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AirLLM 70B inference with single 4GB GPU

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Re: AirLLM 70B inference with single 4GB GPU

#91
post #90
post #51

Earlier quoted context omitted.

At that point, how does this compare with simply running the model on the CPU?

if you take a model that requires 200GB of VRAM and you run it on the CPU, it requires 200GB of RAM instead. Still unfeasible on consumer hardware. With this approach you can easily do it on 12GB or less of either RAM of VRAM, at several seconds per token instead of tokens per seconds. Very unusable, but certainly interesting!

If token speed doesn't count, you can have 200GB "RAM" in swap space.

But I agree, what the OP does is a lot more efficient than this.

Re: AirLLM 70B inference with single 4GB GPU

#92

Earlier quoted context omitted.

it takes you almost 5 mins to write at most 1 word?

If you look at examples of complex code and based on a public NASA dataset: https://raw.githubusercontent.com/timm/ourmine/refs/heads/ma... that has 93 NASA software projects, you are looking at less than 9 LOC per engineer/day 9 LOC per engineer/day...yes its about 5 to 6 min per token. Do you do better?

I write more but I'm no NASA engineer
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