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, GNA, multi device and heterogeneous plugins to accelerate deep learning inference on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from 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 the device.
    • transformations - contains the set of common transformations which are used in OpenVINO plugins.
    • low precision transformations - contains the set of transformations that are used in low precision models
    • bindings - contains all available OpenVINO bindings which are maintained by the OpenVINO team.
      • c - C API for OpenVINO™ Runtime
      • python - Python API for OpenVINO™ Runtime
  • Plugins - contains OpenVINO plugins which are maintained in open-source by the OpenVINO team. For more information, take a look at the list of supported devices.
  • Frontends - contains available OpenVINO frontends that allow reading models from the 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 in C, C++ and Python languages that show basic OpenVINO use cases.

Supported Hardware matrix

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

OpenVINO™ Toolkit also contains several plugins which simplify loading models 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 the important information you may need to create an application 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 system requirements vary depending on platform and are available on dedicated pages:

How to build

See How to build OpenVINO to get more information about the OpenVINO build process.

How to contribute

See Contributions Welcome for good first issues.

See CONTRIBUTING for contribution details. Thank you!

Get a support

Report questions, issues and suggestions, using:

Additional Resources

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