Why is AutoRound being slept on so hard?

Reddit r/LocalLLaMA Tools
auto-round quantization awq gguf intel model-compression open-source

Summary

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.

Seriously, why is almost nobody talking about AutoRound here? I’ve been experimenting with it on Qwen3.6 27B lately (running an AMD setup), and the perplexity/accuracy retention at low bits absolutely blows standard AWQ or RTN out of the water. Especially for models with complex reasoning or long contexts, it seems like a total cheat code. Yet, if you look at Hugging Face, almost every major model cook is still dumping standard AWQ or basic GGUF scripts. Is it just a bad branding issue because Intel’s name is on the repo and people think it’s vendor-locked to Gaudi or Arc? (It’s literally just PyTorch, it runs fine anywhere). Or is the 15-minute calibration time too much of a UX hassle for the mass-uploaders? Now that AutoRound natively exports directly to standard GGUF (bypassing llama.cpp's convert_hf_to_gguf.py which usually throws a NotImplementedError), there’s basically no reason not to use it. Am I missing something here? Is there a hidden downside or regression in inference speed that I haven't noticed? Would love to hear from anyone else who's actually baking these quants.
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.