nncf 2.19.0
Neural Networks Compression Framework
Requires Python: >=3.10
Keywords: bert, classification, compression, hawq, mixed-precision-training, mmdetection, nas, nlp, object-detection, pruning, quantization, quantization-aware-training, semantic-segmentation, sparsity, transformers

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# Neural Network Compression Framework (NNCF)

Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for
optimizing inference of neural networks in [OpenVINO&trade;](https://docs.openvino.ai) with a minimal accuracy drop.

NNCF is designed to work with models from [PyTorch](https://pytorch.org/),
[TorchFX](https://pytorch.org/docs/stable/fx.html), [TensorFlow](https://www.tensorflow.org/),
[ONNX](https://onnx.ai/) and [OpenVINO&trade;](https://docs.openvino.ai).

NNCF provides [samples](https://github.com/openvinotoolkit/nncf/blob/develop/#demos-tutorials-and-samples) that demonstrate the usage of compression algorithms for different
use cases and models. See compression results achievable with the NNCF-powered samples on the [NNCF Model Zoo page](https://github.com/openvinotoolkit/nncf/blob/develop/docs/ModelZoo.md).

The framework is organized as a Python\* package that can be built and used in a standalone mode. The framework
architecture is unified to make it easy to add different compression algorithms for both PyTorch and TensorFlow deep
learning frameworks.

For more information about NNCF, see:

- [NNCF repository](https://github.com/openvinotoolkit/nncf)
- [User documentation](https://docs.openvino.ai/nncf)
- [NNCF API documentation](https://openvinotoolkit.github.io/nncf/autoapi/nncf/)
- [Usage examples](https://github.com/openvinotoolkit/nncf/tree/develop/docs/usage)
- [Notebook tutorials](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/README.md#model-training)
- [NNCF Compressed Model Zoo](#nncf-compressed-model-zoo)

## Table of contents

- [Key Features](#key-features)
- [Installation](#installation-guide)
- [Third-party integration](#third-party-repository-integration)

## Key Features<a id="key-features"></a>

### Post-Training Compression Algorithms

| Compression algorithm                                                                                    | OpenVINO  | PyTorch   | TorchFX   | TensorFlow    | ONNX          |
| :------------------------------------------------------------------------------------------------------- | :-------: | :-------: | :-----------: | :-----------: | :-----------: |
| [Post-Training Quantization](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported | Experimental | Supported     | Supported     |
| [Weights Compression](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/post_training_compression/weights_compression/Usage.md)               | Supported | Supported | Experimental | Not supported | Supported |
| [Activation Sparsity](https://github.com/openvinotoolkit/nncf/blob/develop/src/nncf/experimental/torch/sparsify_activations/ActivationSparsity.md)          | Not supported | Experimental | Not supported| Not supported| Not supported |

### Training-Time Compression Algorithms

| Compression algorithm                                                                                      | PyTorch      | TensorFlow    |
| :--------------------------------------------------------------------------------------------------------- | :----------: | :-----------: |
| [Quantization Aware Training](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/quantization_aware_training/Usage.md) | Supported    | Supported     |
| [Weight-Only Quantization Aware Training with LoRA and NLS](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/quantization_aware_training_lora/Usage.md) | Supported    | Not Supported     |
| [Mixed-Precision Quantization](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#mixed-precision-quantization) | Supported | Not supported |
| [Sparsity](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/Sparsity.md)                            | Supported    | Supported     |
| [Filter pruning](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/Pruning.md)                       | Supported    | Supported     |
| [Movement pruning](https://github.com/openvinotoolkit/nncf/blob/develop/src/nncf/experimental/torch/sparsity/movement/MovementSparsity.md)                    | Experimental | Not supported |

- Automatic, configurable model graph transformation to obtain the compressed model.
  > **NOTE**: Limited support for TensorFlow models. Only models created using Sequential or Keras Functional API are supported.
- Common interface for compression methods.
- GPU-accelerated layers for faster compressed model fine-tuning.
- Distributed training support.
- Git patch for prominent third-party repository ([huggingface-transformers](https://github.com/huggingface/transformers)) demonstrating the process of integrating NNCF into custom training pipelines.
- Exporting PyTorch compressed models to ONNX\* checkpoints and TensorFlow compressed models to SavedModel or Frozen Graph format, ready to use with [OpenVINO&trade; toolkit](https://docs.openvino.ai).
- Support for [Accuracy-Aware model training](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/Usage.md#accuracy-aware-model-training) pipelines via the [Adaptive Compression Level Training](https://github.com/openvinotoolkit/nncf/blob/develop/docs/accuracy_aware_model_training/AdaptiveCompressionLevelTraining.md) and [Early Exit Training](https://github.com/openvinotoolkit/nncf/blob/develop/docs/accuracy_aware_model_training/EarlyExitTraining.md).

## Installation Guide<a id="installation-guide"></a>

For detailed installation instructions, refer to the [Installation](https://github.com/openvinotoolkit/nncf/blob/develop/docs/Installation.md) guide.

NNCF can be installed as a regular PyPI package via pip:

```bash
pip install nncf
```

NNCF is also available via [conda](https://anaconda.org/conda-forge/nncf):

```bash
conda install -c conda-forge nncf
```

System requirements of NNCF correspond to the used backend. System requirements for each backend and
the matrix of corresponding versions can be found in [installation.md](https://github.com/openvinotoolkit/nncf/blob/develop/docs/Installation.md).

## Third-party Repository Integration<a id="third-party-repository-integration"></a>

NNCF may be easily integrated into training/evaluation pipelines of third-party repositories.

### Used by

- [HuggingFace Optimum Intel](https://huggingface.co/docs/optimum/intel/optimization_ov)

  NNCF is used as a compression backend within the renowned `transformers` repository in HuggingFace Optimum Intel. For instance, the command below exports the [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) model to OpenVINO format with INT4-quantized weights:

  ```bash
  optimum-cli export openvino -m meta-llama/Llama-3.2-3B-Instruct --weight-format int4 ./Llama-3.2-3B-Instruct-int4
  ```

- [Ultralytics](https://docs.ultralytics.com/integrations/openvino)

  NNCF is integrated into the Intel OpenVINO export pipeline, enabling quantization for the exported models.

- [ExecuTorch](https://github.com/pytorch/executorch/blob/main/examples/openvino/README.md)

  NNCF is used as primary quantization framework for the [ExecuTorch OpenVINO integration](https://docs.pytorch.org/executorch/main/build-run-openvino.html).

- [torch.compile](https://docs.pytorch.org/tutorials/prototype/openvino_quantizer.html)

  NNCF is used as primary quantization framework for the [torch.compile OpenVINO integration](https://docs.openvino.ai/2025/openvino-workflow/torch-compile.html).

- [OpenVINO Training Extensions](https://github.com/openvinotoolkit/training_extensions)

  NNCF is integrated into OpenVINO Training Extensions as a model optimization backend. You can train, optimize, and
  export new models based on available model templates as well as run the exported models with OpenVINO.

## NNCF Compressed Model Zoo<a id="nncf-compressed-model-zoo"></a>

List of models and compression results for them can be found at our [NNCF Model Zoo page](https://github.com/openvinotoolkit/nncf/blob/develop/docs/ModelZoo.md).

## Citing

```bi
@article{kozlov2020neural,
    title =   {Neural network compression framework for fast model inference},
    author =  {Kozlov, Alexander and Lazarevich, Ivan and Shamporov, Vasily and Lyalyushkin, Nikolay and Gorbachev, Yury},
    journal = {arXiv preprint arXiv:2002.08679},
    year =    {2020}
}
```

## Telemetry

NNCF as part of the OpenVINO™ toolkit collects anonymous usage data for the purpose of improving OpenVINO™ tools.
You can opt-out at any time by running the following command in the Python environment where you have NNCF installed:

`opt_in_out --opt_out`

More information available on [OpenVINO telemetry](https://docs.openvino.ai/2025/about-openvino/additional-resources/telemetry.html).