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Posted by stared 19 hours ago

Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses(quesma.com)
252 points | 124 commentspage 2
qsbuilder 10 hours ago||
I always wonder is it safe to run one of these models on a personal pc, or do you guys recommend something like docker, sorry a bit new to all of this.
bitwize 10 hours ago|
Yes, it's fine to run a model on bare metal. The model is just a token predictor. Leave out the fine semantics about this; it's a function taking a set of input tokens to output tokens.

It can't mess with your computer or files until you hook it to a harness, which interprets some of the model's output as commands to execute. So, model on bare metal, harness in a container or VM.

kouteiheika 17 hours ago||
Note that these quants are not quantized uniformly, so 4-bit isn't actually a "true" 4-bit here, so these observations won't necessarily hold up to other quants which might be done differently.
wgd 17 hours ago|
It looks like they tested Q4_K_M which should be just the standard K-quant without any imatrix calibration. The smaller ones are indeed dynamic though.
mrbonner 16 hours ago||
I use a 2-bit quant from Unsloth on my MBP M5 32GB of RAM. It run slower than molasses at 2 too/s kind of thing. Not sure it is usable at that rate for anything.
dvh 18 hours ago||
Could this be used to estimate how many fingers LLM have?
nozzlegear 14 hours ago||
1-bit, 2-bit, penny and dime.
KennyBlanken 15 hours ago||
It's strange that the author has completely ignored the 3 bit quants which allow someone with a 16GB GPU to have 100-120k and still get full performance. You can't run any of the 4-bit quants on a 16GB gpu with enough context to be useful for all but the most basic tasks.

General purpose agents can need up to 30k just to reply with "1+1=2" because their prompting is so overloaded. 60-70k is decently usable, still not great for anything complex. A long running task in a general purpose agent can easily hit 100k.

What the vast majority of people care about is performance around what desktop consumer GPUs can run. 8GB, 10, 12, and 16GB of VRAM. What do models that will run at full performance, do?

Also important to know is how Qwen3.8-27B stacks up against qwen3.6-35B-A3B, which due to being MoE, will run on a 16GB card with plenty of speed 90% of the time, at higher quant - so you get more parameters and better quant. But 3.8 is supposed to be "better", so...?

rvba 17 hours ago||
Those benchmarks are very interesting.

But is there any model that actually works in a decent way at quantization of 1?

bellowsgulch 18 hours ago|
Qwen3.8 27B seems like it was clearly supposed to be a high-end consumer open-weights model, but the t/s is so low for me on my old M1 Max 64GB that I hope others are getting use out of it.

Unfortunately, the calculus has changed and it seems cheaper to me to just use MiMo V2.5 for pennies or DeepSeek V4 Flash instead of using Qwen anymore unless I need a local model specifically for doing reverse engineering work that gets otherwise rejected.

spider-mario 18 hours ago||
> Qwen3.8 27B seems like it was clearly supposed to be a high-end consumer open-weights model, but the t/s is so low for me on my old M1 Max 64GB that I hope others are getting use out of it.

Have you tried it with MTPLX? I get around 30 tok/s with it, also on an M1 Max with 64GB.

SwellJoe 18 hours ago|||
Even at 30 t/s, 3.8 thinks so long, even on medium, it still takes 3x or more longer than any cloud model, in my testing.
lowbloodsugar 16 hours ago||
I've got an M1 Max 64GB too. It's just not an LLM-class workstation. Give it a year and buy an M7 and you'll be laughing. Right now is a really bad time to invest in anything - using the cloud is the cheapest option, especially for open weight models.
Xeoncross 18 hours ago||||
Nice, which model quantization is this? Is it on huggingface?
spider-mario 16 hours ago||
MTPLX is this software: https://github.com/youssofal/MTPLX

I tried it with the author’s 4-bit quant of Qwen 3.8 27B: https://huggingface.co/Youssofal/Qwen3.8-27B-MTPLX-Optimized... (but no need to download it manually; MTPLX will ask which one you want).

bellowsgulch 17 hours ago|||
Thanks, man! I’ll go use that now that I know. llama-server the last time I used it for inference with this model wasn’t able to produce work fast enough to reach those numbers.
Xeoncross 18 hours ago|||
I leave it running at night. No danger of burning my token subscriptions and it has hours and hours to run slowly with a manager like: github.com/kunchenguid/gnhf
sroussey 18 hours ago|||
Have you tried https://huggingface.co/prism-ml/Bonsai-27B-mlx-1bit ? PrismML is the only people i am aware of doing 1bit that is decent.
sidewndr46 16 hours ago|||
I've ran some agentic stuff with Qwen3.8-27B-UD-Q4_K_M on my RTX 3090. It's fast enough to be usable in my opinion. But Qwen3.6-35B-A3B in the same quantization is much faster
ThrowawayTestr 18 hours ago||
I treat it like image gen. Send a prompt then come back in 40 minutes.
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