this post was submitted on 16 Oct 2024
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Basically the only thing that matters for LLM hosting is VRAM capacity. Hence AMD GPUs can be OK for LLM running, especially if a used 3090/P40 isn't an option for you. It works fine, and the 7900/6700 are like the only sanely priced 24GB/16GB cards out there.
I have a 3090, and it's still a giant pain with wayland, so much that I use my AMD IGP for display output and Nvidia still somehow breaks things. Hence I just do all my gaming in Windows TBH.
CPU doesn't matter for llm running, cheap out with a 12600K, 5600, 5700x3d or whatever. And the single-ccd x3d chips are still king for gaming AFAIK.
This only matters if you are running large models. If you stick with Mistral sized models you don't need nearly as much hardware.
I'll also add that some frameworks and backends still require CUDA. This is improving but before you go and buy an AMD card, make sure the things you want to run will actually run on it.
For example, bitsandbytes support for non-CUDA backends is still in alpha stage. https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend
For local LLM hosting, basically you want exllama, llama.cpp (and derivatives) and vllm, and rocm support for all of them is just fine. It's absolutely worth having a 24GB AMD card over a 16GB Nvidia one, if that's the choice.
The big sticking point I'm not sure about is flash attention for exllama/vllm, but I believe the triton branch of flash attention works fine with AMD GPUs now.
From what I have seen CUDA is still first supported.
VRAM and RAM I think. Still AMD seems always slower than Nvidia for some reason for this purpose. Same for Blender benchmarks.
Ah I use my AMD GPU with Bazzite and it is wonderful.
CPU does not matter when GPU matters. Otherwise small models will do fine on CPU especially with more recent instructions for running LLMs.
I am not a fan of CPU offloading because I like long context, 32K+. And that absolutely chugs if you even offload a layer or two.
Ah I see