this post was submitted on 05 Feb 2024
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PSA: give open-source LLMs a try folks. If you're on Linux or macOS, ollama makes it incredibly easy to try most of the popular open-source LLMs like Mistral 7B, Mixtral 8x7B, CodeLlama etc... Obviously it's faster if you have a CUDA/ROCm-capable GPU, but it still works in CPU-mode too (albeit slow if the model is huge) provided you have enough RAM.
You can combine that with a UI like ollama-webui or a text-based UI like oterm.
ROCm? Is that even supported now? Last time I checked it was still a dumpster fire. What are the RAM and VRAM reqs for the Mixtral8x7b?
ROCm is decent right now, I can do deep learning stuff and CUDA programming with it with an AMD APU. However, ollama doesn't work out-of-the-box yet with APUs, but users seem to say that it works with dedicated AMD GPUs.
As for Mixtral8x7b, ~~I couldn't run it on a system with 32GB of RAM and an RTX 2070S with 8GB of VRAM, I'll probably try with another system soon~~ [EDIT: I actually got the default version (mixtral:instruct) running with 32GB of RAM and 8GB of VRAM (RTX 2070S).] That same system also runs CodeLlama-34B fine.
So far I'm happy with Mistral 7b, it's extremely fast on my RTX 2070S, and it's not really slow when running in CPU-mode on an AMD Ryzen 7. Its speed is okayish (~1 token/sec) when I try it in CPU-mode on an old Thinkpad T480 with an 8th gen i5 CPU.
I have a ryzen apu, so I was curious. I tried yesterday to fiddle with it, and managed to up the "vram" to 16gb. But installing xformers and flash-attention for LLM support on igpus is not officially supported and was not possible to install anything past pytorch. It's step further for sure, but still needs lots of work.