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this post was submitted on 07 Feb 2024
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It's been discovered that you can reduce the bits per parameter down to 4 or 5 and still get good results. Just saw a paper this morning describing a technique to get down to 2.5 bits per parameter, even, and apparently it 's fine. We'll see if that works out in practice I guess
I'm more experienced with graphics than ML, but wouldn't that cause a significant increase in computation time, since those aren't native types for arithmetic? Maybe that's not a big problem?
If you have a link for the paper I'd like to check it out.
My understanding is that the bottleneck for the GPU is moving data into and out of it, not the processing of the data once it's in there. So if you can get the whole model crammed into VRAM it's still faster even if you have to do some extra work unpacking and repacking it during processing time.
The paper was posted on /r/localLLaMA.
You can take a look at exllama and llama.cpp source code on github if you want to see how it is implemented.