Modal

“I think eventually like for any company where model quality really matters, unless you kind of train your own model in the end, like, I feel like it's going to be hard to sort of defend the fact that like, You know, you're, you have a better solution. Cause li…”

“So, so one example of a customer I think is super cool building really amazing stuff is Suno. Which does AI generated music. So they run all their inference on modal very large scale.”

“The main thing that really started driving all the traction was when stable diffusion came out. And a bunch of people came to us and like, Hey, actually, this looks kind of cool. Like you have GPU access. It's very easy to, you know, you have to think about, y…”

“I think there's so many benefits of this multi-tenant model in terms of capacity management that to me, it is very clearly like a big part of the future of AI is like running a big pool of compute and slicing it very dynamically.”

“We run a very big compute pool, like thousands of GPUs and CPUs, and we make it very easy to get, you know, if you need a hundred GPUs, we can typically get you that within seconds.”

“And that's actually a field where we start to see a lot more usage as model as well. Is there's a lot of, I feel like there's like a kind of a resurgence of computational biology.”

“We can't really use Docker and Kubernetes. So we're going to have to throw that out and probably going to have to build our own file system, which we did pretty early in a build our own scheduler and build our own container runtime. So that was like basically …”

“Traditionally, most of modal has always been inference. Like that's been our main use case, but we're really interested also in training. So in particular, like probably focused more on these like shorter, like very bursty sort of experimental training runs, n…”