OpenVINO™ Toolkit repository
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What is OpenVINO toolkit?

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.

  • Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks
  • Use models trained with popular frameworks like TensorFlow, PyTorch and more
  • Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud

This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.


  • OpenVINO™ Runtime - is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice.
    • core - provides the base API for model representation and modification.
    • inference - provides an API to infer models on device.
    • transformations - contains the set of common transformations which are used in OpenVINO plugins.
    • low precision transformations - contains the set of transformations which are used in low precision models
    • bindings - contains all available OpenVINO bindings which are maintained by OpenVINO team.
      • c - provides C API for OpenVINO™ Runtime
      • python - Python API for OpenVINO™ Runtime
  • Plugins - contains OpenVINO plugins which are maintained in open-source by OpenVINO team. For more information please take a look to the list of supported devices.
  • Frontends - contains available OpenVINO frontends which allow to read model from native framework format.
  • Model Optimizer - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
  • Post-Training Optimization Tool - is designed to accelerate the inference of deep learning models by applying special methods without model retraining or fine-tuning, for example, post-training 8-bit quantization.
  • Samples - applications on C, C++ and Python languages which shows basic use cases of OpenVINO usages.

Supported Hardware matrix

The OpenVINO™ Runtime can infer models on different hardware devices. This section provides the list of supported devices.

Also OpenVINO™ Toolkit contains several plugins which should simplify to load model on several hardware devices:


OpenVINO™ Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.


User documentation

The latest documentation for OpenVINO™ Toolkit is available here. This documentation contains detailed information about all OpenVINO components and provides all important information which could be needed if you create an application which is based on binary OpenVINO distribution or own OpenVINO version without source code modification.

Developer documentation

Developer documentation contains information about architectural decisions which are applied inside the OpenVINO components. This documentation has all necessary information which could be needed in order to contribute to OpenVINO.


The list of OpenVINO tutorials:

Products which use OpenVINO

System requirements

The full information about system requirements depends on platform and is available on dedicated pages:

How to build

Please take a look to OpenVINO Wiki to get more information about OpenVINO build process.

How to contribute

See CONTRIBUTING for details. Thank you!

Get a support

Please report questions, issues and suggestions using:

See also

* Other names and brands may be claimed as the property of others.