# AutoRound Quantization for LLMs

> Source: <https://www.dotnetperls.com/2026_6_23_autoround-quantization-for-llms>
> Published: 2026-06-23 07:00:00+00:00

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).