Intel AutoRound Accelerates Low Bit LLM Performance Across Intel CPUs, GPUs, and CUDA Devices

Intel is advancing the efficiency of Large Language Model deployment with the introduction of AutoRound, a next generation post training quantization algorithm that delivers faster, more accurate, and more efficient LLM serving across Intel CPUs, Intel GPUs, and even CUDA based devices. As part of Intel’s broader AI acceleration roadmap, the upcoming Crescent Island data center GPU architecture is confirmed to include native support for FP8, MXFP8, and MXFP4 formats ensuring an aligned hardware software pipeline for the next wave of low bit inference workloads.

Intel announced that AutoRound is now fully integrated into LLM Compressor enabling a streamlined workflow where models can be quantized and served with minimal code. LLM Compressor in turn works seamlessly with compressed tensors and is directly compatible with vLLM allowing enterprises, researchers, and developers to accelerate model serving immediately.

The collaboration aims to deliver higher accuracy at low bit width quantization, lightweight tuning that requires only hundreds of optimization steps, zero added inference overhead, and a simplified developer experience.

What Is AutoRound?

AutoRound is Intel’s advanced PTQ algorithm designed specifically for LLMs and Vision Language Models. It introduces three learnable parameters per quantized tensor: the rounding offset v and the α and β clipping control parameters. By processing decoder layers sequentially and applying signed gradient descent, AutoRound jointly optimizes both rounding and clipping effects to significantly reduce block wise reconstruction error.

Key advantages include

• Superior accuracy in very low bit environments
  

• Support for multiple quantized data types including W4A16, MXFP8, MXFP4, FP8, NVFP4 with more formats planned
  

• Layer wise and mixed bit precision search for customizable accuracy to performance scaling
  

• Applicability to both LLMs and VLMs

AutoRound enables quantized model formats that unlock inference acceleration across Intel Xeon processors, Intel Gaudi accelerators, Intel Data Center GPUs, Intel Arc B Series graphics, and even non Intel GPUs using CUDA.

Looking ahead, Intel confirms that Crescent Island will incorporate native support for FP8, MXFP8, and MXFP4. AutoRound quantized models will immediately benefit from these optimized data formats, providing a direct pathway from algorithm level innovation to commercial deployment across Intel’s AI hardware ecosystem.

This integration positions AutoRound as a pivotal technology for next generation LLM serving helping organizations reduce compute requirements, improve throughput, and deploy more efficient models at scale.

Do you see low bit quantization becoming the standard approach for enterprise LLM deployment? What benefits stand out most from Intel’s AutoRound strategy?