Jensen Huang: evaluation

Transcript context

…So, as you mentioned, NVIDIA is this company that’s adapting to the environment. So, which point can you say, did the environment change and began adapting sort of secretly- … in the early days from GPU for gaming, maybe the early deep learning revolution to we’re now going to start thinking of it as an AI factory? What does NVIDIA do? It produces AI; let’s build a factory that makes AI. I could reason through that systematically. We started out as an accelerator company. But the problem with accelerators is that the application domain’s too narrow. It has the benefit of being incredibly optimized for the job. You know, any specialist has that benefit. The problem with intense specialization is that, of course, your market reach is narrower, but that’s even fine. The problem is, the market size also dictates your R&D capacity. And your R&D capacity ultimately dictates the influence and impact that you can possibly have in computing. And so, when we first started out as an accelerator, very specific accelerator, we always knew that was going to be our first step. We had to find a way to become accelerated computing. But the problem is, when you become a computing company, it’s too general purpose and it takes away from your specialization. The tur- I connected two words that actually have fundamental tension. The better computing company we become, the worse we became as a specialist. The more of a specialist, the less capacity we have to do overall computing. And so, that… And I connected those two words together on purpose, that the company has to find that really narrow path, step by step by step, to expand our aperture of computing, but not give up on the most important specialization that we had. Okay, so the first step that we took beyond acceleration was we invented a programmable pixel shader. So, that was the first step towards programmability. It was our first journey towards moving into the world of computing. The second thing that we did was we created, we put FP32 into our shaders. That FP32 step, IEEE-compatible FP32, was a huge step in the direction of computing. It was the reason why all of the people who were working on stream processors and, you know, other types of data flow processors discovered us. And they said, “Hey, all of a sudden, you know, we might be able to use this GPU that’s incredibly computationally intensive, and it’s now, you know, compliant with IEEE.” I can take my software that I was writing, you know, previously on CPUs, and I can see about using the GPU for that. And which led us to create, put C on top of FP32, what’s called, we call Cg. The Cg path took us to eventually CUDA. CUDA, step by step by step we… Well, the putting CUDA on GeForce, that was a strategic decision that was very, very hard to do, because it cost the company enormous amounts of our profits, and we couldn’t afford it at the time. But we did it anyway because we wanted to be a computing company. A computing company has a computing architecture. A computing architecture has to be compatible across all of the chips that we build. Can you take me through that decision? So, putting CUDA on GeForce, could not afford to do? Can you explain that decision? Why boldly choose to do that anyway? Can you explain that decision?…

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