# Introducing AutoRound: Intel's Advanced Quantization for LLMs and VLMs

DevFeed: [Introducing AutoRound: Intel's Advanced Quantization for LLMs and VLMs](<https://devfeed.tech/articles/introducing-autoround-intel-s-advanced-quantization-for-llms-and-vlms-7113.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/autoround>)

Author: wenhua cheng; Haihao Shen; weiweiz1; Heng Guo; Huang, Tai; Ke Ding; Ilyas Moutawwakil; Marc Sun; Mohamed Mekkouri

Published: 2025-04-29T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [quantization](<https://devfeed.tech/topics/quantization.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [intel](<https://devfeed.tech/topics/intel.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>), [llama](<https://devfeed.tech/topics/llama.md>), [qwen](<https://devfeed.tech/topics/qwen.md>)

Tags: [architectures](<https://devfeed.tech/tags/architectures.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [intel](<https://devfeed.tech/tags/intel.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llms](<https://devfeed.tech/tags/llms.md>), [model](<https://devfeed.tech/tags/model.md>), [offline](<https://devfeed.tech/tags/offline.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [precision](<https://devfeed.tech/tags/precision.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

## AI overview

This article introduces AutoRound, Intel's weight-only post-training quantization method for LLMs and VLMs. It uses signed gradient descent to optimize weight rounding and clipping ranges, supports low-bit formats from INT2 to INT8, and aims to preserve accuracy with efficient quantization. The article describes its model, hardware, export-format, calibration, and tuning-recipe support, including performance on low-bit benchmarks.

## Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.