> I think that having no support on current-gen flagship models does equal broadly terrible software support and is more worth highlighting, since otherwise someone might assume they could just go pick one of those up and get a 24GB GPU on the cheap, especially in the context of LLMs (which is what the OP was asking about).
Absolutely fair and I agree with this part. I started my reply with "FWIW" (For What It's Worth) on purpose.
> For RDNA2, you apparently can get LLMs running, but it requires forking/patching both bitsandbytes and GPTQ: https://rentry.org/eq3hg - and this will be true for any library (eg, can you use accelerate? deepspeed? fastgen? who knows, but certainly no one is testing it and AMD doesn't care if you're not on CDNA).
I haven't tried any of the GPU-based LLMs yet. SD leveraging PyTorch (which seems to have solid ROCm support) worked for me. It will not be faster than NVIDIA for sure but if someone already has a 16GB+ AMD card they may be able to at least play with stuff without needing to purchase an NVIDIA card instead.
Absolutely fair and I agree with this part. I started my reply with "FWIW" (For What It's Worth) on purpose.
> For RDNA2, you apparently can get LLMs running, but it requires forking/patching both bitsandbytes and GPTQ: https://rentry.org/eq3hg - and this will be true for any library (eg, can you use accelerate? deepspeed? fastgen? who knows, but certainly no one is testing it and AMD doesn't care if you're not on CDNA).
I haven't tried any of the GPU-based LLMs yet. SD leveraging PyTorch (which seems to have solid ROCm support) worked for me. It will not be faster than NVIDIA for sure but if someone already has a 16GB+ AMD card they may be able to at least play with stuff without needing to purchase an NVIDIA card instead.