And I'm thrilled to announce that Jensen himself is here today. He's going to join me on stage. We're going to spend a few minutes chatting about the partnership. And we're going to see where AI infrastructure goes from here. So with that, let me please welcome to the stage Jensen Huang.
What's up, Jensen? How you doing?
Boy, that's a huge stage. I don't run a long way.
Out of breath? You okay? I know. Let's fire up. Good to see you. There you go. Yeah. Congrats on a great kickoff yesterday, GTC. You guys are off to the races this week.
Look, you maybe heard some of what I just said. So, we're talking about connectivity today.
The next trillion-dollar company, ladies and gentlemen.
Wow! That would be exciting. Let's do it together. Let's do it together. But it really all starts with what's happening today in AI infrastructure kind of more broadly. So, how do you see that from the big picture standpoint? We're at this extraordinary moment. Customer demand's through the roof. How do you see connectivity playing into this and the interconnect that's required?
Yeah, that's really great. You know, yesterday, I said that useful AI has arrived. It's the reason why your demand is going through the roof. It's the reason why my demand's going through the roof.
And this new computing pattern that makes it possible, it's called agents. And these agents have a particular computing platform, a computing pattern that is disaggregated and distributed.
When you take a computing problem and you disaggregate it into a lot of parts and you distribute it across the entire data center, what's necessary is connectivity. That's the reason why Marvell's so well. That's the reason why Marvell is so essential. We've distributed and disaggregated computing so that it runs across these enormous clusters so that we could get aggregating the total compute, the total memory, the total bandwidth that we have. And what makes it possible is connectivity.
Yeah, we're seeing it and then as you think about
They're going to be the next trillion-dollar company.
We got a little work to do, but we're on our way. We're on our way. Thank you, Jensen. Well, let's talk about scale. I mean, we used to talk about tens of GPUs and CPUs and XPUs connected. Now thousands, now maybe millions at some point. So, as you scale the compute and you scale the connectivity, I think we talked about things like agents, but how do you think about that across data centers, within data centers? How do you think about connectivity at large playing that role? And what kinds of technologies do you think are important there?
Well, at the foundation of it, the agent computing pattern requires an orchestration system that allows the large language models, the computing, to be able to think and reason and come up with plans, but it also has to use tools and browse the internet, access memory, access long-term memory, deal with short-term working memory. All of that requires a lot of connectivity. But it's also the case, and if you look at the way we introduced Vera Rubin, Hopper was designed for training. Grace Blackwell introduced NVLink 72, our first scale-up fabric. It introduced the idea of extremely fast inference for MOE models that are very large, mixture of expert models that are extremely large. And so, Grace Blackwell was for inference. Vera Rubin is to run agents, which is the reason why the Vera Rubin system includes, of course, the Vera Rubin thinking AI, but it also includes Vera CPUs for orchestration. It includes Vera CX for storage acceleration, for managing long-term memory. And the way that I think about these systems, sometimes maybe the CSP wants to design their own custom chip. And between us, we also partner together on NVLink Fusion, which makes it possible for you to use the same system architecture and with Vera Rubin inside, some of your semi-custom chips, a lot of your interconnect, silicon photonics and optics and technology such, and we can create essentially a disaggregated, distributed, and heterogeneous data center. And so, that's the big idea. And yet, their system architecture is identical, their networking technology can leverage a lot of NVIDIA's stack. The CPU could be Vera, and yet it can leverage a lot of your stack. So, NVLink Fusion is about taking NVIDIA's technology and our platforms, Marvell's technologies and platforms, and we fuse it. That's why it's called Fusion.
Yeah. No, I think about the partnership, and we've been working together a long time. I think memorializing it with the investment, which we really appreciate. I think it's been huge for us. We're honored to have it.
You know, who doesn't love making money? It's nice to give.
