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Qwen 3.8 27B
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Re: Qwen 3.8 27B
#782Can anyone who has that specific personal test he tries on different models , and tries this model , to tell us here if possible , how good or bad is this new model ? compared to others ? I only trust those users genuine personal tests
There is a down to earth guy on YT that performs a series of tests against LLMs running on non-god-tier commodity hardware. He will likely be testing this soon enough. https://www.youtube.com/@lukesdevlab I don't know if that is what you are looking for or not and as always your experiences may be different.
Replying since I can't edit - he tested it here: https://www.youtube.com/watch?v=1EzVVj7DFPc
Re: Qwen 3.8 27B
#783Earlier quoted context omitted.
Since the 3060s would have to communicate constantly using pcie, I think I'd go with intel
Currently software is king when it comes to inference performance though.
Having the entire model in the vram without having to transfer anything is even more king, though
Re: Qwen 3.8 27B
#784Earlier quoted context omitted.
I'm still confused about Qwen 3.6 35B A3B. Everything I read said that the 27B model performs better at coding tasks, so what's the purpose of the 35B model?
Runs faster on bad hardware.
Or even on fairly high end (by general public standards, not gaming community standards) consumer hardware. Fantastic fit for M-series Macs with 32-96GB RAM.
Re: Qwen 3.8 27B
#785Earlier quoted context omitted.
In my experience with Qwen3.6 35B-A3B, disabling thinking made the model generate inaccurate replies. Ask it for the recipe of egg salad and it gives you the recipe of an omelette. Did I miss something, is it possible to have that model be reliable without thinking?
The official docs recommend different sampler options for thinking off. Have you tried adjusting those?
Frontier model labs don't know anything about good sampling settings, and why should they? Their belief is that "sampling doesn't matter, we can simply scale our model and improve logprobs quality that way"
Ignore whatever nonsense qwen says about what settings to use. Turn off top-p and top-k. Turn on top-n-sigma and keep it at its defaults.
See repetitions that you don't like? Ignore the "repetition, frequency, presence" penalties and turn on DRY (don't repeat yourself).
Want more creativity? Temperature is okay but XTC (eXclude Top Choices) is better.
Llama.cpp quietly has had far advanced samplers compared to the shit stuff Qwen et al recommends for years.
Re: Qwen 3.8 27B
#786Earlier quoted context omitted.
The demand curve for speed and intelligence seems pretty steep to me. If you look at the hiring marketplace, being just marginally better than your peers can be very lucrative. If you’re competing on speed or capability as a company (or as an employee), you’re probably going to be willing to pay for the frontier.
> If you look at the hiring marketplace, being just marginally better than your peers can be very lucrative. I would say that in software this is completely false. Someone straight out of college, not very useful, makes 75-100K. Top level senior outside of FAANG is making twice that at best (and at least 10x more capable).
You told on yourself about being either European or from a flyover state.
Re: Qwen 3.8 27B
#787Absolutely the best pelican I've seen from a model that runs on my laptop: https://tools.simonwillison.net/markdown-svg-renderer#url=ht... Bicycle is the right shape. Pelican beak is excellent. Nice background. Most importantly, the pelican has one leg on each side of the bicycle - that's very rare. (No chain on this bicycle though - in the reasoning trace it says "already chainstay... skip chain detail; maybe a smal…
If I'm doing the math right, that's like 17 t/s? I haven't played with Qwen 3.8 yet, but that seems really slow for a 27B on an M5 Max with sufficient RAM to hold it in memory.
Re: Qwen 3.8 27B
#788[deleted]
Re: Qwen 3.8 27B
#789Re: Qwen 3.8 27B
#790Credit where it's due. Qwen 3.8 27B is only the second local model after Gemma 4 that managed to correctly reason through one of my private benchmarks. It took 5x as many tokens to do it and 12m30s with MTP enabled, but it did do it. Gemma 4 reasoned through it more implicitly, while Qwen 3.8 reasoned more explicitly. Laguna and Muse Glimmer failed hard on it, though they're useful for other tasks. The VRAM usage see…
"correctly reason through one of my private benchmarks"
i would also like to make one myself for my testing. could you give a rough idea or an outline or point in the general direction on what to do?