Meta AI Fellow and PyTorch creator Soumith Chintala explains why custom kernel optimizations do not offer durable defensibility for LLM inference startups.
Prediction Not checkable as stated
Chintala: George Hotz's TinyGrad requires major breakthroughs to match PyTorch
“There's no, like, I don't think, like, unless we have, like, great breakthroughs, like, George's vision is achievable, like, or, like, he should be thinking about a narrower problem, such as, I'm only gonna make this for, like, work for self-driving car con ne…”
Prediction Not checkable as stated
Chintala: Apple's MLX will fail server-side due to lack of differentiation
“If they end up expanding onto the server side, and they'll probably build something like PyTorch as well, right? Like, eventually, that'll where it will land. And I think there, they will kind of fail on the, like, lack of differentiation. Like, it wouldn't be…”
Insight
Chintala: Simple AI frameworks must accept long compile times
“You can write a very simple framework but then you also should be willing to eat the long compile times of, like, searching for that optimal performance at runtime.”
Opinion
Chintala: Nvidia's primary competitive moat is NVLink interconnect, not GPU silicon
“The mode that Nvidia has right now, I feel like, is that they're, they have the interconnect that no one else has. Like, AMD GPUs are pretty good. I'm sure there's very silicon that is not bad at all, but, like, the interconnect like, NVLink is uniquely awesom…”
Prediction Not checkable as stated
Chintala: LLM inference market will become a low-margin laundromat business
“My view of the LLM inference market in general is that it's like the laundromat model. Like you, the margins are going to drive down towards the bare minimum, like It's gonna be all kinds of arbitrage between how much you can get the hardware for, and then how…”
Insight
Chintala: Synthetic data only works where humans already have symbolic models
“Outside of this, like, where we don't have good symbolic models, like, synthetic data obviously, like, doesn't make any sense. So synthetic data is not a magic wand where it'll work in all cases, in every case, you know, whatever. It's just where we as humans …”