You now have me testing it with reasoning turned off, which I have never bothered much with on any other local models because it's rarely worth it.
The result appears to be almost as good as Qwen 3.6 35B A3B on medium thinking mode.
It second-guesses a little, it gives broader/more speculative answers, of course, and it missed the nuance of one of my prompts, but this gives me a lot more confidence that the Low reasoning effort is going to be as good as they say, and perhaps in some cases non-thinking looks like it would be enough.
Hm — brain jumped tracks a bit there at nearly 4am.
I'm talking about budget — I mean limiting it to 2048 tokens.
… for one or other of the models I tested at the same time, in llama-server, there is a dropdown that offered options (unlimited, max, medium which was 2048)
(I've tested so many of these things now that they are beginning to blur.)
I thought that was llama-server with Qwen 35B, just checked and it's not. Nor is it Gemma 4 26B. Perhaps it was Ternary Bonsai which I tested again and deleted earlier.
Anyway I took to clipping Qwen 3.6 35B at 2048 tokens reasoning in LM Studio and elsewhere, and it did OK at that (because it often loops like mad on an ambiguous prompt if not curtailed).
FWIW I just rechecked outputs and I am a bit over-optimistic when I say 3.8 27B 's non-thinking output is that good. I spotted a couple of subtle errors in my tests that Low thinking didn't fail on. It is good, but it is not quite Qwen 3.6 35B thinking level.
It varies widely based on a bunch of factors. With this specific model at 8-bit quantization and MTP, it starts out at about 25 t/s for basic chat, but for agentic tasks with long context it slows way down to something like 12-15. I don't see a big difference in token rates based on any config changes I have tried, or going to the smaller 6-bit quantization, so far, though I haven't spent a ton of time on experimenting.
If you already have one or more of them, then, yeah, you can use them for this model or any other at around this size or smaller, but I wouldn't recommend you buy them (or anything else, right now, everything is just too overpriced). You can run better models for less money at higher speeds. I bought mine before they got more expensive, but I wish I'd just bit the bullet and bought newer/faster cards before they got more overpriced. Or, the actual smart money, even back then was to just use cloud models and forget about self-hosting.
You now have me testing it with reasoning turned off, which I have never bothered much with on any other local models because it's rarely worth it.
The result appears to be almost as good as Qwen 3.6 35B A3B on medium thinking mode.
It second-guesses a little, it gives broader/more speculative answers, of course, and it missed the nuance of one of my prompts, but this gives me a lot more confidence that the Low reasoning effort is going to be as good as they say, and perhaps in some cases non-thinking looks like it would be enough.
Really useful, thanks.
> Qwen 3.6 35B A3B on medium thinking mode
Qwen 3.6 doesn’t have configurable reasoning effort, does it?
Hm — brain jumped tracks a bit there at nearly 4am.
I'm talking about budget — I mean limiting it to 2048 tokens.
… for one or other of the models I tested at the same time, in llama-server, there is a dropdown that offered options (unlimited, max, medium which was 2048)
(I've tested so many of these things now that they are beginning to blur.)
I thought that was llama-server with Qwen 35B, just checked and it's not. Nor is it Gemma 4 26B. Perhaps it was Ternary Bonsai which I tested again and deleted earlier.
Anyway I took to clipping Qwen 3.6 35B at 2048 tokens reasoning in LM Studio and elsewhere, and it did OK at that (because it often loops like mad on an ambiguous prompt if not curtailed).
FWIW I just rechecked outputs and I am a bit over-optimistic when I say 3.8 27B 's non-thinking output is that good. I spotted a couple of subtle errors in my tests that Low thinking didn't fail on. It is good, but it is not quite Qwen 3.6 35B thinking level.
Yes, default everything, no tuning, 8_K_XL Unsloth quantization on dual Radeon V620 GPUs (which aren't blazing, but faster than the Strix Halo).
What speed do you get on this setup? Im tempted to use the same GPU.
It varies widely based on a bunch of factors. With this specific model at 8-bit quantization and MTP, it starts out at about 25 t/s for basic chat, but for agentic tasks with long context it slows way down to something like 12-15. I don't see a big difference in token rates based on any config changes I have tried, or going to the smaller 6-bit quantization, so far, though I haven't spent a ton of time on experimenting.
If you already have one or more of them, then, yeah, you can use them for this model or any other at around this size or smaller, but I wouldn't recommend you buy them (or anything else, right now, everything is just too overpriced). You can run better models for less money at higher speeds. I bought mine before they got more expensive, but I wish I'd just bit the bullet and bought newer/faster cards before they got more overpriced. Or, the actual smart money, even back then was to just use cloud models and forget about self-hosting.