Same here! Qwen3.6-35B-A3B is the only local model I've found that runs reasonably on my iGPU. Looks like me and and my noisily-wheezing laptop will be sitting out this upgrade.
MoE is literally 5x times faster (on CPU) than comparable dense Qwen!
Tested:
Qwen3.6-35B-A3B-UD-Q4_K_XL
Qwen3.8-27B-UD-Q4_K_XL
27 dense is far more capable than 35A3.
on the 395+ AI MAX w/128GB, the A10B qwen 3.5 can do a lot of long running work if you don't need to baby sit it. deer-flow works well like that.
llama serve -hf unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL
but it failed my basic prompt to compose a vim regex to match CamelCaseWords
downgraded a bit with Q4_K_M from ollama run qwen3.8:27b
and /set nothink and at least 1 regex matched FooBar
prompt eval at 2.8 t/s eval at 0.94t/s
I particularly enjoyed this usage of the regex: /%\1\%/ ... onward for 700+ characters of \%\/ :-)
I'm using a Q4 quantization from unsloth (Qwen3.6-35B-A3B-UD-Q4_K_XL). It gets up to ~20 tokens/second in generation. I don't know precisely how much KV cache I can safely use, but it's in between 140k–256k. (I.e., 140k reliably works, 256k kernel-crashes from OOM. Don't feel like bisecting).
Inference is llama.cpp with the Vulkan GPU backend on Linux. (I.e., -DGGML_VULKAN=1 on the llama.cpp build, and --gpu-layers all on llama-cli or llama-server. (And for my specific setup, two kernel parameters specific to amdgpu: ttm.pages_limit and ttm.page_pool_size. A driver VRAM limiter. Look it up if you're on amdgpu!)).
I assume it would not be able to handle an unquantized Qwen3.6-35B or is it irrelevant as you almost always would want to run a quantized version of the model on consumer hardware?