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Jensen Huang CEO
Thanks Colette. There’s been a lot of talk about an AI bubble. From our vantage point we see something very different. As a reminder, Nvidia is unlike any other accelerator. We excel at every phase of AI from pre training and post training to inference and with our two decade investment in CUDA X acceleration libraries, we are also exceptional at science and engineering simulations, computer graphics, structured data processing to classical machine learning.
The world is undergoing three massive platform shifts at once. The first time since the dawn of Moore’s Law. Nvidia is uniquely addressing each of the three transformations. The first transition is from CPU general purpose computing to GPU accelerated computing. As Moore’s Law slows, the world has a massive investment in non AI software from data processing to science and engineering simulations representing Hundreds of billions of dollars in cloud computing spend each year. Many of these applications, which ran once exclusively on CPUs are now rapidly shifting to CUDA. GPUs accelerated computing has reached a tipping point.
Secondly, AI has also reached a tipping point and is transforming existing applications while enabling entirely new ones for existing applications. Generative AI is replacing classical machine learning in search ranking, recommender systems, ad targeting, click through prediction to content moderation. The very foundations of hyperscale infrastructure. Meta’s gem, a foundation model for ad recommendations trained on large scale GPU clusters and exemplifies this shift in Q2. Meta reported over a 5% increase in ad conversions on Instagram and 3% gain on Facebook feed driven by generative AI based Gem. Transitioning to generative AI represents substantial revenue gains for hyperscalers.
Now a new wave is rising. Agentic AI systems capable of reasoning, planning and using tools from coding assistants like Cursor and quadcode to radiology tools like idoc, legal assistants like Harvey and AI chauffeurs like Tesla, FSD and Waymo. These systems mark the next frontier of computing. The fastest growing companies in The World Today OpenAI, Anthropic, XAI, Google, Cursor, Lovable, Replit, Cognition AI, Open Evidence, Abridged, Tesla are pioneering agentic AI. So there are three massive platform shifts. The transition to accelerated computing is foundational and necessary, essential in a post Moore’s Law era. The transition to generative AI is transformational and necessary, supercharging existing applications and business models. And the transition to agentic and physical AI will be revolutionary, giving rise to new applications, companies, products and services.
As you can, as you consider infrastructure investments, consider these three fundamental dynamics. Each will contribute to infrastructure growth in the coming years. Nvidia is chosen because our singular architecture enables all three transitions and thus so for any form and modality of AI across all industries, across every phase of AI, across all of the diverse computing needs in a cloud and also from cloud to enterprise to robots. One architecture Toshio, back to you. We will now open the call for questions. Operator, would you please pull for questions?
Operator
Thank you. At this time I would like to remind everyone in order to ask a question, press star. Send the number one on your telephone keypad. We’ll pause for just a moment to compile the Q and A roster. As a reminder, please limit yourself to one question. Thank you. Your first question comes from Joseph Moore with Morgan Stanley. Your line is open.
Morgan Stanley Analyst
Great, thank you. I wonder if you could update us. You talked about the 500 billion of revenue for Blackwell plus Rubin in 25 and 26 at GTC at that time you talked about 150 billion of that already having been shipped. So as the quarters wrapped up, are those still kind of the general parameters that there’s 350 billion in the next kind of, you know, 14 months or so and you know, I would assume over that time you haven’t seen all the demand that there is, there’s any possibility of upside down to those numbers as we move forward.
Jensen Huang CEO
Yeah, thanks Joe. I’ll start first with a response here on that. Yes, that’s correct. We are working into our 500 billion forecast and we are on track for that as we have finished some of the quarters and now we have several quarters now in, in front of us to take us through the end of calendar year 26. The number will grow and we will achieve, I’m sure, additional needs for compute that will be shippable by fiscal year 26. So we shipped 50 billion this quarter, but we would be not finished if we didn’t say that we’ll probably be taking more orders. For example, just even today our announcements with KSA and that agreement in itself is 400 to 600,000 more GPUs over three years. Anthropic is also net new. So there’s definitely an opportunity for us to have more on top of the 500 billion that we announced.
Operator
The next question comes from CJ Muse with Cantor Fitzgerald. Your line is open.
Cantor Fitzgerald Analyst
Yeah, good afternoon. Thank you for taking the question. There’s clearly a great deal of consternation around the magnitude of AI infrastructure buildouts and the ability to fund such plans in the roi. Yet you know, at the same time you’re talking about being sold out. Every stood up GP is taken. The world hasn’t seen the enormous benefit yet, you know, from 300. Never mind. Rubin and Gemini 3 just announced Groc 5 coming soon. And so the question is this, when you look at that as the backdrop, do you see a realistic path for supply to catch up with demand over the next 12 to 18 months or do you think it can extend beyond that time frame?
Jensen Huang CEO
Well, as you know, we’ve done a really good job planning our supply chain. Nvidia supply chain basically includes every technology company in the world and TSMC and their packaging and our memory vendors and memory partners and all of our system ODMs have done a really good job planning with us and we were planning for a big year. You know, we, we’ve seen for some time the three transitions that I spoke about. Just, just a Second ago, accelerated computing from general purpose computing. And it’s really important to recognize that AI is not just agentic AI, but generative AI is transforming the way that hyperscalers did the work that they used to do on CPUs, generative AI made it possible for them to move search and recommender systems and you know, add recommendations and targeting. All of that has been generated, has been moved to generative AI and, and it’s still transitioning.
And so whether you, whether you installed Nvidia GPS for data processing or you did it for generative AI for your recommender system, or you’re building it for agentic chatbots and the type of AIs that most people see when they think about AI, all of those applications are accelerated by Nvidia. When you look at the totality of the spend, it’s really important to think about each one of those layers. They’re all growing, they’re related, but not the same. But the wonderful thing is that they all run on Nvidia GPUs simultaneously because the quality of the AI models are improving so incredibly. The adoption of IT in the different use cases, whether it’s in code assistance, which Nvidia uses fairly exhaustively. And we’re not the only one. I mean the fastest growing application in history, combination of cursor and Cloud code and OpenAI’s codecs and, and GitHub Copilot, these applications are the fastest growing in history. And it’s not just used for software engineers, it’s used by, because of vibe coding, it’s used by engineers and marketeers all over companies, supply chain planners all over companies. And so I think that that’s just one example. And the list goes on. Whether it’s open evidence and the work that they do in healthcare or the work that’s being done in digital video editing, Runway and I mean the number of really, really exciting startups that are taking advantage of generative AI and agentic AI is growing quite rapidly. And not to mention we’re all using it a lot more.
And so all of these exponentials, not to mention just today I was reading a text from Demis and he was saying that pre training and post training are fully intact and Gemini 3 takes advantage of the scaling laws and God received a huge jump in quality performance, model performance. We’re seeing all of these exponentials running at the same time. Just always go back to first principles and think about what’s happening from each one of the dynamics that I mentioned before. General purpose computing to accelerated computing, generative AI replacing classical machine learning and of course agentic AI which is a brand new category.
Operator
The next question comes from Vivek Arya with Bank of America Securities. Your line is open.
Bank of America Analyst
Thanks for taking my question. I’m curious, what assumptions are you making on Nvidia content per gigawatt in that 500 billion number? Because we have heard numbers as low as 25 billion per gigawatt of content as high as 30 or 40 billion per gigawatt. So I’m curious what power and what dollar per gigawatt assumptions you are making as part of that 500 billion number and then longer term. Jensen, the 3 to 4 trillion in data center by 202030 was mentioned. How much of that do you think will require vendor financing and how much of that can be supported by cash flows of your large customers or governments or enterprises? Thank you.
Jensen Huang CEO
In each generation, from Ampere to Hopper, from Hopper to Blackwell, Blackwell to Rubin, we are a part of the data center increases. And Hopper generation was probably something along the lines of 20, some odd 20 to 25 Blackwell generation, Grace Blackwell particularly is probably 30 to 30, you know, say 30 plus or minus. And then Reuben is probably higher than that. And in each one of these generations the speed up is X factors and therefore their tco, the customer TCO improves by X factors. And the most important thing is in the end you still only have 1 gigawatt of power. You know, 1 gigawatt data center is 1 gigawatt of power and therefore performance per watt. The efficiency of ...
