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34B Q4 will use around 20GB of memory.

If it's running slow, make sure metal is actually being used[0]. You can get as much as a 50-100% boost in tokens/s, if by chance it's not enabled.

I'm averaging 7 to 8 tokens/s on an M1 Max 10 core (24 GPU cores).

[0] if using llama-cpp-python (or text-generation-webui, ollama, etc) try:

`pip uninstall llama-cpp-python && CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install llama-cpp-python`

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Thank you. I had to reduce the context length to get this to work without crashing (from 16k to 8k)—and I'm seeing the ~100% speed up you mentioned.

However, when I run the LLM, OSX becomes sluggish. I assume this is because the GPU's utilized to the point where hardware-based rendering slows down due to insufficient resources.

I wonder if there's a way to avoid that slowdown?

I haven't noticed any slowdowns. Maybe check that threads/n_threads is set correctly for your machine (total cores - 2. 10 cores = 8, 8 cores = 6).

n_gpu_layers should also be set to anything other than 0 (default). I don't think the exact number matters for metal, but I use 128.

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