Q1 2027 Earnings Call Transcript

Good afternoon. My name is Sarah, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's First 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 1 on your telephone keypad. If you would like to withdraw your question, press star 1 again. Thank you.

Toshiya Hari, you may begin your conference.

Thank you, and good afternoon, everyone. Welcome to NVIDIA's conference call for the 2027. With me today from NVIDIA are Jensen Huang, president and chief executive officer and Colette Kress, executive vice president and chief financial officer. 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 2027. The content of today's call is NVIDIA's property.

It cannot be reproduced or transcribed without our prior written consent. During this call, we may make forward looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10 k and 10 q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission.

All our statements are made as of today, 05/20/2026, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non GAAP financial measures. You can find a reconciliation of these non GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website.

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

Thank you, Toshiya. Delivered an exceptional quarter. With revenue, operating income, and free cash flow exceeding our prior records. Total revenue of $82 billion was up 85% year over year and 20% sequentially.

This marked our third consecutive quarter of year over year acceleration and the fourteenth straight quarter of sequential growth. A significant feat given the sheer size and complexity of our manufacturing operations. The $13.5 billion sequential revenue increase was also a record. We capitalized on the inflection and inference demand by ramping Blackwell systems across our diverse end customer base.

From hyperscalers to model makers to AI cloud providers and sovereign customers. In Q1, we also allocated capital effectively across R and D, investments in our ecosystem, and share repurchases. We returned a record $20 billion to our shareholders while executing strategic investments, both upstream supply chain and downstream go to market ecosystem. This is critical to the market's development and our long term position.

Data center revenue of $75 billion was up 92% year over year and 21% sequentially. Driven by sustained strength in our Blackwell architecture. And demand for GB300 and NVL72 was particularly strong with frontier model builders and hyperscalers each having cumulatively deployed hundreds and thousands of Blackwell GPUs. marked the fastest product ramp ouour company's history. Grace Blackwell is the fastest training system as well as the lowest token generation cost at inference.

Spectrum-X, our end to end Ethernet platform purpose built for AI is now larger than all Ethernet network peers combined. InfiniBand has also had a very strong quarter growing more than 4x year over year driven by deployments of our next generation XDR technology. For your models, data center computing revenue of $60 billion was up 77% year over year, while data center networking revenue of $15 billion nearly tripled year over year. Before we deep dive into data center, we would like to brief you on our transition to a new reporting framework that better reflects our current and future growth drivers.

We have 2 market platforms. Data center and edge computing. Within data center, we will report to submarkets. Hyperscale and ACIE.

Which incorporates AI clouds, industrial, and enterprise. Hyperscale will include revenue from the public cloud, and the world's largest consumer Internet companies, while ACIE, addresses our growth opportunities in diverse AI purpose built data centers, and AI factories across industries and countries. Edge computing highlights devices for agentic and physical AI. Including PCs, gaming consoles, workstations, AI RAN base stations, robotics, and automotive.

For your reference, we have posted on our website a revenue breakdown based on our new platforms for the past 9 quarters. Moving back to our data center results. Hyperscale at $38 billion was approximately 50% of data center revenue and increased 12% quarter over quarter. ACIE revenue was $37 billion and grew 31% quarter over quarter including AI cloud revenue that more than tripled year over year.

Our customers have enabled rapid stand up of AI compute capacity. The number of partner data centers exceeding 10MW has nearly doubled in just 1 year. Now surpassing 80 sites. Sovereign revenue increased more than 80% year over year.

NVIDIA AI infrastructure is now deployed across nearly 40 countries representing $50 trillion in GDP. As evident to our Q1 results our customer base is diverse and growing. Supported by our vast ecosystem and installed base, breadth of CUDA accelerated application, and the lowest token cost provider. We are well positioned to address a market opportunity that far exceeds that of any other AI computing platform.

Demand for AI infrastructure continues to expand at an unprecedented pace. The build out of AI factories is accelerating. The value of NVIDIA AI infrastructure is rising. The price of renting an H100 has risen 20% year to date, while A100 cloud pricing is up nearly 15%.

Benefiting from the versatility of our platform and continuous performance enhancements enhanced by our software stack, customers are generating profitable revenue beyond the depreciable life of their GPUs. The vast and trusted marketplace for NVIDIA compute is a critical foundation on which billions in AI infrastructure spending is being financed by the ecosystem. There are 2 primary drivers behind the accelerating build out of AI infrastructure. First, from search and advertising to recommender systems and content understanding.

The largest hyperscale workloads continue to transition from CPU to GPU based accelerating computing. Second, the adoption of products and services native to AI is inflecting. Since the advent of ChatGPT, we have witnessed mainstream AI transition from 1 shot inference to reasoning and to now agentic. AI is no longer a nice to have.

AI is now a necessity for enhancing productivity across all industries and roles. This is propelling revenue acceleration across all layers of the AI cake. Including energy, chips, infrastructure, models, and applications. Growth in the model layer particularly at Anthropic and OpenAI, has been incredible with momentum continuing to accelerate.

Including breakout growth in OpenAI's codec since the launch of GPT 5.5. With analysts now forecasting hyperscale CapEx to exceed $1 trillion by 2027 and Agentic AI beginning to proliferate all industries AI infrastructure spending is on track to reach $3 trillion to $4 trillion annually by the end of this decade. Our Blackwell architecture is everywhere. Adopted, and deployed by every major hyperscaler every cloud provider, and every major model maker. Last month, we celebrated OpenAI's launch of GPT 5.5 codesigned for trained with, and served on Blackwell.

Currently positioned at the top of Artificial Analysis leaderboards, Microsoft's Farweave the world's most powerful AI data center is now live. Ahead of scheduled powered by hundreds of thousands of Blackwell GPUs. Starting this year, AWS will add more than 1 million Blackwell and Rubin GPUs and are collaborating on spectrum networking. At Google, Blackwell will be offered to customers in the cloud including confidential computing capability, a new foundation for secure high performance AI.

Our share of frontier AI compute is increasing We have deepened our collaboration with Anthropic and are delighted to be a strategic partner to expand their compute capacity. We will support the company's growth trajectory through AWS, Azure, CoreWeave, StacyX AI, and more. Now with the addition of Anthropic, to OpenAI, Gemini, StacyX xAI, Meta, MSL, Microsoft AI, TML, Reflection, Perplexity, Cursor, and other major frontier labs already building on NVIDIA. Our share of frontier AI models will grow significantly.

Today's data centers are revenue generating AI factories constrained by power and capital AI factory operators must choose the right architecture. With our extreme codesign approach, we deliver the industry's lowest token cost the highest token throughput, and the highest ROI. MLPerf inference results are in. And once again, we swept every benchmark.

As Blackwell Ultra delivered the highest throughput across the broad set of models and deployment scenarios. Full stack innovations drove the 2.7x increase in throughput and a 60% reduction in the cost per token on GV300 compared to just 6 months ago. NVIDIA compute is not just the highest performance AI infrastructure, It is the most economic and financeable. Customers do not buy GPUs.

They build AI factories. And the right economic metric is not the purchase price of the GPU, It is the lifetime cost an AI factory producing intelligence, token per watt, tokens per dollar, uptime, utilization, time to production, software durability, and asset life. NVIDIA excels at all of them. Agentic AI and reinforcement learning represents new growth opportunities for CPUs.

Building on the success of our Grace CPU, Vera is arriving just in time to meet this inflection. Built on custom arm cores and codesigned end to end with Rubin GPUs and NVLink, Vera will deliver up to 1.5x faster performance per core. 2x performance per watt, and 4x density per rack compared to x 86 based alternatives. VeraCPU opens a brand new $200 billion TAM for NVIDIA, a market we have never addressed before. And every major hyperscale and system maker is partnering with us to get it deployed.

We have visibility to nearly $20 billion in total CPU revenue this year. Setting us up to become the world leading CPU supplier. Our annual product cadence, a pace that is unmatched, remains a key pillar supporting our market position. We are on track to commence production shipments of VeraRubin in the second half of this year starting in Q3. By integrating 7 purpose built chips across 5 accelerated racks, VeraRubin will deliver up to 35x higher inference throughput and up to 10x greater AI factory revenue compared with Blackwell.

As an early adopter, Google's XGS bare metal instances, can support up to 960 thousand Rubin GPUs across multiple sites. Can enable customers to run their largest AI workloads on NVIDIA's optimized infrastructure. While the US government has approved licenses for H200 to be shipped to China based customers, We have yet to generate any revenue. And we are uncertain whether any imports will be allowed into the country.

As a result, consistent with last quarter, we are not including any China data center compute revenue in our outlook. Let me move to edge computing. Our edge computing market platform generated $6.4 billion up 10% quarter over quarter and 29% year over year. Robust Blackwell workstation demand a strong contributor to the growth, while consumer demand fell modestly due to higher memory and system prices.

Our physical AI continues to gain momentum exceeding $9 billion in revenue over the last 12 months. Our partnership with Uber will power the robotaxi fleet across nearly 30 cities and 4 continents by 2028. And in robotics, leading companies across a range of industrial, surgical, and humanoid applications are building on NVIDIA's technology, to develop and deploy at scale. We remain front footed in securing sufficient supply to support our customers' growth. In Q1, we increased total supply inclusive of inventory purchase commitments on prepaids to $145 billion.

While we are not immune to supply challenges, we remain confident in our ability to support the growth opportunity ahead. With our intense focus, scale, and long standing partnerships with critical suppliers, continuing to serve us well. Let me move to the rest of the P&L. GAAP gross margin was 74.9%, and non GAAP gross margins was 75%.

Largely flat sequentially by Blackwell Systems continued to account for most of our shipments. GAAP and non GAAP operating expenses were up 12% sequentially, primarily due to higher compensation and an increase in compute and infrastructure costs. Our non GAAP effective tax rate of 16% came just below our prior outlook due to favorable geographic mix. And on our balance sheet, days sales outstanding was 45 days, due to favorable timing of collections, We expect to return to the mid fifties in Q2. We generated record free cash flow of $49 billion, up from $35 billion in Q4. I would now like to update you on our capital allocation plan.

First, to reiterate, our intention is to prioritize R&D and strategic investment. Both will enable us to cultivate our ecosystem, drive market growth, and strengthen our market position. As a key enabler of AI, we will make investments necessary to deliver the industry's lowest cost per token and the highest token throughput. Which will help our customers and partners scale and expand the AI frontier.

Return program is another key component of our capital allocation strategy. With confidence in our long term free cash flow outlook, and our commitment to sharing our success with shareholders we are increasing our quarterly dividend from $0.01 to $0.20 per share. We plan to review our dividend on a regular basis as we continue to scale our business. We are also announcing an $80 billion share repurchase authorization which is in addition to the $39 billion remaining on our current plan.

As we indicated at GTC, we plan to return roughly 50% of free cash flow to shareholders this year. Let me turn to the outlook for the second quarter. Total revenue is expected to be $91 billion, plus or minus 2%. We expect sequential growth to be driven primarily by data center.

We are continuing to work vigorously on our supply chain ecosystem to address the incredible demand we see ahead of us. Giving us full confidence in $1 trillion in Blackwell and Rubin revenue we foresee from 2025 through calendar 2027. GAAP and non GAAP gross margins are expected to be 74.9%, 75% respectively plus or minus 50 basis points. For the full year, we are still expecting to be in the mid seventies. Gap and non GAAP operating expenses are expected to be approximately $8.5 billion and $8.3 billion respectively.

For the full year, we now expect OpEx to grow somewhere in the upper forties on a year over year basis. Driven by higher R&D and acceleration in the usage of AI tools to enhance productivity. For the full year 2027, we expect GAAP and non GAAP tax rates to be between 16-18%, excluding any discrete items from material changes to our tax environment. This is lower than our prior expectation of 17% to 19% due changes in geographic mix.

That puts me at the end of this part and I am going to now turn this over to the Q&A with Toshiya.

Thanks, Colette. We will now transition to Q&A. Operator, please poll for questions.

Thank you. At this time, I would like to remind everyone, in order to ask a question, press star, then the number 1 on your telephone keypad. Thank you. Your first question comes from Joseph Moore with Morgan Stanley. Your line is open.

Great. Thank you for letting me ask the question. I guess I would like to ask what drove the change in segmentation? what is the philosophy behind giving us the numbers that way? And then can you talk about any competitive differences between the 2 segments and this kind of surprising CPU number that you talked about? How do you see that across the 2 segments as well? Thank you.

Jen-Hsun Huang: Yeah. Thanks, Joe. First of all, Colette meant to say we are increasing our quarterly dividends from $0.01 to $0.25. I think that extra $0.05 would mean a lot to the large shareholders. So anyhow, let's see. Joe on the segmentation and the description of the business, we wanted to understand our business better. AI is very diverse. And computing is diverse. They are diverse in several ways. The first thing, of course, is AI includes languages and depending on the different industries, it could be 3D graphics. For manufacturing and industrial robotics. It could be proteins for life sciences. It could be small chemicals for life sciences or material sciences. It could be physics. For the physical sciences, whether it is in the energy sector or, of course, the science labs, higher education, so on and so forth. So AI is diverse. The second thing is the applications are diverse. It could be in enterprise, It could be in the energy sector, manufacturing sector, and such. Where it runs is diverse. It could be in the hyperscale cloud. It could be AI natives. These are a whole network of AI natives that are cropping up around the world. Enterprises on prem industrial in the factories, in the plants, all the way to supercomputing centers, and the edge. Edge including, of course, what is what most people see, self driving cars, robotics. But a large growing network of computers inside manufacturing plants whether it is a chip plant or packaging or computer plants, all kinds of different types of manufacturing plants. And then, of course, in the future, every single base station, every single radio network would become an AI powered radio network. And so where it runs and then lastly, how it is governed. You know, it could be operated by public cloud. But it could also have regular industrial regulatory reasons that prevents it from being run in a regulatory cloud. It could be because of confidential computing. It could be because of national security reasons. Different data centers have to be built differently. NVIDIA is quite unique in the sense that we are the only company that builds all of the technology components. We build it in an extreme codesign way. In a complete end to end way, in a full stack way, But then we, of course, open the platform so that it could be integrated into all the different environments. But some environments just require an enterprise, for example, require a company who has all of the technologies working together so that they do not have to build it. They would like to buy it and operate it. And so there is many different segments of the data center market where NVIDIA's total solution, fully integrated solution, with full stack, but still open, that way of doing of producing or delivering products is really, really important. And so if you look at our different segments, the way we broke it out into 3 large segments, You take all of the words that I just said, and you try to find the simplest factoring of it. It would be the hyperscale clouds, That would be 1 large segment. And within that segment, there is 3 different ways that we operate. First way is that we help the hyperscale clouds accelerate their data processing and machine learning workloads. We accelerate and support their AI processing inside. We also, of course, bring a lot of business, NVIDIA ecosystem business to their public clouds. And so that is 1 segment. The second segment is AI natives. Enterprise on premise, industrial on premise. And that and sovereign AI. That segment is growing incredibly fast. Because everybody needs AI, and we are gonna see AI being adopted by every industry, every country, every company. And so everybody wants to build it in a different way. And the fact that we provide the entire solution it makes it much easier, makes it possible at all for people to be able to build these things. And then, of course, the robotic edge. Today, yesterday's computing was largely about personal computing. In the future, it is gonna be about personal AI. And that personal AI, 1 example of it is the self driving car. it is a car. it is a robotic system that is essentially your personal AI. And, of course, there will be all kinds of different types of robotic systems, including even the base station radio network as I mentioned, is gonna be essentially a robotic system. So that is the reason why we broke it all apart this way. it is the simplest way of understanding our business Each 1 of them have different stacks in a lot of ways. They have different operating systems. They operate in a different way. We go to market very differently in each 1 of them. The easiest go to market, of course, is the hyperscaler because they are they are only you know, 5 or 6 of them. But the rest of them, the rest of the industry represents a couple of 250 thousand companies around the world. That go to market is very complex, very diverse. Your understanding of AI has to be extremely diverse. And as you know, NVIDIA has a large the largest suite of acceleration libraries in the world from computational lithography to fluid dynamics to particle physics to molecular dynamics to the list goes on. And all of those libraries are essential for us to engage the vertical industries that represents the second and the third category. Okay? So, anyways, it is really about the fact that our business has now evolved and grown to such a large scale it is helpful to segment it so that you have a better understanding of how our business works.

Your next question comes from Benjamin Reitzes with Melius Research. Your line is open.

Hey, guys. Thank you so much. I wanted to ask Jensen I want to ask you about your philosophy on growth. Your data center business ex China grew about 120% in the quarter, and then you are guiding about 100 CapEx at the hyperscalers is forecast by many, including myself, to, like, grow 90 to a 100% this year.

And you talked about data center still on track to be $3 trillion to $4 trillion by the end of the decade. I was just wondering the goal for the company to grow faster than hyperscaler CapEx? Do are you still comfortable in kind of endorsing that view? And do you still see hyperscaler CapEx kind of still growing after this year at a very rapid clip?

Thanks a lot.

Jen-Hsun Huang: Yeah. Thanks, Benjamin. So first of all, we should be growing faster than hyperscale CapEx. And the reason for that is illustrated by the segmentation that I just described. Our data center business has 2 large parts. It has more parts than that, but we combined it into 2 large parts for simplicity's sake. it is much more complex than the 2 large parts. But I combined it into 2 so that it is is at least easier to understand. Okay? And so if you look at the first part is hyperscalers. that is the that is the hyperscale CapEx that you were just talking about. And there are trillion dollars this year I have every expectation it is gonna grow from here. For fundamentally good reasons. This is the way computing is gonna work in the future. And if they do not compute they will not have the revenues. It is very clear Compute is revenues. Compute is profit. And so the world is changing. Software did not used to use SaaS. It did not used to use as much compute. But AI requires a tremendous amount of compute. But you could do of course, incredibly more. Which is the reason why we heard about the AI frontier, AI companies both Anthropic and OpenAI, growing at an incredible pace. The fact that they can grow within 1 month what some of those SaaS companies would have taken a decade to grow tells you something. And so the first category is hyperscale. And the CapEx is at a trillion dollars. And it is growing. Towards the 3 to 4. The second category. The second category is all of the AI native clouds. They are regional, They are all over the place. There are startups all over the world. Supporting those companies. Their enterprise 250 thousand enterprise companies around the world. Many of them will have to build or want to build AI factories themselves to operate? Many industrial companies there is no choice but to put the computer where the context is, where the action is, You cannot put that in the cloud. It has to respond. Reliably quickly, every single time. cannot imagine you know, a chip plant a chip fab being connected to a cloud service provider. Does not make any sense. And so the second category and the sovereign AI clouds And so there is a whole category of data centers that semi custom chips just do not apply because these data centers wanna buy systems They wanna operate systems. They do not wanna design. They do not wanna build it themselves. And so the second category is extremely diverse. Instead of 6 or 7 companies representing the revenues associated with our first category, The second category is hundreds, thousands of companies, and in the future, it will be hundreds of thousands of companies. With a large collect you know, large number of companies with smaller installations. And that category is gonna continue to grow at incredible pace. This set second category when I talk about physical AI and I talk about how the rest of the $100 trillion industry that has not been impacted by IT in the last 30 years it is about to be impacted by AI. That is the segment that I am talking. The second cluster is growing incredibly fast. Our share of that, of course, is very, very large. We are fairly unique in our abilities to be able to serve this industry. Our platform is built like it is vertically integrated so that everything works. But then we disassemble it so that people could build and buy it in the configure they want and assemble it the way they like. And so this second category is fairly poorly understood. Because there are just so many small companies or so many companies and each 1 of the installations are relatively small compared to, of course, 1 of the hyperscalers. And so if you look at the segmentation and the size of each you could see that, in fact, we are growing share in the hyperscalers because we now have much bigger support from Anthropic, a new partner of ours, and we are helping them expand their capacity greatly in the coming years. And then the second very few companies have exposure into the second category. Because of the platform solution that we have.

Your next question comes from Christopher Muse with Cantor Fitzgerald. Your line is open.

Vera Rubin coming soon, and you obviously have great insight into coming updates to Frontier models, new techniques to optimize around diverse AI workloads with investors keenly focused on your market share and inference, how do you see Vera Rubin in your extreme co engineering impacting your share of the inference market, you know, as we look into late 26, 27?

Jen-Hsun Huang: Well, we are growing share in inference. And we are growing share in inference very, very quickly. And the reason for that is this year, the number of frontier model companies grew. And so there is Cursor and Perplexity and there is some new model companies, TML and Reflection and list goes on. And so the number of frontier model companies has grown. And we added Anthropic to our partnership this year. They are expanding incredibly fast. We have partnered with them to secure computing capacity across Azure, AWS, CoreWeave, I forget who else we have already announced, but there is a whole list of others that we are bringing online for them. So the amount of capacity that we are gonna bring online for Anthropic this year and next year is going to be quite significant. Very significant. And so we are growing and our coverage of anthropic has been largely zero until this until just recently. And so we are gaining share tremendously fast in inference. VeraRubin is going to be even more successful than Grace Blackwell at this point. Every single I cannot think of 1. Every single frontier model company will jump on VeraRubin from the get go. And that was not true before on Blackwell. And so VeraRubin is off to a tremendous start and it will it will surely be more successful than even Grace Blackwell. So I think the end of your answer, CJ, is that we are gaining share in inference. Let me let go back again. To the question that Benjamin was asking Remember, so far, everything that I have just explained in the in the inference question is really focused on hyperscale. Remember, there is a whole second category of AI data centers that we serve almost uniquely. Now this segment is very fragmented requires a fairly integrate a really well integrated platform solution. And a very large go to market. And that segment all of the inference, 100% of that, the vast majority of that is NVIDIA. And then, of course, physical AI. NVIDIA is the practically the only company serving physical AI today. And we have been working on physical AI for a long time. And so that is also growing. So our share of inference is growing very quickly.

Your next question comes from Timothy Arcuri with UBS. Your line is open.

Thanks a lot. Jensen, I wanted to ask about the traction you are getting with some of these custom merchant things you are doing, stuff like, you know, CPX and LPX. And I just wanted to ask and see sort of you know, you have talked before about fat synthesis being, I think, 20% of the market. So would imagine you are getting pretty good traction with LPX.

So can you just talk about that and maybe, you know, also how that fits into your broader platform strategy? Thanks.

Jen-Hsun Huang: The LPX is designed for low latency and high token rate. Its throughput is low, Size capacity is low. And it is context processing, its ability to absorb a lot of context, for example, for software coding. For agentic workloads, its ability to absorb a great deal of context is lower. And so and so the challenge the challenge is simply and I have explained before, that the use case for LPX is not broad. it is, you know, intended for somebody who has a fairly large portfolio of different types of token services, And for the high token rate, maybe these services are quite premium. And the number of customers is not significant. But the token rate is very high. So that remains exactly consistent with what I have said before. And I still expect that. And so I expect that LPX and other SRAM based decode focus, token you know, high token rate generate generated focused accelerators. Will always be will be a niche product for some time. For some time to come. You know, as you know, Grace Blackwell and Vera Rubin we support the entire life cycle of AI from the data processing, preparing for training, Okay? Data processing. To pretraining, to post training reinforcement learning all the way to inference. Grace Blackwell is the best platform in the world to do all of that. And if we if in certain circumstances, so long as the customer the provider already has a high token rate service that they can offer, then we can tack on an LPX and they could deliver that service even better. And so that is how I see the market. And I think I think whether it is 20% or 10% just depends on where we are in the development of AI. I think today, it is a lot less than 20%. Someday, these premium tokens could be 20%. And I am know, we are we are ready to work with work with service providers to enable this capability.

I am excited about Your next question comes from Vivek Arya with Bank of America Securities. Your line is open.

Thanks for taking my question. Jensen, there is a lot of excitement around CPU for Agentic applications and just a lot of noise around the number of CPUs actually exceeding the number of GPUs. And just hoping that you could kind of, you know, give your perspective that, first of all, you know, is this an incremental workload? Is this kind of cannibalizing what the GPU would have done otherwise?

And then secondly, the $20 billion number that you gave, is that for stand alone Vera CPUs, or is that kind of already included in that Vera as part of Vera Rubin? So just if you could educate us educate us on, you know, the role of CPU versus GPU, is it cannibalistic? Is it incremental? And then the $20 billion number, how to kinda put that in context with what you sell, right, which is usually the CPU as part of the GPU?

Thank you.

Jen-Hsun Huang: The 20 billion is for standalone CPU. And remember, we have we have Vera is used in 3 ways. As a standalone c 4 ways. As a let me just start with the 1 that you already know. The first way is VeraRubin. And we will sell millions of rubins and every 2 of them is connected to avera. And, of course, we price those too. And they are properly priced. And so that is number 1 use case. The second use case is Vera standalone CPU. The third is Vera with CX9 and it is and the software stack for storage. And then Vera in a with CX 9 with a software stack for security and compute isolation and confidential computing. K? So each 1 of those use cases is built on Vera. And my sense is that we will be supply constrained throughout the entire life of VeraRubin. There are 4 different use cases of it. And but, anyhow, anyhow, the answer to your question is of the 20 billion is a stand alone. With respect to CPU use, an agent is essentially what people call a harness. The agent has a harness that does the and the harness could be OpenClaw. It could be Hermes. CodeClaude. It. Is essentially a harness around Clot. Around the Claude model. OpenAI's Codex is a harness around the GPT 5.5 model. And so these are harnesses. And these harnesses provide for things like IO, orchestration, memory management, tool use, connected to tools, for example, browsers and things like that, c compilers, Python compilers. And so the harness runs on CPU. And the tool use runs on CPUs. You know? So, for example, if the if the AI were to do a search or do a do a browser use a browser, that would run on the CPU. The world has the world has a billion human users, My sense is that the world is gonna have billions of agents. Not today. I mean, we are gonna grow into it. But we will have billions of agents. And those billions of agents will all use tools, and those tools are gonna be like you know, like PCs, just like us humans using PCs today. In the future you will have an agent using PC. And so if you kind of think along the lines of in the future, you pick your favorite number of agents at the moment, at the moment, call it a few hundred thousand, but in the future, call it, eventually a few billion. I could imagine them all using the effective effectively having PCs that they can all use. And so but the large length every 1 of those every 1 of those agents are gonna spin off sub agents And every time they spin these off, you are gonna need to do inference. that is where the thinking happens. Of the thinking happens on GPUs. All of the orchestration essentially runs on CPUs. And the sub agents, when they are spawned off, they are thinking, they use GPUs. Whenever the agents use simulators, those can run on CPUs or GPUs. Which is the reason why we are working so closely with Cadence and Synopsys and to accelerate all of the world's tools. We are accelerating all of the world's tools and data processing engines and data bay database engines because agents use these tools and have you know, they have lower patience tolerance than humans, and they want things to happen quickly. And so we are accelerating all of the world's tools so that it runs on CUDA. And you could see us doing that you know, when I work with when I work with Cadence and Synopsys and Siemens and, you know, companies and Adobe and that is because we are trying to get all of the world's tools to run on GPUs because they already have GPUs, and it is a lot faster. So we are gonna need a lot more CPUs and Vera was designed to be an agentic CPU. The CPUs of the past were designed to have many cores, so that it could be easily rentable. People rent cores. Well, agents do not rent cores. They just want the work to be done fast. The economics of the past was dollars per court. that is the that is the economics of cloud computing of the past. Economics of AI of the future is tokens per dollar. Or dollars per token. And so what we need to do in the future is to generate tokens process tokens as fast as possible. And that is what that is what Vera does incredibly well. So we are expecting to be very successful with 72 it needs incredibly great security and confidential computing, which is the reason why Vera Rubin's the world's first platform with end to end confidential computing. And it needs, you know, great CPUs. We have got it we have got it all covered.

Your next question comes from Stacy Rasgon with Bernstein Research. Your line is open.

I wanted to go back to the segmentation. So first of all, I am just curious. Where do you put the neo clouds across those 2 segments? Are they in hyperscale, or are they in the AI cloud?

Part of me assumes the latter, but I am not so sure. And then but just what is the magnitude of them? I mean, they are both about the same the same magnitude now. It almost sounded to me like you were suggesting that you thought the latter, the ACIE, cloud would grow faster.

Maybe going forward than hyperscale. Is that what you are trying to say, do you just see, like, the same kind of growth coming from both segments?

Jen-Hsun Huang: First of all, you are correct. That AI native clouds AI native clouds do not build chips, do not design their own chips. And they do not wanna they cannot really assemble you know, unrelated parts together into an AI factory. And their time their patience, their tolerance for time to first token is extremely low. And their need for an architecture that has a great deal of offtake so that it runs every model, has customers from everywhere. Is incredibly high. And so that is the reason why NVIDIA's architecture is so perfect for them. We offer every component and our and whatever we do not offer, our ecosystem of partners offer it. And it is all fully integrated. It all works together. The number of customers that could rent it from an AI native is incredibly high. Basically, every single AI builder every know, every AI native startup, around the world, SaaS companies, enterprise companies, industrial companies. And so our computing our architecture is the most rentable of any computing platform in the world. So it is the it is the most performant. it is the easiest to put together. it is the most rentable. Has the best TCO. And is the easiest to finance. And so all of those properties are quite unique to the needs of AI natives. it is in the second category. They are very similar to even OEMs and so on and so forth. Large enterprises and so forth, surprising Okay. So we put that in the second category. If you look at look at that segment, it started growing after the AI ecosystem developed in the hyperscale. Hyperscale developed AI first for a lot of reasons. You know, they have great computer science. They have excellent data center capability. And they also focus largely on consumer applications which, if not perfect, is not the end of the world, It enhances the service so long as it enhances the service. And so for many of the other applications, industrial applications, enterprise applications, until the AI is very capable and thus really productive work and does it safely and it could do it in a way that can actually generate impact and income It does not really get used. And so you expect the second category to develop slower than hyperscale. And you could see that in the numbers. However, long term, if you look at look at industrial and enterprise, clearly, that is where future economics is going to be. Because it represents some you know, $50 trillion to $80 trillion of the world's economy. And so and it is gonna be larger than that because of AI. And so I expect the second category to be to be larger over time both in the near term over the next several years. I think it is a sure it is a it is a foregone conclusion. Both are gonna grow incredibly fast I expect the second category to still grow faster, but both are gonna grow incredibly fast. And then I am hoping that within the next 5 years, physical AI and robotics segment is gonna grow incredibly fast.

Your next question comes from Jim Schneider with Goldman Sachs. Your line is open.

Good afternoon. Thanks for taking my question. Back at GTC, I believe you discussed a trillion dollars visibility into both your Rubin and Blackwell platform revenue, but I believe that excluded things like LPX Rubin CPX, the Vera CPU racks. Could you maybe give us a sense about whether the Vera CPUs are going to be the biggest source of upside above and beyond that $1 trillion Are you contemplating other sort of combinations of products, including CPUs that would allow you to gain an even greater share of that total TAM.

Thank you. In terms of in terms of incremental above the trillion, I would say, 1, the continued growing of share of the frontier AI models I am expecting to, grow more share and so I am expecting that to grow.

Jen-Hsun Huang: Number 2, we did not include any Verus CPU, standalone CPU in that in that number And so I expect that to be the second largest The TAM is, of course, quite large, and agent agents agentic systems, and all of our customers are quite excited about Vera. And we are gonna sell a whole a whole bunch of Verras. And then third would be LPX. Because I as I explained earlier, LPX is designed as a as a because of its SRAM architecture, it has the benefit of very low latency and very high interactivity. But it is also its throughput and its context processing ability is also quite limited. And that is just kind of the nature of SRAM type based systems, and but the combination you know, we will be able to address the entire spectrum of AI from a pretraining to post training to inference agentic systems through the combination of vera and VeraRubin and LPX.

Your next question comes from Joshua Buchalter with TD Cowen. Your line is open.

Hey, guys. Thanks for taking my question and congrats on the great results. Colette, I believe in your prepared remarks, you mentioned GB-3 100 is sort of the fastest ramp in the company's history. How should we think about Vera Rubin against this benchmark? it is obviously a new architecture.

At the silicon level, but it is similar rack. Does that mean we should expect a similar slope to the VeraRubin ramp as the GB300 Or it be a bit more gradual given the new silicon? Thank you.

Yeah. Well, we have indicated for a while that we will be launching VeraRubin in the second half. We will start in Q3. That will be we are probably gonna start to see our ramping continue, our initial pieces together. And then once we get to Q4, it is hard to say at this point, what will be a faster ramp, but, again, we have demand already planned We have got POs.

We have got almost all of our major customers ready to go. And these are very complex systems that we need to put together So I think it is just about the timing that it is gonna take for us to get that into market, nothing else other than getting from production of all of the different systems that we have ready for order. So little early to say, But, yes, we are gonna start in Q3. And Q1 of next year certainly is going to be And continue to ramp into Q4. very big as well.

There are no further questions at this time.

Toshiya Hari, I turn the call back over to you. Thank you. Before I hand it over to Jensen, please note Jensen will be giving the keynote at GTC Taipei at Computex. On June 1st.

We will also be participating at the TD Cowen TMT conference on May 28th and the Bank of America Global Technology Conference on June 4th. Our earnings call to discuss the results of our 2027 is scheduled for August 26th. With that, here's Jensen to close us out.

Jen-Hsun Huang: This was an extraordinary quarter. Demand has gone parabolic. The reason is simple. Agentic AI has arrived. AI can now do productive and valuable work. Tokens are now profitable, so model makers are in a race to produce more. In the AI era, compute capacity is revenue, and profits. NVIDIA is the platform of this era. Of all the platforms in the world, NVIDIA Compute supports the richest diversity of demand. Let me highlight my top 5 things. First, NVIDIA is the only platform that runs every frontier AI model. With the addition of Anthropic to our existing partners, OpenAI, xAI, Meta, Gemini, and many others, our share of Frontier AI is growing. Second, we are in every hyperscale cloud. Supporting their core data processing and machine learning workloads, internal AI services, as well as supporting their demand for NVIDIA users in their public cloud services. Third, our full stack complete AI factory solution and vast global ecosystem let us uniquely address new AI data center segments new AI cloud natives new AI native clouds and sovereign AI clouds and on premises enterprise and industrial infrastructure. This is that second category I was talking about earlier. Fourth, NVIDIA CUDA extends all the way to the edge. Robotics, autonomous vehicles, embedded medical instruments, AI RAN telco base stations, The next wave is physical AI. With billions of autonomous and robotic systems operating in the physical world. This is the third segment we were talking about earlier. And rounding out the top 5 things, we have a major new growth driver, Vera. The world's first CPU purpose built for agentic AI. Vera opens a brand new $200 billion TAM for NVIDIA, a market we have never addressed before. And every major hyperscaler and system maker is partnering with us to deploy it. The world is rebuilding computing for agentic AI and robotic physical AI. NVIDIA sits at the center of these transitions. We built NVIDIA compute platform over 3 decades. 1 architecture, vast ecosystem, extreme codesign across chips, systems, networking, and software, We built it ahead of this moment so that when agentic AI arrived, NVIDIA would be ready. It has arrived. Look forward to catching up next time.

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