AutoRound Quantization for LLMs

Jun 23, 2026

When an LLM is quantized, it becomes possible to fit it onto a consumer GPU. Quantization is an important step in getting an LLM to work on many GPUs. Recently I investigated the AutoRound quantization from Intel.

In AutoRound, a sophisticated algorithm is used to determine the quantized weights. It claims to be state of the art (SOTA) for quantization. In my ongoing quest to improve my local LLM, I downloaded an AutoRound quantization of Qwen 3.6 from Hugging Face.

In my testing:

The AutoRound quantization worked well and was consistent in its output.

I managed to make some worthwhile code changes in OpenCode with it.

I could not tell if it was better or worse than other same-bit quantizations in an objective sense, but I tended to prefer its output.

Also AutoRound was slightly smaller and faster than other quantizations. No quantization is going to be perfect, but AutoRound may be my new preferred quantization format (unless QAT is available, which is likely even better than AutoRound).

Dot Net Perls features a blog that covers code from a higher level. It focuses on programming—and whatever the author is thinking about.

Consider donating to help offset the costs of running the server. Sites like this cease to exist without financial support.

Sam Allen is passionate about computer languages, and he is responsible for the material here. He hopes it makes the world a nicer place.

Home