CUDA, every mention

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Verbatim, from the transcripts: the passages where CUDA comes up

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▶ 36:09 Jensen Huang We invented CUDA, invented GPUs, and we invented the idea of co-design at a very large scale.
▶ 43:37 Jensen Huang If not for the fact that CUDA is easy to operate on and iterate on, how do they try all of their vast number of experiments to decide which one of the Transformer versions, what kind of 2 times in the scene
▶ 1:02:51 Jensen Huang Sriram is the only person in Washington DC that I think knows CUDA, um, and, and, which is strange anyways, but, but I, I just love the fact

▶ 39:15 Brad Gerstner It keeps them in that CUDA ecosystem, allows NVIDIA to compete, and I think slows down their ability to run the table around the rest of the world.

▶ 29:59 Brad Gerstner Are we better off selling to those countries, those companies, keeping companies like ByteDance and Tencent, et cetera, in the CUDA ecosystem, rather than allowing all of that data, all of those profits to flow right into the Huawei…

▶ 26:25 Brad Gerstner They no longer have to compete with CUDA, uh, and, and Nvidia in China, even though we were sending them the H-Twenty was a nerfed chip, right?
▶ 28:54 Brad Gerstner I think that the competition with CUDA in China distracted

▶ 1:00:12 Brad Gerstner Remember the way CUDA works, the software that runs these GPUs, it constantly gets upgraded.

▶ 48:10 Brad Gerstner So they have from synthetic biology to robotics, um, to physics, to, you know, all the stuff we see in consumer to enterprise IT, they have this incredible stack across CUDA, which is multiple layers of software.

▶ 6:45 Bill Gurley One, it's, it's validated now that they went around CUDA, and I just think that's interesting.

▶ 15:10 Bill Gurley Just to, to clarify one thing you said, or at least restate it to make sure I, I think when you said the software is more important for, for training, you meant CUDA is more of a differentiator on training than it is on inference. 3 times in the scene

▶ 5:37 Sunny Madra Like what was great to hear from him and really, you know, positive for, you know, folks thinking about where Nvidia lives in the stack right now is he kind of got into details and then the sub details below CUDA. 10 times in the scene
▶ 15:50 Bill Gurley That's where CUDA really comes alive in the biggest systems that are out there.
▶ 17:46 Brad Gerstner To me, you know, when you think about that, in the first instance, right, I think it didn't make a lot of sense, but then when you really started thinking about it, he said there's a trail of infra behind the infrastructure that's already… 4 times in the scene

▶ 11:21 Jensen Huang So that entire thing is CUDA accelerated.
▶ 17:25 Jensen Huang There's a trail of free infrastructure behind the new infrastructure that's CUDA compatible. 2 times in the scene
▶ 28:42 Jensen Huang If you just support a CUDA, you know you could go everywhere.

▶ 1:18:28 Brad Gerstner Now expanding on the CUDA stack with a totally new inference, uh, software cluster called NIMS.

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