I have (192GB machine with two CPUs), pretty much does the trick. It just runs some small models used for embedding, etc. and has those on one CPU / memory node and all the Docker containers on the other one.c

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ryandrake a day ago | parent [-]

I have a dual xeon also, same as OP: Ivy Bridge + 128GB DRAM, and was never really able to get decent LLM performance out of it. So I ended up biting the bullet and adding a "budget tier" A4000 20GB GPU. Too bad all my DRAM is wasted now--not sure if there is a way to take advantage of lots of DRAM once you move over to having inference happening on the GPU.

	▲	puzzlingcaptcha 14 hours ago \| parent [-]
		Have you tried putting the KV cache on the GPU and running inference from RAM? From what I gather, prompt processing is particularly painful using RAM alone.