There are two flavors of Qwen 3.6:

- A 27B "dense" model

- A 35B "Mixture of Experts" model, which activates only 3B parameters for each token.

For your hardware, I strongly recommend `unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_M`. I have an M1 Max with 32GB VRAM from 2021 that can read at ~300-500 tokens/sec and write at ~30 tokens/sec with llama-cpp's default settings, which is plenty fast. The 27B model can read ~70tok/sec and write ~5tok/sec.

The 35B MoE model technically takes slightly more memory but is much faster because it's doing 1/9th the work. It's not quite as "smart", but it's comparable.

> qwen3.6-27b Q6_k

That's the dense model, you probably want a mixture-of-experts (MoE) one.

Here's what you probably want instead: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF

27B is the dense one. Try the Qwen3.6-35B-A3B variants for the MoE release. That's what I'm running on a Framework Desktop and I get ~50 tok/s plus or minus a few. The dense one is similarly slow for me -- not sure what to expect on your hardware from the MoE but it should probably be much faster.

I'm running Qwen3.6:27b Q4 KM on a 4090 and similarly fast CPU and I think 32GB of RAM. Make sure the context window is set to be big enough otherwise the conversation will keep compacting. No special MCP tools set up yet. Qwen is able to do web search out-of-the-box although I think it is getting blocked by anti-bot firewalls--I still need to figure out if I can fix that.

here's a simple setup to get you started on an Apple M1 Max from 2021 with 32GB VRAM. it will download 20GB of models to `~/.cache/huggingface/hub`, which you can delete when you're done.

  /Users/gcr/llama.cpp/build/bin/llama-server
      -hf unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_M
      --no-mmproj-offload
      --fit on
      -c 65536 # edit to taste
      --reasoning on --chat-template-kwargs '{"preserve_thinking": true}'
      --sleep-idle-seconds 90 # very aggressive: purge model from vram after this long
      -ctk q8_0 -ctv q8_0 # Optional. Lower memory use, but lower speed. Omit if you can.

For agent harnesses, opencode is okay, as is pi or even Zed's built in agent panel. Claude code "works" with ANTHROPIC_BASE_URL=http://localhost:8080/v1, but is very chatty (the default system prompt burns 20k tokens). Crush (from the charm-bracelet folks) is particularly nice when starting out. I've personally converged on pi-agent under an otherwise-mostly-default setup. You can ask qwen to customize pi or write you an extension which helps a little.

You'll need to add `http://localhost:8080/v1` as an OpenAI-compatible model provider in your coding harness with any API key (doesn't matter) and any model identifier (doesn't matter with llama-cpp).

Note that pi doesn't have permissions. Everything is permitted. The hundred hungry ghosts you've trapped in a jar WILL find a way to delete your home folder someday. That's what Man gets for summoning demons without casting a circle of protection first. Flying too close to the sun etc etc etc

Take backups and then go have fun. Hope this helps.

Look for an open release from the Qwen team in the coming weeks. They like to showcase their proprietary models first, which score higher on benchmarks anyway due to model size.