NVIDIA (NVDA) Q2 FY27 Earnings Call Transcript

Operator00:00

Good afternoon. My name is Tiffany and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's second quarter earnings call. All lines have been placed on mute to prevent any background noise.

Operator00:12

After the speaker's remarks, there will be a question and answer session. If you would like to ask a question during this time, simply press star followed by the number one on your telephone keypad.

Operator00:23

If you would like to withdraw your question, press star one again. Thank you. Toshiya Hari, you may begin your conference.

Toshiya Hari00:38

Thank you. Good afternoon and welcome to NVIDIA's conference call for the second 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.

Toshiya Hari00:53

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.

Toshiya Hari01:10

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.

Toshiya Hari01:26

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.

Toshiya Hari01:38

All our statements are made as of today, August 26, 2026. based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

Toshiya Hari01:51

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.

Toshiya Hari01:54

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

Prepared remarks

Colette Kress02:12

Thanks, Toshiya. 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.

Colette Kress02:21

The surge in AI demand is driving a global infrastructure build out supported by an expanding and diverse set of growth opportunities.

Colette Kress02:39

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.

Colette Kress02:57

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.

Colette Kress03:06

Hyperscale revenue of 49 billion grew 13% sequentially driven by sustained strength in Blackwell.

Colette Kress03:20

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.

Colette Kress03:41

With cloud industry backlog now greater than 2 trillion capex 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.

Colette Kress04:00

Building on its already vast installed base of NVIDIA compute, AWS is deploying an additional 2 million GPUs starting this quarter through the second quarter of fiscal 29 along with Vera CPUs some integrated with Rubin other standalone.

Colette Kress04:19

AWS will serve NVIDIA Nematron family of open models on Amazon Bedrock and SageMaker Amazon will also adopt our full physical AI stack Omniverse Cosmos Isaac and Jetson to power its fleet of warehouse robots

Colette Kress04:29

ACINE revenue of 40 billion increased 25% sequentially and 138% year over year.

Colette Kress04:43

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 build outs.

Colette Kress05:00

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 eight gigawatts in total installed capacity.

Colette Kress05:11

Up from approximately three gigawatts at the end of 2025. Incredibly, we are seeing demand acceleration even at our scale.

Colette Kress05:24

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.

Colette Kress05:36

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.

Colette Kress05:49

Besides building the best AI computing technologies and the most capable supply chain, NVIDIA has three unique capabilities that are engines powering our growth.

Colette Kress06:03

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.

Colette Kress06:17

NVIDIA runs the leading closed models OpenAI, Anthropic, Grok, Meta, Gemini, and the leading open models TML, Mistral, Quinn, Kimi,

Colette Kress06:38

GLM, DeepSeek, MiniMax, and Nematron. We're 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.

Colette Kress06:52

One platform fungible for every model and workload durable for the entire life cycle of AI that combination of performance fungibility and durability is what

Colette Kress07:09

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 TAM.

Colette Kress07:33

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 and VLink Infiniband or Ethernet and Grok LPU announced earlier this week.

Colette Kress07:50

Our ability to extreme code design across GPU, CPU, NVLink scale up networking, scale out networking, systems, algorithms, and software enables us to deliver X factor performance gain every generation.

Colette Kress08:05

Vera Rubin exemplifies this delivering 30x higher throughput per megawatt and 35x lower token cost relative to Grace Blackwell Ultra.

Colette Kress08:23

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

Colette Kress08:45

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.

Colette Kress09:04

Rising adoption of agentic AI is driving an acceleration in demand for data center CPUs. Our Grace CPU introduced in 2021 has been a great success with revenue on a trailing 12 month basis exceeding 5 billion.

Colette Kress09:47

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 1.8 x 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 hyperscaler Neo cloud AI lab and system OEM with shipments already underway to our lead partners including OCI, SpaceX AI and starting this quarter AWS.

Colette Kress10:10

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.

Colette Kress10:22

Since the announcement of our Grok partnership last year, we've been working to unite NVIDIA's high throughput and Groq's high interactivity architectures.

Colette Kress10:46

At Hot Chips earlier this week, we announced that Grok 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.

Colette Kress10:56

We expect to ship Grok 3 LPX in volume later this quarter to early adopters Nebius will be the first.

Colette Kress11:09

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 TAM.

Colette Kress11:26

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.

Colette Kress13:03

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 uptake demand from our vast developer ecosystem hyperscalers will remain a major growth driver but non-hyperscaler growth our ACINE 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 Figma and Together AI now exceed 1 billion in annualized run rate revenue up from 13 companies in Q4 of last year with vertical enterprise software logging the fastest growth.

Colette Kress13:17

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.

Colette Kress13:49

Hudson River Trading and Jane Street are leveraging NVIDIA's powered AI factories to accelerate quantitative trading Samsung Electronics is using NVIDIA Kulitho to achieve up to 20x greater performance in computational lithography while Bristol-Myers Squibb is investing in Vera Rubin AI factory a fast follow to the Roche and Lilly build outs as drug R&D timelines compress from years to months.

Colette Kress14:11

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.

Colette Kress14:26

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.

Colette Kress14:49

We helped Corweave, Nebius, and Enscale build entire infrastructure businesses and Neo clouds are emerging everywhere Firebird in Armenia, Kosova Technologies across Africa, GMI Cloud in Taiwan, Yoda and NESA in India.

Colette Kress15:20

Firmus 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.

Colette Kress15:39

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.

Colette Kress15:54

Neo clouds are seeing strong demand pipelines for many diverse off-takers rather than allocating their entire capacity to a single long term offtake guarantee that lenders typically require to finance a data center independently we have introduced a revenue

Colette Kress16:15

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 in a portion of the Neo cloud's revenue earned above that floor.

Colette Kress16:36

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.

Colette Kress16:51

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.

Colette Kress17:15

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.

Colette Kress17:31

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.

Colette Kress17:46

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.

Colette Kress18:01

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 intelligence, more users and more revenue.

Colette Kress18:18

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.

Colette Kress18:57

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 Goldman Sachs 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.

Colette Kress19:12

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 campus.

Colette Kress19:21

The initial deployment expected to support 4.25 gigawatts of AI factory capacity will be utilized by OpenAI.

Colette Kress19:36

Each generation of NVIDIA AI factory systems deployed at Port Spike could represent approximately 1.5 million NVIDIA GPUs and over 20 years the site could support multiple upgrade cycles.

Colette Kress19:39

Here's the essential economic point.

Colette Kress19:48

The LPS commitment secures a long lived AI factory site while the NVIDIA compute within the data center can be upgraded repeatedly.

Colette Kress20:09

This project deepens our longstanding 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.

Colette Kress20:27

For another Frontier AI lab we will provide selective credit enhancement for nearly two gigawatts of compute This complements the substantial NVIDIA compute capacity they've secured independently without NVIDIA's credit support.

Colette Kress21:17

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 shift the creation of one of the most important technologies in human history and these are once in a generation companies their 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.

Colette Kress21:39

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.

Colette Kress21:49

This remains compute we ship will be consumed by investment grade customers or those that are backed by one.

Colette Kress22:02

In Q2, we shipped less than 1% of our total data center revenue in Hopper 200 products to customers based in China in accordance with the US government licenses.

Colette Kress22:06

Current Hopper shipments are dilutive to corporate gross margins.

Colette Kress22:17

And given ongoing geopolitical uncertainty, there is no China data center compute revenue in our forward outlook.

Colette Kress22:27

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.

Colette Kress22:39

Gap and non-gap operating expenses were up 10% and 11% sequentially primarily due to high compute infrastructure costs and compensation and benefits costs.

Colette Kress22:47

Our non-gap effective tax rate of 16% increased from a year ago primarily due to higher revenue.

Colette Kress23:07

On our balance sheet, inventory increased to 32 billion as we prepared 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.

Colette Kress23:21

In Q2, 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.

Colette Kress23:39

Relative to our plan to return 50% or more of free cash flow we returned 60% on a year-to-date basis and going forward we intend to increase and return excess free cash flow net of strategic uses.

Colette Kress23:50

Let me turn to the outlook for the third quarter. Total revenue is expected to be 108 billion plus and minus 2%.

Colette Kress24:06

We expect sequential growth to be driven primarily by ACINE with data center while growth in hyperscale is expected to reaccelerate in Q4 and into fiscal year 28 as supply of Vera Rubin grows over time.

Colette Kress24:33

We see Vera Rubin accounting for about 20% of data center revenue in Q3. 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.

Colette Kress24:47

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.

Colette Kress24:55

The magnitude of the price increase has exceeded our prior expectations and are headed even higher into next year.

Colette Kress25:09

As a result we are resetting expectations today. For Q3 we expect gap and non-gap gross margins to be 74% plus or minus 50 basis points.

Colette Kress25:11

We expect margins to bottom

Colette Kress25:28

In Q4 in the 71 to 72% range before settling at 72 to 73% in fiscal year 28 as executed price increases take effect in Q1.

Colette Kress26:13

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.

Colette Kress26:28

For the full year we now expect OPEX 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.

Colette Kress26:43

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

Questions and answers

Colette Kress26:51

With that we will now transition to Q&A operator please poll for questions.

Operator27:02

At this time I would like to remind everyone in order to ask a question press star then the number one on your telephone keypad.

Operator27:08

We'll pause for just a moment to compile the Q&A roster.

Operator27:16

Your first question comes from the line of Joseph Moore with Morgan Stanley. Your line is open.

Joseph Moore27:36

Great. Thank you. um I wonder if you could give us color on the 70% and what gives you the confidence to guide a full year out you haven't been doing that and then what's the gap between that amount of growth and the 100% demand growth you know what is the kind of key constraint that that separates those numbers and could you close those gaps over time.

Unidentified speaker28:15

Yeah thanks Joe um as you probably are aware AI has become useful and the AI 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 these agents go through reasoning and planning 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.

Jensen Huang28:16

And so the amount

Jensen Huang28:48

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 AI factory platform a full stack system and you know customers can still mix and match however most companies just don't have the skills to do that or desire to do that.

Jensen Huang29:05

And so there's an entire part of the market that we experience growth their sovereign AI the regional AIs their neo clouds their AI startups their enterprises where we're seeing which represents about half of our business and that's growing 100% a year.

Jensen Huang29:20

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 I think the demand that we see is driven by all of those factors.

Jensen Huang30:37

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 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 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 we're 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.

Jensen Huang31:26

It is the case that we've never forecasted or never guided to a year in advance 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.

Operator31:37

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

CJ Muse31:40

Yeah good afternoon thank you for taking the question.

CJ Muse32:03

Um there's tremendous investor focus on your inference market share can you speak to the evolving workloads you're seeing with agentic AI and how you see your share evolving year over time particularly when you reflect on the growing value of the TAM you're seeing with each new full stack generation your expectation for greater growth from ACINE and then also including Grok 3 LPX we'd love to hear your thoughts.

Jensen Huang32:16

Yeah thanks CJ um the AI life cycle is getting way more complex than it used to be. and it's playing

Jensen Huang35:11

You know into NVIDIA's architecture much 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-train 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 MVLink 72 and was a big surprise on the world when we first created the first 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 MVLink 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 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 gigawatt of technology and NVIDIA's revenue exposure in the Hopper timeframe with Hopper plus Infiniband 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 Grok and all of that increased our revenue contribution or revenue opportunity per gigawatt to 40 billion dollars so each gigawatt of data center increased from say 30 billion dollars about five years ago to now 60 billion dollars today of course the productivity is tremendous the performance is incredible in comparison but you're talking about a 60 billion dollar investment and to the extent that you could use it across multiple phases of the AI life cycle run every single type of model you can imagine running on it whether it's diffusion or auto-regressive or state space or some hybrid version of that every version of attention mechanism you can think of small or large models

Jensen Huang35:34

The 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.

Jensen Huang36:35

Let's see you asked about Grok super excited about Grok 3 we achieved a record token interactive interactivity rate extremely low latency performance generation the team is doing fantastically we spent the last several months fusing the MVLink 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 Grok accelerators I'm super excited about that but the vast majority of the world's data centers will just be Vera Rubin, MVLink 72.

Operator36:45

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

Stacy Rasgon37:28

Hi, guys. Thanks for taking my question. So the 70% growth in fiscal 28, which I guess is sort of calendar 27, so that's something like I don't know a 200 billion dollar uptick versus the prior outlook if I back it out the prior outlook was a trillion dollars over the three years so this is probably 200 billion more I was just wondering if you could talk us through the contributors to that increase across the different products Vera and CPUs and Grok and everything else and also you talked about your price increase that takes effect in Q1 so I assume some of this is is pricing and I guess I'm also curious maybe I'm squeezing too many questions in here but I'm also curious just it's a constrained number what would it be if it wasn't constrained?

Jensen Huang40:59

Um the unconstrained would be a lot higher it's we grew 100% year over year this year the unconstrained you know is is significant and so we're just going to have to go work hard to get more capacity and you know we have a large supply chain we have a really gigantic supply chain 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 way to think about that is most people see just hyperscalers and 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 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 so 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 now have backlogs of two trillion dollars you know that when they stand up NVIDIA compute when that happens their 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 just a race to want to bring more NVIDIA compute online both at the hyperscalers but what you don't see is just really tremendous opportunities outside of the hyperscalers but the other part of it is and 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 about 25 billion per gigawatt and for Vera Rubin it's about 40 billion per gigawatt 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 NVIDIA's 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 I think the big picture is is that we're going through this platform shift and it affects every computer company and 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 you we're just seeing that across the world everybody wants to be part of the AI revolution everybody want everybody will have to be part of this computing shift and everybody has to build infrastructure

Operator41:09

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

Vivek Arya42:18

Uh thanks for taking my question um and thanks for providing all the transparency and all the commitments and guarantees that you have for a number of years when I just you know add up everything that's in the CFO commentary I get to a number of about 500 billion or so obviously over the next several years so I had a few questions related to that first is is that the takeaway that that the sum of all your ecosystem investments over the next several years is in that ballpark or are there other equity or other investments that could still be ahead that's one secondly if there is a specific cash you know part of that that we should think about in fiscal 28 and then Jensen a lot of these investments are you know designed to help the Frontier labs especially OpenAI and Anthropic but both of them are designing their own custom chips in fact OpenAI just you know in the last few days spoke about jalapeno and their claims about being better than Blackwell and so forth so how are you balancing this dynamic where you want to invest a lot in the ecosystem but part of that ecosystem wants to develop competitive solutions thank you

Jensen Huang43:02

Well, we're building something very different. You know, whereas many of 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

Jensen Huang43:17

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

Jensen Huang43:52

And also you know I fully expect and so they're going to run in I think they're going to run on NVIDIA all around the world and of course I think our technology I have 100% 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 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 have every confident that they're going to be customers and partners of ours for a very long time

Jensen Huang44:50

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 that there's they're counting on us to scale up and I I I have 100% confidence that you know through 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.

Colette Kress45:26

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 for the raising of Vera Rubin 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 would also gives us the confidence in terms of our growth in revenue given how much we have already aligned in commitment in terms of our supply as well as capacity that we would need.

Operator45:38

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

Timothy Arcuri46:07

Hi, thanks a lot. Um Jensen I wanted to ask about open source um there's a lot of talk about that these models could gain share for workload in the US you're obviously well positioned with Nemotron but on the other hand a lot of the end demand is being driven by these big frontier model companies so there's a lot of investors that equate open models as being negative for the growth of those companies so how do you sort of put and take that do you see the rise of open models as being good for NVIDIA or ultimately negative thanks

Jensen Huang46:23

Um the world will need both closed models and open models and both closed models and open models are skyrocketing in use

Jensen Huang49:03

Um near most I would say nearly all open models run on NVIDIA and the reason for that is because NVIDIA's footprint around the world um 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 you know closed models we know are doing incredibly well the frontier labs their scales 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 model 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 they 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 cyber security you see the number of cyber security companies that are enabled by frontier models so that they could have distributed massively distributed continuously running autonomous cyber security systems to defend those companies are emerging. There's some amazing companies they couldn't do it without open models and so open models is 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 without one or the other both are going to be extraordinarily successful

Jensen Huang49:24

And lastly, as you know, our market footprint of all AI models, we're the only I think we're the only platform I'm fairly certain we're the only platform that runs every frontier model and whether it's closed or open.

Jensen Huang49:46

Most of them were built on NVIDIA and so they run on 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.

Operator49:58

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

Ben Reitzes50:48

Yeah, hey, thanks. I wanted to ask you a question, Jensen, about demand in a different way. You know, you talked about demand growing 100% next year and I wanted to kind of get a sense for a couple things driving that and even beyond that and there's two concepts here there's recursive self-improvement which apparently at Anthropic and OpenAI is is going very well with you know AI that improves itself and even OpenAI said they could hit AGI by the end of this year and you know with the developments in RSI as well as AGI what happens to industry demand does you know does it inflect further and what does it mean for NVIDIA when that when those things take place and how are you looking at that as a demand catalyst thanks

Jensen Huang53:10

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 every company will have a whole bunch of agents you know we have 40,000 employees roughly in the future we'll 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 I know a lot of people who run it on DGX stations this incredible workstation that 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 got stuff for that to do for it to do all the time and so the when the world goes to agentic fully agentic systems you're going to have agents running all the time working with other agents running all the time and those just be working in the background improving your company you know improving your lives um in a lot of ways we're kind of recursive at this point and you could argue it's coarse grained um 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 close it's a you know kind of a loosely coarse grain self-improvement and so you see that all over all 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 that 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.

Jensen Huang53:16

This is the exact phase where we're at which is the reason why everybody's leaning in.

Operator53:26

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

Jim Schneider54:03

Good afternoon. Thank you for taking my question. If you think about the 100% growth you talked about in terms of the plus unconstrained demand growth you're expecting the 70% you expect to fulfill in terms of supply can we talk about some of the or rank order some of the most acute constraints whether that be things like data center power and shell availability DRAM wafer foundry availability etc if you can maybe help us understand you know which are the biggest among those that would be very helpful thank you

Jensen Huang54:11

There's something funny I could say but I'm going to just not the

Jensen Huang54:34

You know 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 and their stock price doubles the next day I think the answer is is our entire supply chain is challenged and it's everybody is really running flat out

Jensen Huang55:54

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 you know 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 you know and so we just we have a it's not even next year yet and so we've got lots and lots of time work hard every day and so at this moment we have supply for 70% we have more supply than 70% but about 70% our demand is much higher than that and we've gotta go work hard or you know we're going to be disappointing customers and we would like not to disappoint our customers and we like to work hard for them and so I've got 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

Operator56:06

Your final question comes from the line of Aaron Rakers with Wells Fargo. Your line is open.

Aaron Rakers56:27

Yeah, thanks for taking the question. I want to go back to the gigawatts, the 25 to 40, and maybe try and understand, like, you know, I think Jensen, you said at some recent conferences that you know, that's going to further scale. So, you know, as we think about the path even beyond Vera Rubin, we think about Vera Rubin Ultra and so on and so forth.

Aaron Rakers56:49

Like, should we really conceptualize like 40 billion goes to 60 billion, 80 billion and then you know, I guess you know, underneath of that question is how do we kind of think about your ability to scale the capacity deployments you know, is it a linear function or is there something that you know, kind of unlocks your ability to supply more demand as we look through fiscal 27.

Jensen Huang56:50

Great question.

Jensen Huang56:50

28, sorry.

Jensen Huang57:15

Yeah, great question. Great question. And very simple. Is our goal to put as much compute on a plot of land is our goal to put more compute into one gigawatt or less? And so obviously, we would like that, you know, the speed of light answer, the perfect answer is actually infinity per gigawatt.

Jensen Huang57:57

And 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 directionally in that direction. We started 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 dollars 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.

Jensen Huang58:42

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 dollar data centers and so that tells you something about the productivity of NVIDIA's technology and the rentability of it I want to thank all of you for joining us today

Operator58:52

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

Toshiya Hari59:18

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

Closing

Operator59:24

This concludes today's conference call you may now disconnect