CUDA

“Because while CUDA development began in 2006, That was not a useful usable platform for six plus years at NVIDIA.”

“NVIDIA is a platform, in my mind, no doubt about it. CUDA and NVIDIA and general purpose computing on GPUs is a platform, so whatever, you know, all of the stew of powers that go into making that, that go into making Apple, Microsoft, you know, and the like, G…”

“You do any of this work, you cannot deploy it on anything but NVIDIA chips.”

“The whole CUDA investment. Not at first, but definitely now is predicated on being able to amortize that a thousand plus employees spend over the base of the three million developers and all the people who are buying the hardware to use what those developers c…”

“NVIDIA to this day, now this may be changing, we'll talk about this at the end of the episode, has never charged a dollar for CUDA”

“We cannot overstate the importance of this moment, not just for NVIDIA, but for, like, computer science, for technology, for business, for the world, for us staring at the screens of our phones all day every day. This was the big bang moment for artificial int…”

“This is 15 years of CUDA and the hardware underneath it and the libraries on top of it that Nvidia has built. To go recreate that and surpass it on your own is such an enormous, enormous bite to bite.”

“I searched LinkedIn for people who work at NVIDIA today and have the word CUDA in their title. There are 1100 employees dedicated specifically to the CUDA platform.”

“Those were big bets relative to the company's size at the time, but this bet is like an iPhone-sized bet.”

“And what these guys from Toronto did is they went out probably to their local Best Buy or equivalent in Canada. They bought two GeForce GTX-Five-Eighty's, which were the top-of-the-line cards at the time, and they wrote their algorithm, their convolutional neu…”

“If you look at the number of CUDA developers over time, it was released in 2006, It took four years to get the first 100,000 people. Then by twenty-sixteen, 13 years in, they got to a million developers. Then just two years later, they got to two million. So 1…”

“Today there are five hundred million CUDA capable GPUs for developers to target.”

“AMD doesn't have all this capacity reserved from TSMC, at least not for the 2.5 D packaging process for the high end GPUs. AMD doesn't have the developer ecosystem from CUDA.”

“There are literally a couple of hundred million, right? 250,000,300 million installed base of active CUDA GPUs being used in the world today, and they're all architecturally compatible.”

“CUDA is used for almost all fields of science. Everything from molecular dynamics to imaging, CT reconstruction to seismic processing to, you know, weather simulations, quantum chemistry, the list goes on, right?”

“GeForce Now was NVIDIA's first data center product. And our second data center product was remote graphics, putting our GPUs in, in the world's enterprise data centers, which then led us to our third product, which combined CUDA plus our GPU, which became a su…”

“And if you just looked at the pipeline of a programmable shader, it is a processor and is highly parallel, and it is massively threaded, and it is the only processor in the world that does that.”

“We are the only accelerator on the planet where every single accelerator is architecturally compatible with the others. None has ever existed.”

“They buy two NVIDIA GeForce GTX five eighties, which were NVIDIA's top of the line gaming cards at the time. The Toronto team rewrites their neural network algorithms in CUDA, NVIDIA's programming language. They train it on these two off the shelf GTX five eig…”