Putting CUDA on GeForce nearly sank NVIDIA, but install base won

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 11:31

   and it expanded the aperture of applications that we can accelerate with our accelerator. The question is, how do we attract developers to CUDA? because a computing platform is all about developers and developers don't come to a computing platform just because you know it could perform something interesting they come to a computing platform because the install base is large because a developer like anybody else wants to develop software that reaches a lot of people so the install base is in fact the single most important part of an architecture. The architecture could attract enormous amounts of criticism.

2. 12:17

   For example, no architecture has ever attracted more criticism than the x86, you know, as a less than elegant architecture. But yet, it is the defining architecture of today. It gives you an example that in fact so many risk architectures which were beautifully architected incredibly well designed by some of the brightest computer scientists in the world largely failed and so i've given you two examples where one is you know one is elegant the other one's barely aesthetic and so yet x86 survived install base is everything install base defines an architecture not everything else is secondary okay and so there were other architectures at the time CUDA came out OpenCL was here there were you know there's several other competing architectures but the the thing that the decision that we made that was good was we said hey look

3. 13:15

   ultimately it's about um install base and what is the best way we could get a new computing architecture into the world by that time frame GeForce had become successful we were already selling millions and millions of GeForce GPUs a year. And we said, you know, we ought to put CUDA on GeForce and put it into every single PC, whether customers use it or not. And use it as a starting point of cultivating our install base.

4. 13:46

   Meanwhile, we'll go and attract developers and went to universities and wrote books and taught classes and put CUDA everywhere. And eventually, people would discover, and at the time, the PC was the primary computing vehicle. There was no cloud, and we could put a supercomputer in the hands of every researcher in school, every scientist, every engineer in school, or every student in school, and eventually, something amazing will happen.

5. 14:13

   Well, the problem was CUDA increased our cost of that GPU, which is a consumer product, so tremendously, it completely consumed all of the company's gross profit dollars. And so at the time, the company was probably worth, I don't know, at the time, $8 billion or something, $6-7 billion or something like that. after we launched cuda i recognized that it was going to add so much cost but it was something we believed in you know our market cap went down to like one and a half billion dollars and so we were down we were down there for a while and and we clawed our way back slowly but we carried cuda on g-force i always say that nvidia is the house that g-force built because it was g-force that took CUDA out to everybody. Researchers, scientists, they discovered CUDA on GeForce because they were all, you know, many of them were gamers. Many of them built their own PCs

6. 15:20

   anyways. In a university lab, many of them built clusters themselves, you know, using PC components. And so that, you know, that's kind of how we got going. And then that became the platform, the foundation for the deep learning revolution. That was also another great, great observation. Yeah.