I do like the idea of an aftermarket of ancient LLM chips that still have tons of useful life on text processing tasks etc. They don't talk about their architecture much, I wonder how well power can scale down. 200W for such a small model is not something I see happening in a laptop any time soon. Pretty hilarious implications for moat-building of the big providers too.
Yea I mean this is the first publishable draft of a startup cooking on this.
I'm confident there are at least 1-2 OOMs of improvement to come here in terms of the (intelligence : wattage) ratio.
I really thought we were going to need to see a couple of dramatic OOM-improvement changes to the model composition / software layer, in order to get models of Opus 3.7's capability running on our laptops.
This release tells me that eventual breakthrough won't even be strictly necessary, imo.
The way I imagine it in 2-4 years we're going to be hit with a triple glut of better architecture, massive oversupply of hardware and potentially one or two hardware efforts like this really taking off. It's pretty crazy we're already 4 years in and outside of very niche / low availability solutions, it's still either GPU or bust
That's interesting! How do you see "oversupply of hardware" playing out?
Is it because we stop doing ~2024-style, large-scale training (marginal returns aren't worth it)? Or because supply way outpaces the training+inference demand?
AFAIU if the trend lines /S-curves keep chugging along as they are, we won't hit hardware oversupply for a long, long time without some sort of AI training winter.
