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That's AMD's fault. RDNA4 is pretty similar to CDNA4, yet over a year after the release of "pro AI" cards like the r9700, they had basic kernels lacking in vll
by trouve_search 2mo ago
That's AMD's fault.
RDNA4 is pretty similar to CDNA4, yet over a year after the release of "pro AI" cards like the r9700, they had basic kernels lacking in vllm (like w4a16 int4 kernels) while they were implemented in the datacenter CDNA4 cards.
AMD hardware runs well on llama.cpp because basically anything runs on llama.cpp, especially with vulkan. It's not high praise of AMD's software team to say llama.cpp runs well on their hardware
- npodbielski 2mo agoAs you said: everything works on llama.cpp Why it does not work on vllm? Of course you can say that it is AMD fault but there was an issue of abysmal performance of models on Strix Halo, that is open for half a year (https://github.com/vllm-project/vllm/issues/34579#issuecomment-5129108179 https://github.com/vllm-project/vllm/issues/34579#issuecomme...) and nothing is happening there. They do not care about those use cases. Seems like they are going with bit players that will run vllm inside datacenters racks. Hobbyists does not matter.