Moo Live

was live ·

Nvidia Q1 FY2027 earnings conference call

Key Takeaways (AI-Generated)

Financial Performance

- Total revenue of $82 billion, up 85% year-over-year and 20% sequentially, third consecutive quarter of acceleration

- Data center revenue of $75 billion, up 92% year-over-year and 21% sequentially

- Record free cash flow of $49 billion, up from $35 billion in Q4

- GAAP gross margin of 74.9% and non-GAAP gross margin of 75%, largely flat sequentially

Business Highlights

- Blackwell architecture adoption across all major hyperscalers, marking fastest product ramp in company history

- Partnership expansion with Anthropic as strategic partner to expand compute capacity

- Vera CPU launch targeting agentic AI applications, opening new $200 billion TAM

- Physical AI exceeding $9 billion in revenue over the last 12 months

Financial Guidance

- Q2 total revenue expected to be $91 billion ± 2%

- Full confidence in $1 trillion in Blackwell and Ruben revenue from 2025 through 2027

- GAAP and non-GAAP gross margins expected to be 74.9% and 75% respectively for full year

- Plan to return roughly 50% of free cash flow to shareholders this year

Opportunities

- AI infrastructure spending on track to reach $3-4 trillion annually by end of decade

- Vera Rubin production shipments starting Q3 with up to 35X higher inference throughput

- Strategic partnerships with every major hyperscaler and system maker for Vera deployment

- Extreme co-design approach delivering 2.7X throughput increase and 60% cost reduction per token

Risks

- Supply chain challenges acknowledged, though company remains confident in ability to support growth

- US government approved H200 licenses to China but uncertain whether imports will be allowed

Full Transcript (AI-Generated)

Operator

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 speaker's remarks, there will be a question and answer session. If you would like to ask a question during this time, simply press * followed by the number one on your telephone keypad. If you would like to withdraw your question, press *1 again. Thank you, Toshiya Hari, you may begin your conference.

Toshiya Hari

Thank you and good afternoon, everyone. Welcome to Nvidia's conference call for the first quarter of fiscal 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 second quarter of fiscal 2027.

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

All our statements are made as of today, May 20th, 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.

Colette Kress

Thank you, Toshiya. We 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 14th 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 in inference demand by ramping Blackwell Systems across our diverse customer base—from hyperscalers to model makers to AI cloud providers and sovereign customers. In Q1, we also allocated capital effectively across R&D investments in our ecosystem and share repurchases. We returned a record $20 billion to our shareholders while executing strategic investments both upstream in the supply chain and downstream in the 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. The demand for GB300 and BL72 was particularly strong, with frontier model builders and hyperscalers each having cumulatively deployed hundreds and thousands of Blackwell GPUs, marking the fastest product ramp in our company's history. 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 NDR 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'd like to brief you on our transition to a new reporting framework that better reflects our current and future growth drivers. We have two market platforms: data center and edge computing. Within Data Center, we will report two submarkets—hyperscale and ACI, which incorporates AI clouds, industrial, and enterprise. Hyperscale will include revenue from the public cloud and the world's largest consumer Internet companies, while ACI 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 nine quarters. Moving back to our data center results, hyperscale revenue of $38 billion was approximately 50% of data center revenue and increased 12% quarter over quarter.

ACI 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 10 megawatts has nearly doubled in just one 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 from our Q1 results, our customer base is diverse and growing, supported by our vast ecosystem and installed base, breadth of CUDA-accelerated applications, and status as 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 H-100 has risen 20% year to date, while H100 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 two 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-based to GPU-based accelerated 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 one-shot inference to reasoning and now to agentic AI.

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 stack, including energy-efficient 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 Codex since the launch of GPT-5.5.

With analysts now forecasting hyperscale CapEx to exceed $1 trillion in 2027 and agentic AI beginning to proliferate across all industries, AI infrastructure spending is on track to reach $3 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 the opening eyes launch of GPT-5.5, co-designed for, trained with, and served on Blackwell, currently positioned at the top of artificial intelligence leaderboard rankings.

Microsoft's Fairwater, the world's most powerful AI data center, is now live ahead of schedule, powered by hundreds of thousands of Blackwell GPUs. Starting this year, AWS will add more than 1,000,000 Blackwell and Rubin GPUs and is collaborating on Spectrum networking with 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, SpaceX AI, and more. Now, with the addition of Anthropic to OpenAI, Gemini, SpaceX, XAI, Meta, MSL, Microsoft, AITML, 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 co-design 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 a broad set of models and deployment scenarios.

Full-stack innovations drove a 2.7x increase in throughput and a 60% reduction in the cost per token on GB300 compared to just six months ago. NVIDIA Compute is not just the highest-performance AI infrastructure—it is the most economical 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 of an AI factory producing intelligence: tokens 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 represent 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 co-designed 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 x86-based alternatives.

Vera CPU 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 get it deployed. We have visibility into nearly $20 billion in total CPU revenue this year, setting us up to become the world’s 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 Vera Rubin in the second-half of this year starting in Q3. By integrating 7 purpose-built chips across 5 accelerated racks, Vera Rubin 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 A5X bare metal instances, which can support up to 960,000 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 was 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 four 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 and prepayments, 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 focus on 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 margin was 75%, largely flat sequentially as 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-50s in Q2.

We generated record free cash flow of $49 billion, up from $35 billion in Q4. I'd 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.

Our return program is another key component of our capital allocation strategy. Given 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 $0.25 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 ± 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 the $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% and 75%, respectively, ±50 basis points for the full year. We are still expecting to be in the mid-70s. GAAP 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 growth to be somewhere in the upper 40s 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% and 18%, excluding any discrete items or material changes to our tax environment. This is lower than our prior expectation of 17% to 19% due to changes in geographic mix. That puts me at the end of this part. And I'm going to now turn this over to the Q&A with Toshiya.

Toshiya Hari

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

Operator

Thank you. At this time, I would like to remind everyone that in order to ask a question, press * then the number one on your telephone keypad. We'll pause for just a moment to compile the Q&A roster. As a reminder, please limit yourself to one question. Thank you. Your first question comes from Joseph Moore with Morgan Stanley. Your line is open.

Joseph Moore

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

Jensen Huang

Yeah, thanks, Joe. First of all, I meant to say we're increasing our quarterly dividends from 1 cent to $0.25. I think that extra $0.05 would mean a lot to the large shareholders. So anyhow, let's see—Joe, regarding segmentation and the description of the business: we wanted you to understand our business better. AI is very diverse, and computing is diverse. They're diverse in several ways.

The first thing, of course, is that 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's in the energy sector or, of course, science labs, higher education, and so on and so forth. So AI is diverse.

The second thing is that the applications are diverse. It could be in enterprise, it could be in the energy sector, manufacturing sector, and such where it runs. It is diverse. It could be in the hyperscale cloud. It could be AI natives—there’s a whole network of AI natives cropping up around the world—enterprises on-prem, industrial settings in factories and plants, all the way to supercomputing centers and at the edge.

Edge—including, of course, what most people associate with self-driving cars and robotics—but also a large and growing network of computers inside manufacturing plants, whether it's a chip fabrication plant, packaging facility, or computer assembly plant; all kinds of different manufacturing environments. And then, of course, in the future, every single base station and every single radio network will become an AI-powered radio network. And so, where it runs—and lastly, how it's governed.

It could be operated by a public cloud, but it could also be subject to standard industrial regulatory requirements that prevent it from running in a public cloud. This might be due to confidential computing needs or national security concerns. Different data centers therefore have to be built differently. NVIDIA is quite unique in that we are the only company that builds all of the technology components.

We build it through extreme co-design, in a completely end-to-end and full-stack manner. But then, of course, we open up the platform so it can be integrated into all different environments. However, some environments—such as enterprise settings—require a vendor that provides all the technologies working together seamlessly, so customers don’t have to build it themselves; they prefer to buy it and operate it.

Thus, across many different segments of the data center market, NVIDIA’s fully integrated, full-stack—yet open—approach to producing and delivering products is truly, truly important. If you look at our various segments, the way we’ve broken them down into three major categories reflects an attempt to distill everything I just described into its simplest form.

The first would be hyperscale clouds—that’s one major segment. Within this segment, we operate in three distinct ways. First, we help hyperscale cloud providers accelerate their data processing and machine learning workloads. We accelerate and support their internal AI processing. We also, of course, bring substantial business—including video ecosystem workloads—to their public clouds. That’s one segment.

The second segment consists of AI-native enterprises operating on-premises, industrial on-prem deployments, and sovereign AI initiatives. This segment is growing incredibly fast because everyone needs AI, and we’re going to see AI adoption across every industry, every country, and every company. Everyone wants to implement it in their own way, and the fact that we provide a complete solution makes it significantly easier—and in many cases, even possible—for organizations to build these systems.

And then, of course, there’s the robotic edge. Yesterday’s computing was largely about personal computing. The future will be about personal AI. One example of personal AI is the self-driving car—a vehicle that is essentially a robotic system serving as your personal AI. And of course, there will be all kinds of other robotic systems, including even base station radio networks. As I mentioned earlier, those too will essentially become robotic systems.

That’s why we’ve structured our business this way—it’s the simplest framework for understanding what we do. Each segment, in many respects, has its own stack. They run on different operating systems and function in distinct ways. Our go-to-market approach differs significantly across each. The easiest go-to-market is obviously with hyperscalers, since there are only five or six of them. But the rest of the industry comprises roughly 250,000 companies worldwide, making go-to-market strategies highly complex and diverse.

Your understanding of AI must therefore be extremely diverse. As you know, NVIDIA offers the world’s largest suite of acceleration libraries—from computational lithography and fluid dynamics to particle physics and molecular dynamics, with many more domains in between. All these libraries are essential for us to effectively engage with vertical industries that fall under the second and third categories.

In any case, the key point is that our business has now evolved and scaled to such an extent that segmenting it helps provide a clearer understanding of how our business operates.

Operator

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

Ben Reitzes

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, excluding China, grew about 120% in the quarter, and you're guiding to roughly 100%. CapEx at the hyperscalers is forecast by many, including myself, to grow 90% to 100% this year. And you've talked about the data center market still being on track to reach $3 to $4 trillion by the end of the decade. I was just wondering—do you still feel comfortable endorsing the view that the company should grow faster than hyperscaler CapEx? And do you still see hyperscaler CapEx continuing to grow at a very rapid pace beyond this year? Thanks a lot.

Jensen Huang

Yeah, thanks Ben. So first of all, we should be growing faster than hyperscale CapEx, and the reason for that is illustrated by the segmentation I just described. Our data center business has two large parts. It actually has more parts than that, but we’ve combined them into two large segments for simplicity. It’s much more complex than just two large parts, but I grouped them into two to make it at least somewhat easier to understand.

So if you look at the first part, it’s hyperscalers—that’s the hyperscale CapEx you were just referring to—and it’s at $1 trillion this year. I fully expect it to keep growing from here for fundamentally sound reasons. This is how computing will work in the future. And if they don’t compute, they won’t generate revenue. It’s very clear: compute equals revenue, and compute equals profit.

The world is changing. Software didn’t used to rely on SaaS, nor did it consume nearly as much compute—but AI requires an enormous amount of compute. Yet, with that compute, you can accomplish vastly more, which is why we’re hearing about the AI frontier and AI companies like Anthropic and OpenAI growing at an incredible pace. The fact that they can achieve in one month what took some SaaS companies a decade to accomplish tells you something important.

So the first category is hyperscalers, with CapEx at $1 trillion and heading toward $3 to $4 trillion. The second category consists of all the AI-native clouds—they’re regional, scattered everywhere, and include startups around the world supporting those efforts. There are also roughly 250,000 enterprise companies globally, many of which will either need to or want to build their own AI factories to operate. This includes many industrial companies.

There’s simply no choice but to place computing where the context and action are. You can’t put that in the cloud—it must respond reliably and quickly every single time. Just imagine a chip fabrication plant connected to a public cloud service provider; that wouldn’t make any sense. So the second category includes sovereign AI clouds, and there’s an entire class of data centers that semi-custom chips just don’t serve—because these data centers want to buy complete systems and operate them directly, not design or build the infrastructure themselves.

And so the second category is extremely diverse. Instead of 5 or 6 seven companies representing the revenues associated with our first category, the second category is hundreds, thousands of companies and in the future be hundreds of thousands of companies with a large number of companies with smaller installations. And that category is going to continue to grow at incredible pace.

This second category, when I talk about physical AI and I talk about how the rest of the $100 trillion industry that has not been affected by impacted by IT in the last 30 years, it's about to be impacted by AI. That is the segment that I'm talking about. The second cluster is growing incredibly fast. Our share of that, of course, is very, very large. We're fairly unique in our abilities to be able to serve this industry.

Our platform is built like it's vertically integrated so that everything works. But when then we disassemble it so that people can build and buy it in the configuration they want and assemble it the way they like. And so this second category is fairly poorly understood because they're just so many small companies or so many companies in each one of the installations are relatively small compared to of course one of the hyperscalers.

And so if you look at the segmentation and the size of each, you could see that in fact we're growing share in the hyperscalers because we now have much bigger support from Anthropic, a new partner of ours and we're 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.

Operator

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

CJ Muse

Good afternoon, Thank you for taking the question. You have 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.

Jensen Huang

Well we are growing share and inference and we're growing share and inference very, very quickly. And the reason for that is this year the number of frontier model companies grew. And so there's cursor and perplexity and there's a new model companies TML and Reflection and the list goes on. And so the number of Frontier model companies has grown and we added Anthropic to our partnership this year. They're expanding incredibly fast.

We've partnered with them to secure computing capacity across Azure, AWS Core, Weave—I forget who else we've already announced—but there's a whole list of others that we are bringing online for them. And so the amount of capacity that we're going to bring online for Anthropic this year and next year is going to be quite significant, very significant. And so we're growing, and our coverage of Anthropic has been largely zero until just recently. And so we're gaining share tremendously fast in inference.

Vera Rubin's going to be even more successful than Grace Blackwell at this point. Every single—I can't think of one—every single Frontier model company will jump on Vera Rubin from the get-go. And that wasn't true before with Blackwell. And so Vera Rubin is off to a tremendous start and it'll surely be more successful than even Grace Blackwell. So I think the answer, CJ, is that we're gaining share in inference.

Let me go back again to the question that Ben was asking. Remember, so far everything that I've just explained regarding inference is really focused on hyperscale. Remember, there's a whole second category of AI data centers that we serve almost uniquely. This segment is very fragmented, requires a fairly integrated—really well-integrated—platform solution and a very large go-to-market effort.

And that segment—all of the inference, 100% of that—the vast majority of it is NVIDIA, and then of course physical AI. NVIDIA is practically the only company serving physical AI today, and we've been working on physical AI for a long time. And so that is also growing. So our share of inference is growing very quickly.

Operator

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

Timothy Arcuri

Thanks a lot, Jensen. I wanted to ask about the traction you're getting with some of these custom merchant solutions you're doing, things like CPX and LPX. And I just wanted to ask and see—you've talked before about that segment representing, I think, 20% of the market. So I would imagine you're getting pretty good traction with LPX. Could you just talk about that and maybe also how it fits into your broader platform strategy? Thanks.

Jensen Huang

The LPX is designed for low latency and high token rate, but its throughput is low. Its throughput is low, its model size capacity is low, and its context processing—its ability to absorb a lot of context, for example, for software coding or agentic workloads—is lower. And so the challenge is simply this—and I've explained before—that the use case for LPX is not broad.

It's 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. And so that remains exactly consistent with what I've said before. And I still expect that. And so I expect that LPX and other SRAM based decode, decode focus tokens, high token rate generate generated focused accelerators that will always be, it 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, data processing to pre training 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 insert certain circumstances so long as the customer that the provider already has a high token rate service that they can offer, then we can tack on. And LPX and they could deliver that service even better.

And so that's how I see the market and I think whether it's 20% or 10% just depends on where we are in the development of AI. I think today is a lot less than 20%. Someday these premium tokens could be 20%. And I'm you know, we're ready to work with the service providers to enable this capability. I'm excited about it.

Operator

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

Vivek Arya

Thanks for taking my question. Jensen. There's a lot of excitement around CPU for agentic applications and just a lot of noise around the number of CPU's actually exceeding the number of GPU's. And I was 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 CPUs or is that kind of already included in that Vera as part of Vera Rubin. So just if you could 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 kind of put that in context with what you sell, right, which is usually the CPU as part of the GPU. Thank you.

Jensen Huang

The 20 billion is for a stand alone CPU. And remember, we have Vera is used in three ways as a stand alone C4 ways as a let me just start with the one that you already know. The first way is Vera Rubin and we'll sell millions of Rubens and every two of them is connected to a Vera and of course we priced those too and they're properly priced. And so that's number one use case.

The second use case is Vera standalone CPU. The third is Vera with CX9 and it's in the software stack for storage and then Vera in a with CX9 with a software stack for security and compute isolation and confidential computing. So each one of those use cases is built on Vera and my sense is that will be supply constrained throughout the entire life of Vera Rubin. There are four different use cases of it and but 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 work, and the harness could be OpenClaw. It could be Hermes code—clock code, clock code—is essentially a harness around Clyde, around the Opus model. OpenAI codecs are a harness around the GPT-5.5 model. And so these are harnesses, and these harnesses provide capabilities like I/O orchestration, memory management, and tool use connected to tools—for example, browsers, C compilers, Python compilers, and so on.

The harness runs on the CPU, and tool use runs on CPUs, you know. So, for example, if the AI were to perform a search or use a browser, that would run on the CPU. The world has a billion human users. My sense is that the world is going to have billions of agents—not today; I mean we’re going to grow into it—but eventually, there will be billions of agents, and all of those agents will use tools.

And those tools will be kind of like PCs—just as we humans use PCs today, in the future you’ll have an agent using a PC. So if you think along those lines, currently you might pick your favorite number of agents—say, a few hundred thousand—but in the future, eventually, it could be a few billion. I can imagine all of them effectively having and using their own PCs.

And so, every one of those agents is going to spin off sub-agents. And every time they do that, you’re going to need to perform inference—that’s where the thinking happens. All of the thinking happens on GPUs. All of the orchestration essentially runs on CPUs, and when sub-agents are spun off and are thinking, they use GPUs. Whenever agents use simulators, those can run on either CPUs or GPUs.

That’s why we’re working so closely with Cadence and Synopsys—to accelerate all of the world’s tools. We’re accelerating all of the world’s tools, data processing engines, and database engines because agents use these tools and have lower patience tolerance than humans—they want things to happen quickly. So we’re accelerating all the world’s tools so they run on CUDA.

And you can see us doing that—you know, when I work with Cadence, Synopsys, Siemens, and companies like Adobe. That’s because we’re trying to get all of the world’s tools to run on GPUs, since they already have GPUs and it’s much faster. So we’re going to need a lot more CPUs, and Vera was designed to be an agentic CPU.

CPUs of the past were designed with many cores so they could be easily rentable—people rent cores. But agents don’t rent cores; they just want the work done fast. The economics of the past was dollars per core—that was the economics of cloud computing. The economics of AI in the future is tokens per dollar or dollars per token. So what we need to do in the future is generate and process tokens as fast as possible—and that’s exactly what Vera does incredibly well.

So we’re expecting to be very successful with Vera. But ultimately, what we’re doing is building infrastructure for AI—and it needs incredibly great storage, which is why we built STX. It needs incredibly good networking, which is why we have Spectrum-X. It obviously needs incredibly powerful GPUs and inference scalability, which is why we developed NVLink 72. It also requires incredibly strong security and confidential computing, which is why Vera includes Rubens—the world’s first platform with end-to-end confidential computing—and, of course, it needs great CPUs. We’ve got it all covered.

Operator

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

Stacy Raskin

Hi, guys. Thanks for taking my question. I wanted to go back to the segmentation. So, first of all, I'm just curious—where do you place the Neo clouds across those two segments? Are they in hyperscale or in the AI cloud? Part of me assumes the latter, but I'm not so sure. And then, just considering their magnitude—I mean, they're both about the same size—it almost sounded to me like you were suggesting that the AI cloud might grow faster going forward than hyperscale. Is that what you're trying to say, or do you see both segments growing at a similar pace?

Jensen Huang

First of all, you're correct that AI-native clouds don't build chips, don't design their own chips, and they don't want to. They really can't assemble unrelated parts into an AI factory. Their patience—or rather, their tolerance—for time-to-first-token is extremely low, and their need for an architecture with broad model compatibility, capable of running every model and serving customers from everywhere, is incredibly high.

That’s precisely why Nvidia’s architecture is so perfect for them. We offer every component, and whatever we don’t provide, our ecosystem of partners does—and it’s all fully integrated and works seamlessly together. The number of customers who can rent this infrastructure from an AI-native provider is incredibly high: essentially every AI builder, every AI-native startup globally, SaaS companies, enterprise companies, and industrial firms.

So our computing architecture is the most rentable computing platform in the world. It’s the most performant, the easiest to deploy, the most rentable, offers the best total cost of ownership (TCO), and is also the easiest to finance. All these attributes uniquely meet the needs of AI-native companies. This falls into the second category. Surprisingly, they’re quite similar to OEMs, large enterprises, and so on.

So we classify that in the second category. If you look at that segment, its growth began only after the AI ecosystem matured within hyperscalers. Hyperscalers developed AI first for many reasons—they have strong computer science expertise, excellent data center capabilities, and they primarily focus on consumer applications, where imperfection isn’t catastrophic as long as it enhances the service.

For many other applications—industrial and enterprise use cases—AI won’t be widely adopted until it becomes highly capable, reliably productive, safe, and able to generate tangible impact and income. As a result, you’d expect the second category to develop more slowly than hyperscale, which is evident in the numbers.

However, in the long term, if you consider industrial and enterprise sectors, that’s clearly where future economic value will emerge—it represents roughly $50–80 trillion of the global economy. And with AI, it will become even larger. So I expect the second category to eventually surpass hyperscale. Over the next several years, I think it’s a foregone conclusion: both segments will grow incredibly fast, but I still anticipate the second category growing faster.

And I’m hoping that within the next five years, the physical AI and robotics segment will also grow incredibly fast.

Operator

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

Jim Schneider

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

Jensen Huang

In terms of what’s incremental above the trillion, I would say, first, the continued growing share in frontier AI models—I’m expecting to gain more share there, so I expect that to grow. Second, we didn’t include any standalone CPUs—various CPUs—in that number, so I expect that to be the second-largest contributor. The TAM is, of course, quite large in agents and agentic systems, and all of our customers are very excited about Vera, and we’re going to sell a whole bunch of Veras.

And then third would be LPX, because, as I explained earlier, LPX is designed with an SRAM architecture, which gives it the benefit of very low latency and very, very high interactivity. However, its throughput and context-processing capability are also quite limited—that’s just the nature of SRAM-based systems. But through the combination of Vera, Vera Rubin, and LPX, we’ll be able to address the entire spectrum of AI—from pre-training to post-training to inference and agentic systems.

Operator

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

Joshua Buchhalter

Hey guys, thanks for taking my question and congrats on the great results, Colette. I believe in your prepared remarks. You mentioned that GB 300 is the fastest ramp in the company's history. How should we think about Vera Rubin against this benchmark? It's obviously a new architecture at the silicon level, but it uses a similar rack. Does that mean we should expect a similar slope to the Vera Rubin ramp as the GB 300, or should it be a bit more gradual given the new silicon? Thank you.

Colette Kress

Yeah, we’ve indicated for a while that we will be launching Vera Rubin in the second half—we’ll start in Q3. That will be our initial shipments. And then once we get to Q4, we’re probably going to start seeing our ramp continue. It’s hard to say at this point which will have a faster ramp. But again, we already have demand planned. We’ve got purchase orders, and we’ve 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’s just about the timing it’s going to take for us to get that into the market—nothing else other than getting production up and running across all the different systems we have ready for order. So it’s still a bit early to say, but yes, we’re going to start in Q3 and continue to ramp into Q4 and Q1 of next year—certainly, that’s going to be very big as well.

Operator

There are no further questions at this time. Toshiya Hari, I turn the call back over to you.

Toshiya Hari

Thank you. Before I hand it over to Jensen, please note that Jensen will be giving the keynote at GTC Taipei at Computex on June 1st. We will also be participating in 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 second quarter of fiscal 2027 is scheduled for August 26th. With that, here's Jensen to close this out.

Jensen 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 five 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, MSL, 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 enable us to uniquely address new AI data center segments, new AI cloud natives, new AI-native clouds, 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 five 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 the NVIDIA Compute Platform over three decades—one architecture, a vast ecosystem, and extreme co-design 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.

Operator

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

Details at NVIDIA IR

Tips: This summary was generated by an AI language model based on publicly available information, including auto-generated subtitles and publicly released audio from third-party sources. While efforts have been made to ensure accuracy, AI-generated content may contain errors or omissions. Please refer to the official materials released by the respective listed companies for the most accurate and up-to-date information. This content is for informational purposes only and does not constitute investment advice or a recommendation to buy or sell any investment products. The views and summaries presented do not reflect the official position of moomoo. Moomoo makes no warranties, express or implied, regarding the accuracy, timeliness, or completeness of this content and shall not be held liable for any losses arising from its use. The copyright of any original conference call content remains with the respective listed companies.

161

158

1.2M Views