Intel® Neural Compressor aims to provide popular model compression techniques such as Static Quantization, Dynamic Quantization, SmoothQuant, Weight-Only Quantization, Quantization-Aware Training, Mixed Precision, etc.

• Support advanced quantization of Large Language Models (LLMs) and Vision-Language Models (VLMs) such as LLaMA, Qwen, DeepSeek, Flux, FramePack, etc., across diverse quantization techniques and low-precision data types through integration with AutoRound.

• Support a wide range of Intel hardware such as Intel Gaudi Al Accelerators, Intel Core Ultra Processors, Intel Xeon Scalable Processors, Intel Xeon CPU Max Series, Intel Data Center GPU Flex Series, and Intel Data Center GPU Max Series with extensive testing; support AMD CPU, ARM CPU, and NVidia GPU with limited testing.

What's New

• [2026/03] FP8 quantization support for Keras/JAX (experimental)
• [2026/03] FP8 KV cache/Attention static quantization with AutoRound (experimental)
• [2025/12] NVFP4 quantization experimental support
• [2025/10] MXFP8 / MXFP4 quantization experimental support
• [2025/09] FP8 dynamic quantization, including Linear, FusedMoE on Intel Gaudi AI Accelerators
• [2025/05] FP8 static quantization of DeepSeek V3/R1 model on Intel Gaudi AI Accelerators
• [2025/03] VLM quantization in transformers-like API on Intel CPU/GPU

Installation

Choose the necessary framework dependencies to install based on your deploy environment.

Install Framework for PyTorch Backend (on-demand)

Intel Neural Compressor supports PyTorch with CPU, GPU and HPU. Please install the corresponding PyTorch version based on your hardware environment.

• Install intel_extension_for_pytorch for CPU
• Install intel_extension_for_pytorch for Intel GPU
• Use Docker Image with torch installed for HPU
  Note: There is a version mapping between Intel Neural Compressor and Gaudi Software Stack, please refer to this table and make sure to use a matched combination.
• Install torch for other platform

Install Neural Compressor from pypi

# Framework extension API + PyTorch dependency
pip install neural-compressor-pt
# Framework extension API + JAX dependency
pip install neural-compressor-jax

Note: Further installation methods can be found under Installation Guide. check out our FAQ for more details.

Getting Started

After successfully installing these packages, try your first quantization program. Following example code demonstrates FP8 Quantization, it is supported by Intel Gaudi2 AI Accelerator.
To try on Intel Gaudi2, docker image with Gaudi Software Stack is recommended, please refer to following script for environment setup. More details can be found in Gaudi Guide.

Run a container with an interactive shell, more info

docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.24.0/ubuntu24.04/habanalabs/pytorch-installer-2.10.0:latest

Note: Since Habana software >= 1.21.0, PT_HPU_LAZY_MODE=0 is the default setting. However, most low-precision functions (such as convert_from_uint4) do not support this setting. Therefore, we recommend setting PT_HPU_LAZY_MODE=1 to maintain compatibility.

Run the example,

from neural_compressor.torch.quantization import (
    FP8Config,
    prepare,
    convert,
)

import torch
import torchvision.models as models

model = models.resnet18()
qconfig = FP8Config(fp8_config="E4M3")
model = prepare(model, qconfig)

# Customer defined calibration. Below is a dummy calibration
model(torch.randn(1, 3, 224, 224).to("hpu"))

model = convert(model)

output = model(torch.randn(1, 3, 224, 224).to("hpu")).to("cpu")
print(output.shape)

More FP8 quantization doc.

Following example code demonstrates weight-only large language model loading on Intel Gaudi2 AI Accelerator.

from neural_compressor.torch.quantization import load

model_name = "TheBloke/Llama-2-7B-GPTQ"
model = load(
    model_name_or_path=model_name,
    format="huggingface",
    device="hpu",
    torch_dtype=torch.bfloat16,
)

Note: Intel Neural Compressor will convert the model format from auto-gptq to hpu format on the first load and save hpu_model.safetensors to the local cache directory for the next load. So it may take a while to load for the first time.

Documentation

Overview
Architecture	Workflow	APIs	Examples
PyTorch Extension APIs
Overview
Dynamic Quantization	Static Quantization	Smooth Quantization
Weight-Only Quantization	FP8 Quantization	Mixed Precision
MX Quantization	NVFP4 Quantization
Transformers-like APIs
Overview
JAX Extension APIs
Overview
Other Modules
Auto Tune

Selected Publications/Events

• arXiv: Faster Inference of LLMs using FP8 on the Intel Gaudi (Mar 2025)
• PyTorch landscape: PyTorch general optimizations (Mar 2025)
• Blog on SqueezeBits: [Intel Gaudi] #4. FP8 Quantization (Jan 2025)

Note: View Full Publication List.

Additional Content

• Contribution Guidelines
• Legal Information
• Security Policy

Communication

• GitHub Issues: mainly for bug reports, new feature requests, question asking, etc.
• Email: welcome to raise any interesting research ideas on model compression techniques by email for collaborations.