Intel® Neural Compressor

An open-source Python library supporting popular model compression techniques on all mainstream deep learning frameworks (TensorFlow, PyTorch, ONNX Runtime, and MXNet)

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Architecture   |   Workflow   |   Results   |   Examples   |   Documentations

Intel® Neural Compressor aims to provide popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks such as TensorFlow, PyTorch, ONNX Runtime, and MXNet, as well as Intel extensions such as Intel Extension for TensorFlow and Intel Extension for PyTorch. In particular, the tool provides the key features, typical examples, and open collaborations as below:


Install from pypi

pip install neural-compressor

More installation methods can be found at Installation Guide. Please check out our FAQ for more details.

Getting Started

Quantization with Python API

# Install Intel Neural Compressor and TensorFlow
pip install neural-compressor
pip install tensorflow
# Prepare fp32 model
from import DataLoader, Datasets
from neural_compressor.config import PostTrainingQuantConfig

dataset = Datasets("tensorflow")["dummy"](shape=(1, 224, 224, 3))
dataloader = DataLoader(framework="tensorflow", dataset=dataset)

from neural_compressor.quantization import fit

q_model = fit(


Architecture Workflow Examples APIs
Python-based APIs
Quantization Advanced Mixed Precision Pruning (Sparsity) Distillation
Orchestration Benchmarking Distributed Compression Model Export
Neural Coder (Zero-code Optimization)
Launcher JupyterLab Extension Visual Studio Code Extension Supported Matrix
Advanced Topics
Adaptor Strategy Distillation for Quantization SmoothQuant
Weight-Only Quantization (INT8/INT4/FP4/NF4) FP8 Quantization
Innovations for Productivity
Neural Insights Neural Solution

More documentations can be found at User Guide.

Selected Publications/Events

Additional Content


  • GitHub Issues: mainly for bugs report, new feature request, question asking, etc.

  • Email: welcome to raise any interesting research ideas on model compression techniques by email for collaborations.

  • [WeChat group]./imgs/wechat_group.jpg): scan the QA code to join the technical discussion.