Hoping this is real. It's too bad to see the signals from Qwen that they will not be releasing a 35B-A3B for the 3.8 lineup. The MoE architecture makes a huge difference for being able to run these local models on reasonable consumer hardware.
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.
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).
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.
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
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.
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.
tbh, I have stopped using MoE in the name of speed, the dense (with more active parameters) makes a real difference in output quality