Qwen 3.6 27B AutoRound GGUF, need your feedback

Reddit r/LocalLLaMA Models
qwen gguf autorumd model-quantization open-source

Summary

A user shares their GGUF quantized version of Qwen 3.6 27B using AutoRound, claiming it performs better than other quants, and invites feedback.

I have always been a fan of the AutoRound quants of this model, for some reason, it thinks less (sort of like Qwopus models) and comes up with solutions quicker than Unsloth quants for instance. [https://huggingface.co/sphaela/Qwen3.6-27B-AutoRound-GGUF](https://huggingface.co/sphaela/Qwen3.6-27B-AutoRound-GGUF) I am sharing this with everyone so you can give them a try, but honestly, don't hesitate, in all my tests they have always been reliable, I have even used the Q6 quant without MTP (before MTP quants were available) just because the Q6 was extremely precise in my C++ coding tasks
Original Article

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I compared GGUF quants of Qwen3.6 27B to NVFP4, AWQ, AutoRound, and FP8

Reddit r/LocalLLaMA

A detailed benchmark comparing 16 quantizations of Qwen3.6 27B across GGUF, NVFP4, AWQ, AutoRound, and FP8 formats, measuring KL divergence from the unquantized reference. Weight-only GGUF quants generally offer the best quality-size tradeoffs, while vLLM quants vary substantially.

Why is AutoRound being slept on so hard?

Reddit r/LocalLLaMA

A user questions why AutoRound, a quantization tool offering superior accuracy retention at low bits and direct GGUF export, is overlooked despite outperforming standard AWQ and RTN, especially on complex models like Qwen3.6 27B.