NVIDIA's Jensen Huang on AI Chip Design, Scaling Data Centers, and his 10-Year Bets
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No Priors: Artificial Intelligence | Technology | Startups
36 min
3 speakers
8 chapters
transcribed 1 month ago
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What are NVIDIA’s 10‑year strategic bets and why do they matter?
Hi listeners and welcome to No Priors. Today we're here again, one year since our last discussion with the one and only Jensen Huang, founder and CEO of NVIDIA. Today, NVIDIA's market cap is over $3 trillion, and it's the one literally holding all the chips in the AI revolution. We're excited to hang out in NVIDIA's headquarters and talk all things frontier models and data center scale computing and the bets NVIDIA is taking on a 10-year basis. Welcome back, Jensen.
Thirty years in to NVIDIA and looking ten years out, what are the big bets you think are are still to make? Is it all about scale up from here? Are we running into limitations in terms of how we can squeeze more compute memory out of the architectures we have? What are you focused on?
Well, if we take a step back and and think about what we've done, we went from coding to machine learning. from writing software tools to creating AIs and all of that running on CPUs that was designed for human coding to now running on GPUs designed for um AI coding basically. machine learning. And so the the world has changed. The the way we do computing, the whole stack has changed. And as a result, the scale of the problems we could address has changed a lot because we could if you could paralyze your software on one GPU, You've set the foundations to parallelize across a whole cluster, or maybe across multiple clusters or multiple data centers. And so I think we've we've set ourselves up to be able to scale computing uh at a level and develop software at a level that nobody's ever imagined before.
And so we're at the beginning of that. Um uh over the next ten years Uh our hope is that we could double or triple performance every year at at scale, not at chip. At scale. And to be able to therefore drive the cost down by a factor of two or three, drive the energy down by a factor of two, three every single year. When you do that every single year, when you double or triple every year. In just a few years it adds up. And so it compounds really, really aggressively. And so I wouldn't be surprised. If, you know, the way people think about Moore's Law, which is uh uh two X every couple of years, um, you know, we're gonna be on some kind of a hyper Moore's Law curve. And um I I fully hope
that we continue to do that. Well what do you think is the driver of making that happen even faster than Moore's Law? Because I know Morzla was sort of self-reflexive, right? It was something that he said, and then they people kind of implemented it to make it happen.
Yep.
The two fundamental
Um техnical pillars. One of them was Denard scaling, and the other one was Carver Mead's VLSI scaling. And both of those techniques were rigorous techniques. Um, but uh those those techniques have really run out of steam. And and uh so now we need a new way of doing scaling. You know, obviously the new way of doing scaling are are all kinds of things associated with co-design, unless you can modify or change. change the algorithm to reflect the architecture of the system. Or change and then change the system to reflect the architecture of the new software and go back and forth. Unless you can com control both sides of it, you have no hope. But if you can control both sides of it, you can do things like move from FP sixty-four to FP thirty-two to BF sixteen to FP eight to you know FP four to who knows what.
Right. And so and so I think that that co-design is a very big part of that. The second part of it. We call it full stack. The second part of it is uh data center scale. You know, unless you could treat the network as a compute fabric. And and uh push a lot of the work into the network, push a lot of the the work into the fabric. And as a result, you you're compressing. you know, doing compressing at very large scales. And so that that that's the reason why we bought Mellanox and started fusing InfiniBand and MV Link um in such an aggressive way. And now look where MV Link is gonna go. You know, the the compute fabric is going to going to um uh uh scale out. uh what appears to be one incredible processor called a GPU.
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Chapters
8 chapters
1
What are NVIDIA’s 10‑year strategic bets and why do they matter?
0:05–5:28
2
How is NVIDIA outpacing Moore’s Law with new scaling techniques?
5:28–10:30
3
What role does NVLink play in today’s AI‑focused data‑center architecture?
10:30–14:33
4
How does NVIDIA design, build, and optimize super‑scale data centers like the x.AI supercluster?
14:33–19:03
5
In what ways is AI being used to accelerate chip design and performance?
19:03–23:34
6
What is NVIDIA’s vision for embodied AI and digital employees?
23:34–28:09
7
How is generative AI transforming scientific research and discovery?
28:09–33:24
8
How does Jensen Huang personally use AI tools in his daily workflow?
33:24–36:48
Speakers
3 identifiedMore from No Priors: Artificial Intelligence | Technology | Startups
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