Comment by throwdbaaway
7 days ago
There are Qwen3.5 27B quants in the range of 4 bits per weight, which fits into 16G of VRAM. The quality is comparable to Sonnet 4.0 from summer 2025. Inference speed is very good with ik_llama.cpp, and still decent with mainline llama.cpp.
Can someone explain how a 27B model (quantized no less) ever be comparable to a model like Sonnet 4.0 which is likely in the mid to high hundreds of billions of parameters?
Is it really just more training data? I doubt it’s architecture improvements, or at the very least, I imagine any architecture improvements are marginal.
AFAIK post-training and distillation techniques advanced a lot in the past couple of years. SOTA big models get new frontier and within 6 months it trickles down to open models with 10x less parameters.
And mind the source pre-training data was not made/written for training LLMs, it's just random stuff from Internet, books, etc. So there's a LOT of completely useless an contradictory information. Better training texts are way better and you can just generate & curate from those huge frontier LLMs. This was shown in the TinyStories paper where GPT-4 generated children's stories could make models 3 orders of magnitude smaller achieve quite a lot.
This is why the big US labs complain China is "stealing" their work by distilling their models. Chinese labs save many billions in training with just a bunch of accounts. (I'm just stating what they say, not giving my opinion).
There's diminishing returns bigly when you increase parameter count.
The sweet spot isn't in the "hundreds of billions" range, it's much lower than that.
Anyways your perception of a model's "quality" is determined by careful post-training.
Interesting. I see papers where researchers will finetune models in the 7 to 12b range and even beat or be competitive with frontier models. I wish I knew how this was possible, or had more intuition on such things. If anyone has paper recommendations, I’d appreciate it.
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More parameters improves general knowledge a lot, but you have to quantize more in order to fit in a given amount of memory, which if taken to extremes leads to erratic behavior. For casual chat use even Q2 models can be compelling, agentic use requires more regularization thus less quantized parameters and lowering the total amount to compensate.
The short answer is that there are more things that matter than parameter count, and we are probably nowhere near the most efficient way to make these models. Also: the big AI labs have shown a few times that internally they have way more capable models
Considering the full fat Qwen3.5-plus is good, but barely Sonnet 4 good in my testing (but incredibly cheap!) I doubt the quantised versions are somehow as good if not better in practice.
I think it depends on work pattern.
Many do not give Sonnet or even Opus full reign where it really pushes ahead of over models.
If you're asking for tightly constrained single functions at a time it really doesn't make a huge difference.
I.e. the more vibe you do the better you need the model especially over long running and large contexts. Claude is heading and shoulders above everyone else in that setting.
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When you say Sonnet 4, do you mean literally 4, or 4.6?
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It doesn’t. I’m not sure it outperforms chatgpt 3
You are not being serrious, are you? even 1.5 years old Mistral and Meta models outperform ChatGPT 3.
3 not 3.5? I think I would even prefer the qwen3.5 0.8b over GPT 3.
With MoE models, if the complete weights for inactive experts almost fit in RAM you can set up mmap use and they will be streamed from disk when needed. There's obviously a slowdown but it is quite gradual, and even less relevant if you use fast storage.
any good packages you recommend for this?
Qwen3.5 35B A3B is much much faster and fits if you get a 3 bit version. How fast are you getting 27B to run?
On my M3 Air w/ 24GB of memory 27B is 2 tok/s but 35B A3B is 14-22 tok/s which is actually usable.
Using ik_llama.cpp to run a 27B 4bpw quant on a RTX 3090, I get 1312 tok/s PP and 40.7 tok/s TG at zero context, dropping to 1009 tok/s PP and 36.2 tok/s TG at 40960 context.
35B A3B is faster but didn't do too well in my limited testing.
with regular llama.cpp on a 3070ti I get 60tok/s TG with the 9B model, it's quite impressive.
The 27B is rated slightly higher for SWE-bench.
27B needs less memory and does better on benchmarks, but 35B-A3B seems to run roughly twice as fast.
Don't sleep on the 9B version either, I get much faster speeds and can't tell any difference in quality. On my 3070ti I get ~60tok/s with it, and half that with the 35B-A3B.
Say more please if you can. How/why is ik_llama.cpp faster then mainline, for the 27B dense? I'd like to be able to run 27B dense faster on a 24GB vram gpu, and also on an M2 max.
ik_llama.cpp was about 2x faster for CPU inference of Qwen3.5 versus mainline until yesterday. Mainline landed a PR that greatly increased speed for Qwen3.5, so now ik_llama.cpp is only 10% faster on token generation.