Have you tried with a single CPU to get rid of the NUMA penalty? I understand this likely means halving the memory but I am interested in how much of a difference it makes
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
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.
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.
I haven't, since the machine has almost no GPU to speak of. It's an old machine, although in theory it would make sense to pop an R9700 in there and load a bunch of models into RAM and swap them in and out.