GPT-4 is incredibly useful, and it's clear there's so much straightforward bits of additional product development on top of it in various verticals that will be incredible productivity multipliers in the respective verticals.
GPT-4 is incredibly useful, and it's clear there's so much straightforward bits of additional product development on top of it in various verticals that will be incredible productivity multipliers in the respective verticals.
The best I can run is either LLaMa 65B at 1 token per second (too slow) on my CPU or LLaMa 30B on my GPU at quite a fast ~30 tokens a second.
Nowhere near the usefulness of GPT-4.
I assume there will be advances that basically make the context window practically infinite. That ought to make the lower parameter count models much more powerful on its own. I also assume they will become more efficient to run through sparsity/pruning, though I'm a total novice on this topic.
I wonder how many generations of hardware are we talking? One? Two? Or three? It feels like it's potentially within that range.
Emphasis on running, not training.
I Would be surprised if that is the case.
This isnt going to be the moneymaker people think it will be..
I'm a programmer, and I pay a subscription for GPT-4 because it's ridiculously worth it.
I'm curious, what do you do that you don't think GPT-4 is worth it?
What do you do for a living, and do you find GPT-4 helpful for it?