Jeff Schmidt, co-founder of Nous Research, explains why open-source AI groups must develop decentralized pre-training capabilities rather than just fine-tuning existing models.
Insight
Schmidt: AI industry relies on outdated 1990s architecture assumptions ripe for disruption
“It turns out that most assumptions in the AI space right now are a product of that's just how things had been done when there was not nearly as much energy and attention to it. So someone made an assumption maybe in the early nineties that everyone just kind o…”
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…”