So what is currently the best and easiest way to use an AMD GPU for reference I own a rx6700xt and wanted to run 13B model maybe superhot but I’m not sure if my vram is enough for that Since now I always sticked with llamacpp since it’s quiet easy to setup Does anyone have any suggestion?
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I’ve a rx 6650 xt and I generally use llama.cpp with the ROCm patch (tested up to commit ac7876ac20124a15a44fd6317721ff1aa2538806).
It works great with around 25 layers moved to the GPU for my 8GB card. 18, if you want to do something else GPU related (like watching a HW Accelerated video).
To be fair it’s a long time now that I don’t update llama.cpp and it had gone through a lot of changes in the meantime, like the addition of the
LLAMA_CUDA_DMMV_X,LLAMA_CUDA_DMMV_YandLLAMA_CUDA_KQUANTS_ITERparameters, so your mileage may vary and it’s possible you’ll have to manually modify the PR before merging it in, so not really an easy one click experience for the best performance.It currently doesn’t support SuperHot or similar techniques, mainly because there’s a really big push on new ones each day, and they are waiting to see which will be the real winner.
But I went a bit too much off-topic. I think the easiest, as the other commenter said, is to just go with kobold.cpp. I personally didn’t have a good experience working with text-generation-webui, but a lot of people swear by it.
Just pay nvidia their ill-earned ounce of flesh. I say this as a strong AMD advocate.
It’s clear that AMD isn’t serious about the AI market. They had years to provide a proper competitor to CUDA or at the very least a 1:1 compatibility layer. Instead of doing either of these things, AMD continued messing with half-assed projects like ROCm and the other one the name of which I don’t care to look up. AMD has the resources to build a CUDA compatible API in under 6 months but for some reason they don’t. I don’t know why they don’t, and at this point I don’t really care.
Buy an AMD GPU for AI at your own risk.
