this post was submitted on 14 Feb 2024
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Sorry but has anyone in this thread actually tried running local LLMs on CPU? You can easily run a 7B model at varying levels of quantization (ie. 5 bit quantization) and get a generalized prompt-able LLM. Yeah, of course it's going to take ~4GB of RAM (which is mem-mapped and paged into memory), but you can easily fine tune smaller more specific models (like the translation one mentioned above) and have surprising intelligence at a fraction of the resources.
Take, for example, phi-2 which performs as well as 13B param models but with 2.7B params. Yeah, that's still going to take 1.5GB RAM which Firefox wouldn't reasonably ship, but many lighter weight specialized tasks could easily use something like a fine tuned 0.3B model with quantization.
Yes, I did. And yes, it is possible. It's terribly slow in comparison, making it less useful. It very quickly devolves into random mumbling or get stuck in weird loops. It also hogs resources that are actually used by other tasks you may be doing.
I mainly test dev AI solutions, and moving from 1B to 7B models made them vastly more pertinent. And moving from CPU implementation (Ryzen 7 3700X) to GPU (RTX 3080 Ti) made them fast enough to be used as quick completion and immediate suggestion without breaking workflow, in addition to freeing resources for IDE, building tools and the actual software being run, while running it on CPU had multi-seconds delay, which made this use case completely useless.