NVIDIA (NVDA) Q2 2027 earnings call transcript

Thank you. Good afternoon and welcome to NVIDIA's conference call for the second quarter of fiscal 2027. With me today from NVIDIA, our Jensen Wong, President and Chief Executive Officer, and Collette 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 third 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 on 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, August 26th, 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-GAP financial measures. You can find a reconciliation of these non-GAP financial measures to GAP financial measures in our CFO commentary, which is posted on our website.

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

Thanks, Tothia. We delivered another outstanding quarter with record revenue, operating income, and EPS. Total revenue of 96 billion, more than doubled year over year as growth accelerated for the fourth consecutive quarter. The surge in AI demand is driving a global infrastructure build out, supported by an expanding and diverse set of growth opportunities, spanning hyperscalers, AI labs, AI natives, enterprises, and sovereign customers. We expect to grow revenue by approximately 70% in fiscal 2028.

This is a supply constrained outlook. Q2 data center revenue increased 18% quarter over quarter to 89 billion with strong contributions from both sub-segments, hyperscale, and ACINE, which includes our neo-cloud, industrial, and enterprise customers. Hyperscale revenue of 49 billion grew 13% sequentially, driven by sustained strength in Blackwell.

Reinforcing that more compute drives more revenue as new GPU capacity comes online, our hyperscale customers delivered strong financial results in the quarter with accelerating revenue growth and expanding margins. With cloud industry backlog now greater than 2 trillion, HAPEX by the top five hyperscalers is expected to reach nearly 800 billion in 2026 and 1.3 trillion in 2027. Today we are delighted to announce an expansion of our partnership with AWS.

Building on is already vast installed based of NVIDIA Compute. AWS is deploying an additional 2 million GPUs starting this quarter through the second quarter of fiscal 29, along with Vira CPUs, some integrated with Ruben, other standalone. AWS will serve NVIDIA NEMATROM family of open models on Amazon Bedrock and SageMaker. Amazon will also adopt our full physical AI stack, Omniverse Cosmos, Isaac, and Judson to power its fleet of warehouse robots.

ACI and E revenue of 40 billion increased 25% sequentially and 138% year over year. Growth was driven by neo-cloud capacity additions to meet the rising demand from enterprises, AI startups, and sovereigns, as well as hyperscalers purchasing capacity to supplement their own buildouts. Using NVIDIA DSX reference designs, our neo-cloud partners are bringing capacity online faster and at lower token cost.

They are expected to exit the year with 8 gigawatts in total installed capacity, up from approximately 3 gigawatts at the end of 2025. Incredibly, we are seeing demand acceleration even at our scale. Customers forecast point to our growth doubling next year. However, as I mentioned earlier, we expect to grow approximately 70% as we are supply constrained. NVIDIA Compute is fully utilized across every cloud we serve. The economic value it generates for our hyperscale, neo-cloud, and AI lab partners keeps rising.

Besides building the best AI computing technologies and the most capable supply chain, NVIDIA has three unique capabilities that are engines powering our growth. First, NVIDIA's architecture runs every model and we're growing share as closed and open model adoption grow. Closed and open models alike, adoption is skyrocketing. NVIDIA runs the leading closed models, opening AI, anthropic, grok, meta, Gemini, and the leading open models, TML, Mistral, Quinn, Kimi, GLM, DeepSeek, MiniMax, and Nematron, where great at small models and giant ones, large or video, auto, regressive or diffusion in the cloud or in the edge.

NVIDIA is great at training, great at inference, great at agentic workloads, one platform, fungible for every model and workload, durable for the entire lifecycle of AI. That combination of performance, fungibility and durability is what makes NVIDIA the productive and financeable compute infrastructure.

Our second unique capability is our full stack AI factory platform that is expanding our share of the data center town. Since Hopper, our revenue opportunity has grown from roughly 18 billion per gigawatt to 25 billion with Blackwell, to 40 billion with Vera Rubin, which now spans Vera CPU, Rubin GPU, NVLink, Infiniban or Ethernet, and grok LPU, announced earlier this week.

Our ability to extreme co-design across GPU, CPU, NVLink scale up networking, scale out networking systems, algorithms and software enables us to deliver x-factor performance game every generation. Vera Rubin exemplifies this, delivering 30x higher throughput per megawatt and 35x lower token cost relative to grace Blackwell Ultra.

We commenced production shipments of Vera Rubin earlier this month, having already received purchase orders from every major hyperscalar, AI cloud and system OEM. We expect Vera Rubin to mark the fastest product ramp in NVIDIA's history.

Our networking business had another record quarter with revenue growing 18% on a sequential basis. Spectrum X Ethernet, which grew 2.6x on a year over year basis, is already helping us become the largest and fastest growing network company in the world.

Rising adoption of agentic AI is driving an acceleration in demand for data center CPUs. Our great CPU, introduced in 2021, has been a great success, with revenue on a trillion 12 month basis, exceeding 5 billion. Today, we are in full production of our next generation Vera CPU.

As a standalone product, Vera expands our TAM even further. Vera completes a genic task 1.8x faster on the spec benchmark and provides five times the bandwidth per watt than any other data center CPU. We expect Vera to be deployed by every major hyperscalar, Neo cloud, AI lab and system OEM with shipments already underway to our lead partners, including OCI, SpaceX AI and starting this quarter, AWS.

We continue to see demand for approximately 20 billion in total server CPUs and based on our customer demand and improving supply outlook, our preliminary expectation is for CPU revenue to more than double in fiscal 28, positioning us as one of the world's leading server CPU suppliers.

Since the announcement of our GROC partnership last year, we've been working to unite NVIDIA's high throughput and rocks high interactivity architectures at hot chips earlier this week. We announced that GROC 3 LPX, our first rack scale LPU system is in full production and already setting records demonstrating nearly 4x the number of tokens per second against the next best alternative on our artificial analysis benchmark. We expect to ship GROC 3 LPX in volume later this quarter to early adopters. Nebias will be the first.

Today, we're not just selling the best chips. We're selling a full stack AI factory platform offering superior economics for customers and capturing a bigger share of the data center term. Our third unique capability is the combination of our full stack AI factory and rich CUDA ecosystem, allowing us to extend AI into markets. A single chip alone can never reach.

Beyond the hyperscalers lies a massive market anxious to adopt AI, customers with no interest in designing their own custom silicon, NVIDIA's fully proven full stack platform is uniquely suited to help sovereigns, neo-clouds and enterprises build their AI infrastructure, bring it to full operation, continuously optimize it through CUDA software and connect it to off take demand from our vast developer ecosystem.

Hyperscalers will remain a major growth driver. But non hyperscaler growth are AIC and E segment spanning sovereign regional neo-clouds enterprise edge and air gap data centers will represent roughly half of our data center business.

Our AI native startup ecosystem, developed and running primarily on the NVIDIA compute platform is scaling at a rapid pace. Global VC funding in AI roughly 70% of which is spent on compute exceeded 400 billion in the first half of 2026 surpassing the 265 billion raised in all of 2025. Nearly 20 companies, including cursor owned by SpaceX, and other companies in Q4 of last year, with vertical enterprise software logging the fastest growth.

In enterprise on a trailing 12 month basis on Prem revenue in the automotive vertical reached 8 billion while financial services manufacturing and healthcare combined contributed 7 billion in revenue. Hudson River trading and Jane Street are leveraging NVIDIA's power AI factories to accelerate quantitative trading.

Samsung electronics is using NVIDIA to achieve up to 20 x greater performance in computational lithography, while Bristol Myers squid is investing in Vera, Ruben AI factory, a fast follow to the Roche and Lily build outs as drug R&D timelines compress from years to months.

In sovereign AI, our business primarily through the regional neo clouds grew 35% sequentially and more than tripled year over year in Q2. A country or region can allocate land and power directly to a regional cloud partner in ways it never would to a foreign hyperscaler. We don't own a cloud ourselves. We are neutral partner to every sovereign and neo cloud. And because NVIDIA compute is productive, fungible, rentable, and durable, regional cloud interest is surging around the world.

We helped Quarwe, Nevius, and end scale build entire infrastructure businesses and neo clouds are emerging everywhere. Firebird in Armenia, cassava technologies across Africa, DMI cloud in Taiwan, Yoda and NASA in India, Fermis in Australia, YTL AI cloud in Malaysia, pairing local land power and operating expertise with our platform.

Last month, we announced a partnership with Noatra, Japan's national AI company to build an NVIDIA DSX AI factory that will create open models to power AI agents, digital twins, robotics, and physical AI applications.

South Korea's LG and Hyundai Motor Group are partnering with NVIDIA to build and scale AI. And in Europe, a record 35 new NVIDIA powered AI supercomputers were unveiled to advance industry and scientific breakthroughs. Neo clouds are seen strong demand pipelines for many diverse off-takers, rather than allocating their entire capacity to a single long-term off-take guarantee that lenders typically require to finance a data center independently.

We have introduced a revenue sharing structure. NVIDIA provides a take or pay commitment on a portion of the facility's capacity, a minimum revenue guarantee that gives lenders the confidence to underwrite the project and in exchange we share an portion of the Neo cloud's revenue earned above that floor.

Independent capital still underwrites every deal on its own merits. We're not making loans. In this model, we get paid twice once on the hardware sale, and again through the share of rental revenue, a highly reoccurring stream layered on top of a one-time equipment purchase. Over time, this model can expand our addressable market and create reoccurring usage-linked revenue stream alongside our core platform revenue, with the potential to drive billions in revenue over the medium to long-term.

Together, NVIDIA's three unique capabilities, a platform that runs every model, a full stack AI factory platform, capturing more of the data center TAM, and a CUDA ecosystem that extends AI into markets no single chip could reach alone, reinforce one another and are the engines of our growth.

Let me update you on our progress with our Frontier AI Labs. The Frontier AI Labs have extraordinary demand for training and inference compute, but they are growing faster than what their balance sheets and credit profiles can support. They have rapidly growing customer demand, yet still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently.

In other words, their growth isn't limited by their technology or customer demand, it's limited by compute. For these companies, more compute means more more intelligence, more users, and more revenue. NVIDIA is needed to help power this flywheel.

First, we've invested nearly 50 billion in the Frontier AI Labs. This was a meaningful commitment, but it represented a small fraction of our expected free cash flow over the same period. Further, to support the Frontier Labs infrastructure build-outs, we recently announced partnerships with six of the world's leading infrastructure capital providers, Apollo, BlackRock, Blackstone, Brookfield, Golden Sax and KKR, to establish financing platforms that will raise over 500 billion of third-party capital.

With these partnerships, building on our unique, fungible, and durable computing platform, the AI Labs will be able to build and assess AI infrastructure funded by long-term institutional capital at relatively attractive rates. Last week, we announced that we secured land power, shell capacity through our partnership with SoftBank Energy to exclusively host NVIDIA Compute at their Portsmouth Calypus. The initial deployment expected to support 4.25 gigawatts of AI factory capacity will be utilized by OpenAI. Each generation of NVIDIA AI factory systems deployed at Portsmouth could represent approximately 1.5 million NVIDIA GPUs, and over 20 years, the site could support multiple upgrade cycles.

Here's the essential economic point. The LPS commitment secures a long-lived AI factory site while the NVIDIA Compute within the data center can be upgraded repeatedly. This project deepens our long-standing partnership with OpenAI. OpenAI has committed to substantially deployments of NVIDIA AI infrastructure through 2030. OpenAI's existing and planned commitments represent approximately 12 gigawatts of NVIDIA Compute. For another Frontier AI Lab, we will provide selective credit enhancement for nearly 2 gigawatts of compute.

This complements the substantial NVIDIA Compute capacity they've secured independently without NVIDIA's credit support. We recognize the scale of this support, and we know some will call this circular financing. We see it differently. We're going through a major computing platform. The creation of one of the most important technologies in human history, and these are once in a generation companies.

The technology leadership is proven, and their customer traction and usage are skyrocketing. We expect them to become the largest technology companies in history. We believe these investments measured against the strength of their demand. The business they create for us, the ecosystem they build on NVIDIA's platform, and the equity returns on our invested capital will be excellent. And our risk is limited.

The NVIDIA Compute platform is fungible and durable and can be redeployed to support other customers. For context, we expect demand from the AI Labs for which we expect to leverage our balance sheet to contribute toward roughly a quarter of our business next year. This remains compute we ship will be consumed by investment grade customers or those that are backed by one.

In accordance with the US government licenses. Current hopper shipments are deluded to corporate gross margins. And given ongoing geopolitical uncertainty, there is no China data center compute revenue in our forward outlook.

Moving to the rest of the P&L gap and non gap gross margins were both 75% largely unchanged from last quarter due to a similar product mix. Gap and non gap operating expenses were up 10% and 11% sequentially, primarily due to high compute infrastructure costs and compensation and benefits costs.

Our non gap effective tax rate of 16% increased from a year ago, primarily due to higher revenue. On our balance sheet, inventory increased to 32 billion as we prepare for the Vera Rubin launch. Days of sales outstanding increased to 60 days reflecting extended payment terms for large purchases by certain investment grade customers to be shipped over multiple quarters.

In Q two, we returned a record 26 billion to shareholders, 20 billion through share repurchases and 6 billion through our quarterly dividend of 25 cents per share. Relative to our plan to return 50% or more a free cash flow, we've returned 60% on a year to date basis and going forward. We intend to increase and return excess free cash flow, not a strategic uses.

Let me turn to the outlook for the third quarter. Total revenue is expected to be 108 billion plus and minus two percent. We expect sequential growth to be driven primarily by ACINE with data center. Well, growth and hyper seal is expected to re accelerate in Q four and into fiscal year 28 as supply of Vera Rubin grows over time.

We see Vera Rubin accounting for about 20% of data center revenue in Q three. Looking ahead, our preliminary expectation is for fiscal year 28 revenue to grow approximately 70% year over year. Although we will work to close the supply demand gap, we expect supply to remain a bottleneck at least through the end of fiscal year 28.

Many of you have expressed concerns regarding our gross margins as component costs have risen significantly. As you are already aware, we are experiencing extreme pricing conditions in memory. The magnitude of the price increase has exceeded our prior expectations and are headed even higher into next year. As a result, we are resetting expectations today. For Q three, we expect gap and non gap gross margins to be 74% plus or minus 50 basis points. We expect margins to bottom in Q four in the 71 to 72% range before settling at 72 to 73% in fiscal year 28 as executed price increases take effect in Q one.

We want to be direct about this rather than let it linger as an open question. Memory scarcity today is being driven in large part by the AI build out itself and unlike a component that simply raises our costs with no offset benefit. Tighter memory supply is a symptom of the same demand surge that's driving our own growth.

We have a long standing deep relationships with all three major memory suppliers and we're working closely with them to further increase the capacity. Our roadmap requires gap and non gap operating expenses are expected to be approximately 9.2 billion and 9.0 billion respectively. For the full year, we now expect up X to grow in the low 50s driven by a broadening of our product portfolio and further increase in the usage of AI tools, which is already and will continue to enhance engineering productivity.

For full year, fiscal year 27, we continue to expect gap and non gap taxes to be between 16 and 18%, excluding any discrete items and material changes to our tax environment.

Yeah, thanks Joe. As you probably are aware, AI has become useful and the the agents that are being adopted everywhere use an enormous amount of compute. First of all, the large language models are larger than ever because they're smarter than ever and and these agents go through reasoning and planning multiple, multiple turns of tool use. The amount of compute necessary for an agent versus a human using it is probably 15 to 100 times depending on the type of problem you're trying to solve.

And so the amount of compute necessary is just extraordinary. That's that's a factor that almost everybody sees the part that people don't see. About our growth because we're practically singular because the nature of how we deliver products. I mean, we're the only company in the world that creates and builds offers an entire factory platform, a full stack system.

And you know, customers can still mix and match. However, most most companies just don't have the skills to do that or desire to do that. And so there's an entire part of the market that we experience growth. There's sovereign AI, the original AIs, their new clouds, their AI startups, their enterprises, where we're seeing, which represents by half of our business and that's growing 100% a year.

That part of the world's computing is likely to be larger over time than even what we're currently experiencing in the cloud. And so, so I think the demand that we see is driven by all of those factors. It is also the case that you can no longer procure technology per se and stand up these infrastructure. You've got to go secure the land power and shell, which, you know, oftentimes is a couple, two, three years out.

All of the rest of the supply chain necessary to align the construction, the power, the cooling. You know, all of the labor that's necessary. And what AI infrastructure is creating so many jobs all over the United States and all around the world. It just takes a lot more planning. And so we're involved in in securing infrastructure now further down the pipeline. You know, just as a long time ago, people asked me, why is it that we're working with memory suppliers when we're a chip company. And today people understand it's really quite genius that we were working on our supply chain so far upstream.

We work with power generator companies, we downstream, we work with land power and shell companies all around the world. And that helps prepare all of this computing that's going to be built that will ultimately deploy for our ecosystem and our customers. And so we just have a lot greater visibility now upstream and downstream. It is the case that we've never forecasted or never guided to a year in advance. And, and even though our demand is much greater than 70%, our supply allows us to confidently deliver 70%. And we're going to continue to work with our supply chain to increase on that.

But what we wanted to do is to be consistent with everybody from our customers, our shareholders, our supply chain. Everybody sees the same view. And the reason why that's important is because, you know, everybody's putting a lot of resources at play. And so we wanted to make sure that everybody has the same set of information.

And we've got a huge year coming up next year. And it's going to be pretty extraordinary.

Yeah, thanks, CJ. The AI lifecycle is getting way more complex than it used to be. And it's playing into NVIDIA's architecture much more greatly than it used to be. And so you could kind of see it as four phases. You know, there's the first phase, which is preparing all of the data that you need. Some of it is synthetic. Some of it is real.

Some of it is human labor and human-labeled and generated. Pre-trained the models. And then there's post-training, the third phase. And then there's the agentic inference. And agentic inference is extremely complicated. And so every one of those phases are complicated. The thing that's really great about the NVIDIA architecture, and we created this with NVLink 72. And it was a big surprise on the world when we first created the world's first rack scale architecture. It was hardly easy.

And it was very challenging building the first generation. We're now in our third generation of NVLink 72 rack scale systems. We had to reinvent the entire supply chain, reinvent systems, reinvent the technology, redistribute our software, refactor our software. Everything, every aspect of it was hard. But what it allowed us to do was to create one fungible system that allows us to transition from data creation, data preparation, to pre-training, to post-training, to agentic inference.

The benefits to customers is incredible. And the reason for that is because you've just spent, you know, and we just mentioned each gig-a-watt of technology and NVIDIA's revenue exposure in the Hopper timeframe with Hopper plus Infiniban. And now Vera Rubin and CPU and three types of different networking, because it takes that many types of networking to address the entire world's data center, not to mention the scale-in security networking and the scale-across multi-campus networking. So you could argue five different types of networking systems.

And then, of course, rock. And all of that increased our revenue contribution or revenue opportunity per gig-a-watt to $40 billion. So each gig-a-watt of data center increased from, say, $30 billion about five years ago to now $60 billion today. Of course, the productivity is tremendous.

The performance is incredible in comparison. But you're talking about a $60 billion investment. And to the extent that you could use it across multiple phases of the AI lifecycle, run every single type of model you can imagine running on it, where there's diffusion or autoregressive or state space or some hybrid version of that, every version of attention mechanism you can think of, small or large models, the light, the investment that you make will be preserved and useful and productive for a lot longer time.

And so I think our advantage in this new world is really quite extraordinary. And it could explain, you know, why it is that our growth is actually accelerating. It was already large, but now it's accelerating.

Let's see. You asked about GROC. I was super excited about GROC 3. We achieved a record token interactivity rate, extremely low latency performance generation. The team is doing fantastically. We spent the last several months fusing the MV-Link architecture, which will be the core. And it'll be the core engine. And then for services that would like to have super high interactivity, super high speed token generation done, you know, the throughput is going to be a lot lower.

The cost per token will be higher, but you could associate it with, you know, high ASP services. And so for those companies, you could bolt on one of our GROC accelerators. I'm super excited about that. But the vast majority of the world's data centers will just be very Ruben, MV-Link 72.

It's, we grew 100% year over year. This year. The unconstrained, you know, is significant. And so we're just going to have to go work hard to get more capacity.

And, and you know, we have, we have a large supply chain. We have a really gigantic supply chain. And, and so we have incredible partners. And we've secured a lot of supply, but we just need a lot more to break it down. The, the way to think about that is most people see just hyperscalers.

And that's half of the war, that's half of the picture. The other half of the picture is what we call AC. And that's all the enterprise, the neo clouds, the sovereign AIs. You know, that part of the world is invisible to everybody. And the reason for that is because they, they don't buy custom chips. They don't buy chips one at a time. They really need an entire factory platform built for them.

And, and so that's, that's a space that we add just a tremendous amount of value. Now, of course back in this hyperscale space, that's growing incredibly too. Right. You know that they, they now have backlogs of $2 trillion. You know that when they stand up and video compute. When that happens, the revenues go up. Their earnings contribution go up.

Compute is profitable, very profitable today. And compute directly translates into increased revenues. And so there's a, there's a, just a race to want to bring more and video compute online, both at the hyperscalers. But what you don't see is just really tremendous opportunities outside the hyperscalers. But the other, the other part of it is, and so the reason why, you know, we mapped it out for you.

In the case of Hopper, we were at about 18 billion per gigawatt. For Grace Blackwell, we're at about 25 billion per gigawatt. And for Vera Rubin, it's about 40 billion per gigawatt. And, and the productivity is, you know, X factors increase in each generation. And so customers want to race to the next generation as fast as they can.

Meanwhile, because in videos compute is so productive, the tokens they're generating, the GPU hours they're renting out is insanely profitable. As you know, their margins are fantastic.

And so, so all of that is just simultaneously happening happening. I think the big picture is, is that we're going through this platform shift and it affects every computer company. And every company, every industry in the world uses computers. So therefore, every industry is affected, every company is affected. And this new way of doing computing is intelligent.

It's not based on retrieval of files, but it's now generative, generating intelligence. And that requires compute, but the results you get is phenomenal. The results you get is tremendously better. And so, you know, we're just seeing that across the world. Everybody wants to be part of the AI revolution. Everybody will have to be part of this computing shift. And everybody has to build infrastructure.

Well, we're building something very different. You know, whereas, whereas many of these, these XPUs are inference specific chips for one cloud or one service. NVIDIA is a platform, an entire AI factory platform that spans the entire AI life cycle that you can use in any cloud. It's in every cloud. You can run anywhere will help you set it up anywhere.

And so we built something very different. These, all of the AI services at some point are going to want to go around the world. And those data centers won't necessarily be just built by them. And, and, and also, you know, I fully expect, and so they're, they're going to run, and I think they're going to run on NVIDIA all around the world. And, and of course, I think our technology, I have a hundred percent confidence that our technology will continue to be extraordinary for them and that the economics of using our technology, you know, whether it's from data processing to, to training to post-training to agentic processing, our technology is going to be extraordinary for them.

They're going to use it. And so I'm, every confident that they're going to be customers and partners of ours for a very long time. Now, having said that, taking a step backwards, investing in these two companies or there are several AI labs that we've invested in, investing in these companies are once in a generation opportunity. I think the only regret that I have is that I didn't invest more and sooner.

And both of the two of the companies will likely go public soon and others will follow. And these will be some of the most consequential technology companies in history. And so I'm delighted to be friend, to be their friend. I'm delighted to partner with them. I'm delighted that they're building an ecosystem on top of the NVIDIA architecture.

I'm delighted that there's, they're counting on us to scale up. And I, I have a hundred percent confidence that, you know, through, through a quite a long period of time, they're going to be utilizing NVIDIA Compute for a lot of their computing. And so I feel great about it.

So, Vivek, let me add a little bit more regarding the commitments and the portion within those commitments, which is our supply commitments. This is essential. This is essential for the raising of your ribbon today, as well as all next year. You can see that those commitments, the biggest parts of them are in the first three years, and we will use that to build the products that we need. This also gives us the confidence in terms of our growth and revenue, given how much we have already aligned in commitment in terms of our supply, as well as capacity that we would need.

The world will need both closed models and open models. And both closed models and open models are skyrocketing in use.

I would say nearly all open models run on NVIDIA. And the reason for that is because NVIDIA is footprint around the world is the highest.

And our architecture is the most fungible. It's everywhere. It's in PCs and edge devices like DGX Spark, which is doing great. All the way to robots and workstations and your on-prem data centers.

Open models are doing incredibly well. Closed models we know are doing incredibly well. The Frontier Labs, their sales are skyrocketing. Their margins are fantastic. They're generating profitable tokens. They're only limited by the amount of compute. That is equally true for open models.

And our position in open models is very good. You know, because the CUDA ecosystem is literally everywhere. The open models are also foundational to just about every AI startup and every enterprise company around the world. It's vital to them. And the reason for that is because you should rent intelligence, strong intelligence, smart intelligence, wherever you can, which is the reason why, you know, we rent it. And I encourage my employees to use the cloud services as much as it can.

But every major company and every, surely every country and every startup needs to build their domain specific, their proprietary AI, their proprietary alpha. And the open models reaching Frontier levels has made it possible, has enabled them to all do that. One of the areas where Frontier models is vital is cybersecurity. You see the number of cybersecurity companies that are enabled by Frontier models so that they could have distributed, massively distributed, continuously running autonomous cybersecurity systems to defend.

Those companies are emerging. They're some amazing companies. They couldn't do it without open models. And so open models is both incredibly successful and has finally reached the Frontier. But it's also vital to the American economy. It's vital to the world economy. It's vital to companies to build their own proprietary AI. You can't do it without one or the other. Both are going to be extraordinarily successful.

And lastly, as you know, our market footprint of all AI models were the only, I think we're the only platform or fairly certain we're the only platform that runs every Frontier model. And whether it's closed or open. Most of them were built on NVIDIA. And so they run great on NVIDIA. And so we're delighted by any model succeeding.

So long as models succeed, I'm very happy. And both closed and open models are going to succeed. And they're both simultaneously driving our sales.

I appreciate that. It's going to inflect further. Today, the vast majority of AI is prompted by people. I believe that this last month it has crossed.

Most AI are now agentic. But in the future, in the future, every company will have a whole bunch of agents. You know, we have 40,000 employees roughly. And the future will have 400,000 agents, 4 million agents. And those agents are running continuously. They're running in the background. If you have anybody who builds, if you know anybody who builds edge, you know, personal AI agents, and they run it on their, like, a DGX Spark.

And, you know, I know a lot of people who run it on DGX stations, this incredible workstation that we built, and you can buy it from Dell, and they're incredible. And these AI agents running on a DGX station runs 24-7. Because you've got stuff for it to do all the time. And so, when the world goes to agentic, fully agentic, agentic systems, you're going to have agents running all the time, working with other agents running all the time. And those will be working in the background, improving your company, you know, improving your lives.

In a lot of ways, we're kind of recursive at this point, at this point. And you could argue it's coarse-grained. But every time you run through an agent, it reflects on how it could do a better job next time, and it updates the skill file. And so, the skills document, the markdown, is updated at the end of every single one of them. And so, next time you run it, it's going to get better.

It's a, you know, kind of a loosely coarse-grained self-improvement. And so, you see that all over, always. And so, in a lot of ways, and for many tasks, we could say that we've already achieved AGI. I think all of those milestones and all those, you know, they're kind of senseless at this point. I think the most important thing that matters for the industry is that one, AI is now doing productive and useful work.

Two, AI is generating profitable tokens. And three, if we had more compute, we could generate more profitable tokens, which results in more profit, for all of the services. This is the exact phase we're at, which is the reason why everybody's leaning in.

There's something funny I could say, but I'm going to just not. In the last year, one of the funnest things to do is just to go figure out where I go for dinner and who I have dinner with. Their stock price doubles the next day.

I think the answer is our entire supply chain is challenged. Everybody is really running flat out. And more capacity is coming online all the time, which is one of the advantages, what's going to happen this year. It's not going to come online in just an instance in time, but it's going to come online every day. Yields are going to get improved.

We're going to be doing yield improvement. We're going to work hard on working with every one of our suppliers.

It's not even next year yet. We've got lots and lots of time to work hard every day. At this moment, we have supply for 70%.

We have more supply than 70%, but about 70%. Our demand is much higher than that. We've got to go work hard, or we're going to be disappointing customers.

And we like not to disappoint our customers, and we like to work hard for them. So I'm going to need the help of the entire supply chain to help me out here. But they all know that.

What I'm telling you about our needs for next year is exactly consistent with what I've told them. Everybody's on the exact same song sheet and I'm trying to be as transparent as we can because we're talking about big numbers.

Great question. Yeah, great question. Great question. And very simple. Is our goal to put as much compute on the plot of land? Is our goal to put more compute into one gigawatt or less? And so obviously, we would like the speed of light answer. The perfect answer is actually infinity per gigawatt. And so if we could literally get a trillion dollars of compute into one gigawatt and one piece of land power shell, it would be a fantastic outcome.

And so the answer is directly in that direction. We started in the world of general purpose computing during Moore's law. We were probably, you know, pick your favorite number, but I'm going to go with something like $5 billion, $3 billion per gigawatt of compute with general purpose computing. And then eventually with Hopper, it was 18. Now, Grace Blackwell is 25.

Next, Vera Rubin is 40. And after that, it's going to be higher. And that's excellent. That's fantastic for the industry. It's fantastic for customers. So long as the productivity of it continues to grow, the durability and the fungibility continues to grow, then people are happy to invest in assets that generates revenues, generates profits, and helps them recoup their returns so incredibly fast. I mean, I heard the other day that return on investment capital is now less than a year.

And we're talking about $50 billion data centers. And so that tells you something about, about the productivity of NVIDIA's technology and the rentability of it. I want to thank all of you for joining us today.

There are no further questions at this time to see a hurry. I'll turn the call back over to you.

Thank you. Before we close, please note that Jensen will be participating in a keynote fireside chat at the Goldman Sachs communicopia and technology conference in San Francisco on September 10th. He'll also be giving a keynote at GTC Berlin on October 21st. Our earnings call to discuss the results of our third quarter of fiscal 2027 is scheduled for November 17th. Thank you for joining us today. Operator, please close the call.