Jeff Schmidt, co-founder of Nous Research, explains why long-standing technical constraints in AI model training can be challenged and overcome.
Insight
Schmidt: Relying solely on fine-tuning is an existential threat to open-source AI
“Because that for us, if we're just fine tuning models. Right. That's like an existential threat, right? That's like an actual existential threat because the closed providers will continue to get better and we would be like dead in the water in a lot of sense.”
Insight
Schmidt: Sharing only key signals in distributed training yields equivalent model learning
“We know that like what we, what needs to be communicated between these things, the two, the different nodes are just these few key pieces of information. And that is necessary. That is a necessary condition or rather a sufficient condition To get the equivalen…”
Prediction Not checkable as stated
Schmidt: Decentralized training will force NVIDIA to redesign chips around VRAM ratios
“What might happen sooner would be a redesign of the types of chips that NVIDIA or someone would make. Okay, under this model, we can dedicate more VRAM versus, there's like this question of how much VRAM versus how much processing power is on a die, and that, …”
Assertion Partly supported
Schmidt: Compute chips in NVIDIA's RTX 4090 and H100 are almost identical
“I think people don't actually realize that like a forty-ninety and like an H 100 are in a lot of ways the same card. For the non-gamers in the room, explain the forty-ninety. The chip that's inside of them is almost identical. The chip, the actual compute chip…”
Prediction Not checkable as stated
Schmidt: Consumer gaming GPUs will become the sweet spot for distributed training
“Because you're able to distribute it so wide, I think the gaming GPU angle is really going to be like the sweet spot. If, as long as there's continued to be sort of like higher end gaming GPUs, and those are on comparison with the high end training GPUs, even …”
Prediction Didn’t hold up
Schmidt: Decentralized training of 400B parameter AI models is solvable by 2025
“I think it still is, it would still be, you know, like a next year sort of environment thing that we would have to do. There are some scaling problems, or not scaling problems, but technical things about how you shard the model, because at that point you get t…”