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montroseryesterday at 5:39 PM10 repliesview on HN

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.


Replies

parsimo2010yesterday at 5:59 PM

Honest question/suggestion for the HN audience- Since Qwen released the weights for Qwen3.8 2.4T-A95B and we already have the staring point of Qwen3.6 35B-A3B, couldn't someone distill the bigger model and make a "pseudo" Qwen3.8 35B-A3B? Sure, it wouldn't be an official Qwen release but couldn't someone improve on Qwen 3.6 and get the thing everyone is asking for?

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

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smcleodyesterday at 8:35 PM

The smarter 27B is so fast with MTP I've found I really don't need the 35B-A3B. You get around 70tk/s on a M5 Max lowering to around 40tk/s at higher context sizes.

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dofmyesterday at 6:55 PM

Quick tests suggest it’s pretty good at reasoning and tool use (keen to search to check its thinking) and it seems to waste much less time thinking, too.

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
in the system prompt.

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.

mirekrusinyesterday at 6:37 PM

Personally I find speculative decoding much better strategy than MoE – performance wise it's there at 90-100 t/s on 2x4090, great intelligence – really great fit.

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Sha1rholderyesterday at 11:34 PM

> The MoE architecture makes a huge difference for being able to run these local models on reasonable consumer hardware

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.

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huseyinkelesyesterday at 10:39 PM

I've been experimenting with it on a M4 Pro 24G for the last few hours and it's been very promising using 32k context. getting around 30-40 tps

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.

regexorcisttoday at 3:07 AM

What signals? Looking more like a tiered release.

hgoelyesterday at 9:33 PM

The way it was said made it sound like they had something better than a 35B-A3B coming.

vkakuyesterday at 10:46 PM

This is all real. More real things coming soon.

verdvermyesterday at 7:09 PM

I'm running qwen3.8 27B dense on reasonable hardware (oem spark)

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