So it feels very fast.

But it does not seem to be better than Qwen 3.6 35B at coding. A bit worse, I think, though I will test it more.

If you have a machine that can fit a 35B model in VRAM, I would suggest testing Muse Glimmer with (from memory)

  Reasoning strength: low

Despite being a dense model, this is actually capable of solving code problems faster than the Qwen MoE, despite having only one fifth of the raw token performance.

I am calling this a suggestion for the audience because I don't have the will/resources to do this.

> Base / architecture: Qwen/Qwen3.6-35B-A3B (Qwen3_5MoeForCausalLM, 256 experts, ~3B active). The "3.8" in the name refers to the teacher, not the base.

Not endorsement, haven't run it myself, just found the link.

MTP is a trade-off, as it pushes some more of the model off the GPU.

I have managed to get usable quants of Laguna S2 and even DeepSeek V4 flash on this setup.

There is clearly some intelligence loss compared to similar sized dense models, but I feel like it stomps on the 9-12b models I could run fully on GPU

tbh, I have stopped using MoE in the name of speed, the dense (with more active parameters) makes a real difference in output quality

Go here: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF

See the right hand side panel, you see a whole palette of quantizations and their respective sizes. Should give you an idea. Note that these are not the only quantizations available.

I get 20 tokens/s on an M4 Max (larger GPU).

With Qwen3.8 27B I could not get anywhere near 32k context window, that made it very unusable for agentic coding, although it was very smart.

That's not true. For computers without unified memory architecture (which is the vast majority) VRAM capacity is the bottleneck for local models. In that case a dense model can deliver significantly more intelligence than an MoE model of the same size. And for a typical consumer/gamer Nvidia GPU, dense models are fast enough.

Most MoE architectures have a few experts that are always running; this, the router, KV, and whatever else you have space for can stay in fast VRAM; and the remaining experts can be offloaded.