Jensen Huang: evaluation

Transcript context

…The following is a conversation with Jensen Huang, CEO of NVIDIA, one of the most important and influential companies in the history of human civilization. NVIDIA is the engine powering the AI revolution, and a lot of its success can be directly attributed to Jensen’s sheer force of will and his many brilliant bets and decisions as a leader, engineer, and innovator. This is Lex Fridman Podcast. And now dear friends, here’s Jensen Huang. You’ve propelled NVIDIA into a new era in AI, moving beyond his focus on chip scale design to now rack scale design. And I think it’s fair to say that winning for NVIDIA for a long time used to be about building the best GPU possible, and you still do, but now you’ve expanded that to extreme co-design of GPU, CPU memory, networking, storage, power cooling, software, the rack itself, the pod that you’ve announced, and even the data center. So let’s talk about extreme co-design. What is the hardest part of co-designing a system with that many complex components and design variables? Yeah, thanks for that question. So first of all, the reason why extreme co-design is necessary is because the problem no longer fits inside one computer to be accelerated by one GPU. The problem that you’re trying to solve is you would like to go faster than the number of computers that you add. So you added 10,000 computers, but you would like it to go a million times faster. Then all of a sudden you have to take the algorithm, you have to break up the algorithm, you have to refactor it, you have to shard the pipeline, you have to shard the data, you have to shard the model. Now all of a sudden when you distribute the problem this way, not just scaling up the problem, but you’re distributing the problem, then everything gets in the way. This is the Amdahl’s law problem where the amount of speed up you have for something depends on how much of the total workload it is. And so if computation represents 50% of the problem, and I sped up computation infinitely like a million times, you know, I only sped up the total workload by a factor of two. Now all of a sudden, not only do you have to distribute a computation, you have to shard the pipeline somehow. You also have to solve the networking problem because you’ve got all of these computers are all connected together. And so distributed computing at the scale that we do, the CPU is a problem, the GPU is a problem, the networking is a problem, the switching is a problem. And distributing the workload across all these computers is a problem. It’s just a massively complex computer science problem. And so we just gotta bring every technology to bear. Otherwise, we scale up linearly or we scale up based on the capabilities of Moore’s Law, which has largely slowed because Dennard scaling has slowed. I’m sure there’s trade-offs there. Plus you have a complete disparate disciplines here. I’m sure you have specialists in each one of these high bandwidth memory, the network and the NVLink, the NICs, the optics and the copper that you’re doing, the power delivery, the cooling, all of that. I mean, there’s like world experts in each of those. How do you get ’em in a room together to figure out-…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.