--- title: Advancing low‑bit quantization for LLMs: AutoRound x LLM... description: AutoRound, Intel's post-training quantization algorithm, is now integrated into LLM Compressor, enabling high-accuracy low-bit quantization (W4A16) for large... canonical: https://daily.dev/posts/advancing-low-bit-quantization-for-llms-autoround-x-llm-compressor-0b1ym4jas twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Advancing low‑bit quantization for LLMs: AutoRound x LLM Compressor | daily.dev og:description: AutoRound, Intel's post-training quantization algorithm, is now integrated into LLM Compressor, enabling high-accuracy low-bit quantization (W4A16) for large... og:url: https://daily.dev/posts/advancing-low-bit-quantization-for-llms-autoround-x-llm-compressor-0b1ym4jas og:image: https://api.daily.dev/og/posts/0B1yM4JAs.png og:image:alt: Advancing low‑bit quantization for LLMs: AutoRound x LLM Compressor og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Advancing low‑bit quantization for LLMs: AutoRound x LLM Compressor **[Red Hat Developer](https://daily.dev/sources/rhdev)** · 6 min read · 1 upvotes · 0 comments ## Summary AutoRound, Intel's post-training quantization algorithm, is now integrated into LLM Compressor, enabling high-accuracy low-bit quantization (W4A16) for large language models with minimal tuning overhead. The integration supports sequential calibration, produces compressed tensors compatible with vLLM serving, and works across Intel hardware (Xeon, Gaudi, Arc GPUs) and CUDA devices. The workflow requires just 128 calibration samples and ~200 iterations, with future plans to support FP8, MXFP4/8, mixed-bit search, and MoE models. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [GitHub - intel/auto-round: A SOTA quantization algorithm for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, S](https://daily.dev/posts/github---intel-auto-round-a-sota-quantization-algorithm-for-high-accuracy-low-bit-llm-inference-se-jf24m2wld) · Hacker News · 1 upvotes · 0 comments - [LLM Compressor 0.9.0: Attention quantization, MXFP4 support, and more](https://daily.dev/posts/llm-compressor-0-9-0-attention-quantization-mxfp4-support-and-more-y9b2mh3yd) · Red Hat Developer · 1 upvotes · 0 comments - [5 LLM Quantization Techniques](https://daily.dev/posts/5-llm-quantization-techniques-nfunrpnuc) · Daily Dose of Data Science \| Avi Chawla \| Substack · 3 upvotes · 1 comments --- Tags: [#data-science](https://daily.dev/tags/data-science), [#llm](https://daily.dev/tags/llm), [#intel](https://daily.dev/tags/intel) [View this post on daily.dev](https://daily.dev/posts/advancing-low-bit-quantization-for-llms-autoround-x-llm-compressor-0b1ym4jas) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Advancing low‑bit quantization for LLMs: AutoRound x LLM Compressor","url":"https://daily.dev/posts/advancing-low-bit-quantization-for-llms-autoround-x-llm-compressor-0b1ym4jas","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/advancing-low-bit-quantization-for-llms-autoround-x-llm-compressor-0b1ym4jas"},"datePublished":"2025-12-09T04:13:02.685Z","dateModified":"2025-12-09T04:13:41.581Z","description":"AutoRound, Intel's post-training quantization algorithm, is now integrated into LLM Compressor, enabling high-accuracy low-bit quantization (W4A16) for large...","image":"https://developers.redhat.com/sites/default/files/styles/share/public/AutoRound_LLMCompressor%20%281%29.png?itok=JSJl_sg6","thumbnailUrl":"https://developers.redhat.com/sites/default/files/styles/share/public/AutoRound_LLMCompressor%20%281%29.png?itok=JSJl_sg6","isAccessibleForFree":true,"articleSection":"Red Hat Developer","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Red Hat Developer","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/2a8895f3216c4900bdc455585a4ad3e4","url":"https://daily.dev/sources/rhdev"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/advancing-low-bit-quantization-for-llms-autoround-x-llm-compressor-0b1ym4jas","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"data-science,llm,intel","timeRequired":"PT6M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Red Hat Developer","item":"https://daily.dev/sources/rhdev"},{"@type":"ListItem","position":3,"name":"Advancing low‑bit quantization for LLMs: AutoRound x LLM Compressor"}]} ```