LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
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Wouldn't using the Vulkan backend instead of ROCm help a ton with concurrency and diffusion, at a marginal (1-2%) performance loss?
Vulkan helps with speed. Must benchmarks prove that out. Concurrency is a mixed bag. You can get some with llama.cpp bit vllm is concurrency king.
Just a couple of weeks ago llama.cpp released tensor parallelism which helps, but its still a experimental feature.
Unfortunately, I don't know of any diffusion runners that work in vulkan. If someone has expertise, let me know!
In my experience Vulkan actually drops performance ever so slightly, but it also improves compatibility (especially on the chips AMD advertises as "AI" then promptly forgets to support via ROCm, like the gfx1101/02/03 family, gfx1150, etc.), which is why I recommend it - as you said, and I've just noted, AMD is famous about doing as little in their AI toolkit for the average user as possible, leading to very limited support on consumer hardware.