), though management expects networking to return to growth in Q1.

Transcript

Operator (participant)

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 would like to withdraw your question, again, press star one. Thank you. Stewart Stecker, you may begin your conference.

Stewart Stecker (Head of Investor Relations)

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 Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the first quarter of fiscal 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 as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette.

Colette Kress (CFO)

Thanks, Stewart. Q4 was another record quarter. Revenue of $39.3 billion was up 12% sequentially and up 78% year-on-year, and above our outlook of $37.5 billion. For fiscal 2025, revenue was $130.5 billion, up 114% 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 fourth 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 H200 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 to 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 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 Llama 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, DeepSeek R1, and Grok 3.

Long-thinking reasoning AI can require 100x more compute per task compared to one-shot inferences. Blackwell was architected for reasoning AI inference. Blackwell supercharges reasoning AI models with up to 25x higher token throughput and 20x lower cost versus H100. It is revolutionary. Transformer Engine is built for LLM and mixture of experts inference, and its NVLink domain delivers 14x the throughput of PCIe Gen 5, ensuring the response time, throughput, and cost efficiency needed to tackle the growing complexity of inference at scale. Companies across industries are tapping into NVIDIA's full-stack inference platform to boost performance and slash costs. Snap tripled inference throughput and cut costs by 66% using NVIDIA TensorRT for its screenshot feature. The proxy 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 obsolescence in rapidly evolving markets. Our performance and pace of innovation is unmatched. We're driven to a 200x reduction in inference costs 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 clouds 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. We've launched a 100,000 GB200 cluster-based instance with NVLink Switch and Quantum-2 InfiniBand. Consumer internet revenue grew 3x year-on-year, driven by an expanding set of generative AI and deep learning use cases. These include recommender systems, vision language understanding, synthetic data generation search, and agentic AI. For example, xAI is adopting the GB200 to train and inference its next generation of Grok AI models.

Meta's cutting-edge Andromeda advertising engine runs on NVIDIA's Grace Hopper Superchip, serving vast quantities of ads across Instagram and 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-year on accelerating demand for model fine-tuning, RAG, and agentic AI workflows, and GPU-accelerated data processing. We introduced NVIDIA Llama-Nemotron 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, as 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 CES, Hyundai Motor 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 CES, we announced the NVIDIA Cosmos World Foundation Model Platform. Just as language foundation models have revolutionized language AI, Cosmos is a physical AI to revolutionize robotics. Leading 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 Blackwell. 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 initiative offer a glimpse into the buildout 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's 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 NVL72 with Spectrum-X. Spectrum-X and NVLink Switch revenue increased and represents a major new growth factor. 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 supercomputers 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, fifth-generation Tensor Cores, and fourth-generation RT Cores, and featuring up to 3,400 AI TOPS.

These GPUs deliver a 2x performance leap and new AI-driven rendering, including neural shaders, digital human technologies, geometry, and lighting. The new DLSS 4 boosts frame rates up to 8x with AI-driven frame generation, turning one rendered frame into three. It also features the industry's first real-time application of transformer models, packing 2x more parameters and 4x the 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 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 DRIVE Orin, running the safety-certified NVIDIA DRIVE OS. We announced Aurora and Continental will deploy driverless trucks at scale powered by NVIDIA DRIVE Thor. Finally, our end-to-end autonomous vehicle platform, NVIDIA DRIVE Hyperion, has passed industry safety assessments by TÜV SÜD and TÜV Rheinland, 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. GAAP gross margins were 73%, and non-GAAP gross margins were 73.5%, down sequentially as expected with our first deliveries of the Blackwell architecture. As discussed last quarter, Blackwell is a customizable AI infrastructure with several different types of NVIDIA-built chips, multiple networking options, and for air- and liquid-cooled data centers. We exceeded our expectations in Q4 in ramping Blackwell, increasing system availability, and 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. It 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 expenses are expected to be an income of approximately $400 million, excluding gains and losses from non-marketable and publicly held equity securities. GAAP and non-GAAP tax rates are expected to be 17%, plus or minus 1%, excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website, including a new financial information AI agent. In closing, let me highlight upcoming events for the financial community. We will be at the TD Cowen Healthcare Conference in Boston on March 3rd and at the Morgan Stanley Technology, Media, and Telecom Conference in San Francisco on March 5th. Please join us for our annual GTC Conference starting Monday, March 17th, in San Jose, California.

Jensen will deliver a news-packed keynote on March 18th, and we will host a Q&A session for our financial analysts 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 28th, 2025. We are going to open up the call operator to questions. If you could start that, that would be great.

Operator (participant)

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 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 C.J. Muse with Cantor Fitzgerald. Please go ahead.

C.J. Muse (Senior Managing Director)

Yeah, good afternoon. Thank you for taking the question. I guess for me, Jensen, as test-time compute and reinforcement learning shows such promise, we're clearly seeing 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.

Jensen Huang (CEO)

Yeah, I appreciate that, C.J. There are now multiple scaling laws. There's the pre-training scaling law, and that's going to continue to scale because we have multi-modality. 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.

There you have Chain of Thought, you have search. The amount of tokens generated, the amount of inference compute needed is already 100x more than the one-shot examples and the one-shot capabilities of large language models in the beginning. And that's just the beginning. This is just the beginning. The idea that the next generation could have thousands of times and even hopefully extremely thoughtful and simulation-based and search-based models that could be hundreds of thousands, millions of times more compute than today is in our future. And so the question is, how do you design such an architecture? Some of the models are auto-regressive. Some of the models are diffusion-based. Some of the times you want your data center to have disaggregated inference, sometimes it's compacted.

And so it's hard to figure out what is the best configuration of a data center, which is the reason why NVIDIA'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, it is many times more performant. But what's really amazing is for long thinking, test-time scaling, reasoning AI models were tens of times faster, 25x 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 (participant)

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

Joe Moore (Managing Director and Head of U.S)

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 have you maintained your enthusiasm for the NVL72 platforms?

Jensen Huang (CEO)

I'm more enthusiastic today than I was at CES. The reason for that is because we've shipped a lot more to CES. We have some 350 plants manufacturing the one and a half million components that go into each one of the Blackwell racks, Grace Blackwell racks. Yes, it's extremely complicated. We successfully and incredibly ramped up Grace Blackwell, 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. 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. CoreWeave 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 come online. And so I think the answer to your question is nothing is easy about what we're doing. We're doing great. And all of our partners are doing great.

Operator (participant)

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

Vivek Arya (MD and Equity Research Analyst)

Thank you for taking my question. Could I just, 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 the strong demand can sustain into next year? And has DeepSeek and whatever innovations they came up with, has that changed that view in any way? Thank you.

Colette Kress (CFO)

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 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. Walking through what you heard Jensen speak about the systems and their complexity, they are customizable in some cases. They've got multiple networking options. 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 (CEO)

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 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 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 needs 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 have 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, kind of the early days of software.

The next wave's coming: agentic AI for enterprise, physical AI for robotics, and sovereign AI as different regions build out their AI for their own ecosystems, and so each one of these is fairly 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 (participant)

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

Harlan Sur (MD and Equity Research Analyst)

Yeah, good afternoon. Thanks for taking my question. Your next generation Blackwell Ultra is set to launch in the second half of this year in line with the team's annual product cadence. Jensen, can you help us understand the demand dynamics for Ultra, given that you'll still be ramping the current generation Blackwell solutions? How do your customers and the supply chain also manage the simultaneous ramps of these two products? And is the team still on track to execute Blackwell Ultra in the second half of this year?

Jensen Huang (CEO)

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 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, laying this out. They have all of the necessary information. And we'll work with everybody to do the 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 NVLink eight system to an NVL72-based system. So the chassis, the architecture of the system, the hardware, the power delivery, all of that had to change. This was quite a challenging transition. But the next transition will slide right in. Grace Blackwell Ultra will slide right in. We've also already revealed and been working very closely with all of our partners on the chip after that. And the chip after that is called Vera Rubin. And all of our partners are getting up to speed on the transition of that. And so preparing for that transition. And again, we're 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 chip after that.

Really exciting new product. So come to GTC, please.

Operator (participant)

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

Timothy Arcuri (MD)

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 (CEO)

We build very different things than ASICs. 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-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, 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 test-time scaling. We're general. We're end to end. We're everywhere.

Because we're not in just one cloud, we're in every cloud. We could be on-prem. We could be in a robot. Our architecture is much more accessible and a great target, initial target for anybody who's starting up a new company. And so we're everywhere. And then the third thing I would say is that our performance and our rhythm is so incredibly fast. Remember that these data centers are always fixed in size. They're fixed in size or they're fixed in power. And if our performance per watt is anywhere from 2x to 4x to 8x, which is not unusual, it translates directly to revenues. And so if you have a 100-megawatt data center, if the performance or the throughput in that 100-megawatt or that gigawatt data center is four times or eight times higher, your revenues for that gigawatt data center are eight times higher.

And the reason that is so different than data centers of the past is because AI factories are directly monetizable through its tokens generated. And so the token throughput of our architecture being so incredibly fast is just incredibly valuable to all of the companies that are building these things for revenue generation reasons and capturing the fast ROIs. And so I think the third reason is performance. And then the last thing that I would say is the software stack is incredibly hard. Building an ASIC is no different than what we do. We have to build a new architecture. And the ecosystem that sits on top of our architecture is 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 say that those four reasons. And then finally, I will say this: just because a chip is designed doesn't mean it gets deployed. And you've seen this over and over again. There are a lot of chips that get built. But when the time comes, a business decision has to be made. And that business decision is about deploying a new engine, a new processor into a limited AI factory in size, 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.

These things are not for the faint of heart, as everybody knows now. There's a lot of different reasons why we do well, why we win.

Operator (participant)

Your next question comes from the line of Ben Reitzes with Melius Research. Please go ahead.

Ben Reitzes (MD and Head of Technology Research)

Yeah, hi. Ben Reitzes 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 U.S. was up about $5 billion or so sequentially. And I think 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 shift towards the U.S.? Your guidance looks like China is probably up sequentially. So just wondering if you could go through that dynamic and maybe Colette weigh in. Thanks a lot.

Jensen Huang (CEO)

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 in percentage. With respect to geographies, the takeaway is that AI is software. It's modern software. It's incredible modern software, but it's modern software, and AI has gone mainstream. AI is used in delivery services everywhere, shopping services everywhere. And if you were to buy a quart of milk and it's delivered to 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. Healthcare services use AI. Financial services use AI. No fintech company will not use AI. Every fintech company will. Climate tech company uses AI. Mineral discovery now uses AI. The number of every higher education, every university uses AI.

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 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 two years into that journey 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.

When you take a step back and look at it from that perspective, we're really just in the beginnings.

Operator (participant)

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 Lipacis with Evercore ISI. Please go ahead.

Mark Lipacis (Senior MD)

Hi. That's Mark Lipacis. 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 2x year-on-year for the January quarter? And if so, would that make it faster growing than the hyperscalers? And then, Jensen, for you, the question, hyperscalers are the biggest purchasers of your solutions, but they buy equipment for both internal and external workloads, external workloads being cloud services that enterprises use. So the question is, can you give us a sense of how that 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 (CFO)

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 on their own work. But keep in mind, that is also where the enterprises are surfacing. Your enterprises are both with your CSPs as well as in terms of building on their own. They're both growing quite well.

Jensen Huang (CEO)

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.

The reason for that is because if you look at the computer industry today, and what is not served by the computer industry is largely industrial. Let me give you an example. When we say enterprise, and let's use a car company as an example because they make both soft things and hard things. In the case of a car company, the employees would be what we call enterprise. 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.

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 robotic cars. They'll all be collecting data and will be improving them using an AI factory. Whereas they have a car factory today, in the future, they'll have a car factory and an AI factory. Then inside the car itself is a robotic system. So, as you can see, there are three computers involved. 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 a robot that's being developed today. It could be a building. It could be a warehouse.

These physical systems require a new type of AI we call physical AI. They can't just understand the meaning of words and languages, but they have to understand the meaning of the world: friction and inertia, object permanence and cause and effect, and all of those types of things that are common sense to you and I, but 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 and 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.

My sense is that long-term, this will be by far the larger of them all, which kind of makes sense. The world's GDP is represented by either heavy industries or industrials and companies that are providing for those.

Operator (participant)

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

Aaron Rakers (Technology Analyst)

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 and 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 GB200 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 (CEO)

Yeah, I appreciate it. First of all, people are still using Volta, Pascal, and Ampere. The reason for that is because there are always things that, because CUDA is so programmable, you could use it. 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 circumstance and says, "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 data lake 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 Ampere 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, 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. All of our GPUs are very well employed.

Operator (participant)

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

Atif Malik (MD and U.S. Semiconductor Capital Equipment and Specialty Semiconductors Analyst)

Hi. Thank you for taking my question. I have a follow-up question on gross margins for Colette. Colette, I understand there are many moving parts: Blackwell yields and NVL 72 and Ethernet mix. And you kind of tiptoed around the earlier question. April quarter is the bottom. But the 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 tariffs' impact to broader semiconductors. So what kind of gives you the confidence in that trajectory in the back half this year?

Colette Kress (CFO)

Yeah. Thanks for the question. Our gross margins, they're quite complex in terms of the material and everything that we put together in a Blackwell system. Tremendous amount of opportunity to look at a lot of different pieces of that on how we can better improve our gross margins over time. Remember, we have many different configurations as well on Blackwell that will be able to help us do that. So together, working after we get some of these really strong ramping completed for our customers, we can begin a lot of that work. If not, we're 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 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.

Operator (participant)

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

Jensen Huang (CEO)

Thank you. No, no. I just wanted to thank you.

Colette Kress (CFO)

I just wanted to open up to Jensen.

Jensen Huang (CEO)

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 OpenAI's, Grok 3, DeepSeek 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. DeepSeek R1 has ignited global enthusiasm. It's an excellent innovation. But even more importantly, it has open-sourced a world-class reasoning AI model. Nearly every AI developer is applying R1 or chain of thought and reinforcement learning techniques like R1 to scale their model's performance.

We now have three scaling laws, as I mentioned earlier, driving the demand for AI computing. The traditional scaling laws of AI remain intact. Foundation models are being enhanced with multimodality, and 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 NVLin72 scale-up fabric and new software technologies let Blackwell process reasoning AI models 25 times faster than Hopper. Blackwell in all of its configurations is in full production. Each Grace Blackwell NVLink

72 rack is an engineering marvel.

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. 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.

Operator (participant)

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