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On my 32GB Ryzen desktop (recently upgraded from 16GB before the RAM prices went up another +40%), did the same setup of llama.cpp (with Vulkan extra steps) and
by kpw94 8mo ago
On my 32GB Ryzen desktop (recently upgraded from 16GB before the RAM prices went up another +40%), did the same setup of llama.cpp (with Vulkan extra steps) and also converged on Qwen3-Coder-30B-A3B-Instruct (also Q4_K_M quantization)
On the model choice: I've tried latest gemma, ministral, and a bunch of others. But qwen was definitely the most impressive (and much faster inference thanks to MoE architecture), so can't wait to try Qwen3.5-35B-A3B if it fits.
I've no clue about which quantization to pick though ... I picked Q4_K_M at random, was your choice of quantization more educated?
- NamlchakKhandro 8mo ago[flagged]
- zargon 8mo agoQuant choice depends on your vram, use case, need for speed, etc. For coding I would not go below Q4_K_M (though for Q4, unsloth XL or ik_llama IQ quants are usually better at the same size). Preferably Q5 or even Q6.