NVIDIA Corporation · Q2 FY2027 call

We expect to grow revenue by approximately 70% in fiscal 2028. This is a supply-constrained outlook.

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.

AWS is deploying an additional 2 million GPUs starting this quarter through the second quarter of fiscal 2029, along with Vera CPUs, some integrated with Rubin, others standalone.

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' forecasts point to our growth doubling next year. However, as I mentioned earlier, we expect to grow approximately 70% as we are supply-constrained.

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, InfiniBand or Ethernet, and Groq LPU, announced earlier this week.

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.

Based on our customer demand and improving supply outlook, our preliminary expectation is for CPU revenue to more than double in fiscal 2028, positioning us as one of the world's leading server CPU suppliers.

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.

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 NeoCloud'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

We recognize the scale of this support, and we know some will call this circular financing. We see it differently. We are 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.

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.

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 U.S. government licenses. Current Hopper shipments are dilutive to corporate gross margins, and given ongoing geopolitical uncertainty, there is no China data center compute revenue in our forward outlook.

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.

Total revenue is expected to be $108 billion ±2%. We expect sequential growth to be driven primarily by ACIE with data center, while growth in hyperscale is expected to re-accelerate in Q4 and into fiscal year 2028 as supply of Vera Rubin grows over time. We see Vera Rubin accounting for about 20% of data center revenue in Q3.

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 Q3, we expect GAAP and non-GAAP gross margins to be 74% ±50 basis points. We expect margins to bottom in Q4 in the 71%-72% range before settling at 72%-73% in fiscal year 2028 as executed price increases take effect in Q1.

Memory scarcity today is being driven in large part by the AI build-out itself, and unlike a component that simply raises our cost with no offset benefit. Tighter memory supply is a symptom of the same demand surge that is driving our own growth.

There is sovereign AI, there are regional AIs, there are NeoClouds, there are AI startups at enterprises where we are seeing, which represents about half of our business, and that is growing 100% a year. That part of the world's computing is likely to be larger over time than even what we are currently experiencing in the cloud.

Even though our demand is much greater than 70%, our supply allows us to confidently deliver 70%. We are going to continue to work with our supply chain to increase on that. What we wanted to do is to be consistent with everybody from our customers, our shareholders, our supply chain. Everybody sees the same view.

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 investment that you make will be preserved and useful and productive for a lot longer time. I think our advantage in this new world is really quite extraordinary, and it could explain why it is that our growth is actually accelerating. It was already large, but now it is accelerating.

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.

investing in these companies are a once-in-a-generation opportunity. I think the only regret that I have is that I didn't invest more and sooner. Two of the companies will likely go public soon, and others will follow. These will be some of the most consequential technology companies in history.

The world will need both closed models and open models. 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's footprint around the world is the highest, and our architecture is the most fungible.

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. We have 40,000 employees, roughly. In the future, we'll have 400,000 agents, 4 million agents.

In a lot of ways, and for many tasks, we could say that we have already achieved AGI. I think all of those milestones and all those, they are 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. Three, if we had more compute, we could generate more profitable tokens, which results in more profit for all of the services.

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 got to go work hard, or we're going to be disappointing customers.

We started in the world of general purpose computing during Moore's Law. We were probably, pick your favorite number, but I am going to go with something like $5 billion, $3 billion per gigawatt of compute with general purpose computing. Then eventually with Hopper, it was 18. Now Grace Blackwell is 25. Next, Vera Rubin is 40, and after that it is going to be higher.

I heard the other day that return on investment capital is now less than 1 year, and we are talking about $50 billion data centers. That tells you something about the productivity of NVIDIA's technology and the rentability of it.