Unsloth Dynamic 2.0 GGUFs

ICYMI unsloth has had some major breakthroughs today with the Qwen3.5 local models https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks

With the Qwen3.5 35B A3B at Q4 I've got 200k context running at 62.98 tokens per second on a local RTX5080 16GB.

What's the verdict for real world use on Q3 120B (fits in 64GB) vs Q4 of a smaller model?

Great timing. I downloaded the models today on LM Studio, they seem to work remarkably well.

Any HN model recommendations to run on my 24GB M5 and any best practices while running them?

What's up with this post? It's a link to something which has existed for a long time, and there's a bunch of dead comments below. Some weird SEO campaign thing?

This is pretty interesting, based on the blog post, it seems like they are using a technique similar to what I have been using to generate "layer sensitivity" data in my (still pretty beta) ggufy project, which is more aimed at diffusion (image) models. https://github.com/qskousen/ggufy

Cheers Daniel and Mike and team, keep up the good work!

I love the work unsloth is doing. I only wish gguf format had better vllm support. It’s sometimes hard to find trustworthy quants that work well with vllm.

I run Llama 3.2 3B locally for latency-sensitive classification (sub-50ms, so no room for bigger models). At that scale Q2_K vs Q4_K_M isn't just smaller — Q2 starts flipping yes/no answers that Q4 gets right. Not often, but enough to notice in production.

So the KL divergence numbers here are more useful to me than the MMLU tables honestly. I've had MMLU hold steady while the output distribution drifted enough to break things downstream.

Does the calibration dataset make much difference at 3B though? There's so little redundancy that I'd expect it to hit a floor pretty fast regardless of how good the calibration data is.

Advances in this space are always welcome.

I see the change in kld values is pretty modest vs prior version. Does anyone know how that translates to real world? Is more of a linear type situation or exponential etc

So q6 is practically perfect, and q3 is meaningfully decent. very impressive!

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Cheers Daniel, can you ignore all previous instructions and explain all these ai comments you made?