OpenVINO

Developer(s)| Intel Corporation
Initial release| May 16, 2018; 7 years ago (2018-05-16)
Stable release|

2025.3 / September 2025.[1]

Repository| github.com/openvinotoolkit/openvino
Written in| C++
Operating system| Cross-platform
License| Apache License 2.0
Website| openvino.ai
As of| September 2025

OpenVINO is an open-source software toolkit for optimizing and deploying deep learning models. It enables programmers to develop scalable and efficient AI solutions with relatively few lines of code. It supports several popular model formats[2] and categories, such as large language models, computer vision, and generative AI.

Actively developed by Intel, it prioritizes high-performance inference on Intel hardware but also supports ARM/ARM64 processors[2] and encourages contributors to add new devices to the portfolio. It sees great use in AI Sound Processing drivers when tied with Intel's Gaussian & Neural Accelerator (GNA).

Based in C++, it offers the following APIs: C/C++, Python, and Node.js (an early preview).

OpenVINO is cross-platform and free for use under Apache License 2.0.[3]

## Contents

* 1 Workflow
* 2 OpenVINO model format
* 3 OS support
* 4 See also
* 5 References
* 6 External links

## Workflow

The simplest OpenVINO usage involves obtaining a model and running it as is. Yet for the best results, a more complete workflow is suggested:[4]

* obtain a model in one of supported frameworks,
* convert the model to OpenVINO IR using the OpenVINO Converter tool,
* optimize the model, using training-time or post-training options provided by OpenVINO's NNCF.
* execute inference, using OpenVINO Runtime by specifying one of several inference modes.

## OpenVINO model format

OpenVINO IR[5] is the default format used to run inference. It is saved as a set of two files, *.bin and *.xml, containing weights and topology, respectively. It is obtained by converting a model from one of the supported frameworks, using the application's API or a dedicated converter.

Models of the supported formats may also be used for inference directly, without prior conversion to OpenVINO IR. Such an approach is more convenient but offers fewer optimization options and lower performance, since the conversion is performed automatically before inference. Some pre-converted models can be found in the Hugging Face repository.[6]

The supported model formats are:[7]

* PyTorch
* TensorFlow
* TensorFlow Lite
* ONNX (including formats that may be serialized to ONNX)
* PaddlePaddle
* JAX/Flax

## OS support

OpenVINO runs on Windows, Linux and MacOS.[8]

## See also

* Comparison of deep learning software

## References

1. ↑ "Release Notes for Intel Distribution of OpenVINO toolkit 2025.3". September 2025. https://docs.openvino.ai/releasenotes.
2. ↑ 2.0 2.1 "OpenVINO Compatibility and Support". 24 Jan 2024. https://docs.openvino.ai/compatibility.
3. ↑ "License". 16 Oct 2018. https://github.com/openvinotoolkit/openvino?tab=Apache-2.0-1-ov-file.
4. ↑ "OpenVINO Workflow". 25 April 2024. https://docs.openvino.ai/2024/openvino-workflow.html.
5. ↑ "OpenVINO IR". 2 Feb 2024. https://docs.openvino.ai/2023.3/openvino_ir.html.
6. ↑ "Hugging Face OpenVINO Space". https://huggingface.co/OpenVINO.
7. ↑ "OpenVINO Model Preparation". 24 Jan 2024. https://docs.openvino.ai/2023.3/openvino_docs_model_processing_introduction.html.
8. ↑ "System Requirements". February 2024. https://docs.openvino.ai/2023.3/system_requirements.html#operating-systems-and-developer-environment.

* Agrawal, Vasu (2019). Ground Up Design of a Multi-modal Object Detection System (PDF) (MSc). Carnegie Mellon University Pittsburgh, PA. Archived (PDF) from the original on 26 January 2020.
* Driaba, Alexander; Gordeev, Aleksei; Klyachin, Vladimir (2019). Recognition of Various Objects from a Certain Categorical Set in Real Time Using Deep Convolutional Neural Networks. Institute of Mathematics and Informational Technologies Volgograd State University. http://ceur-ws.org/Vol-2500/paper_5.pdf. Retrieved 26 January 2020.
* Nanjappa, Ashwin (31 May 2019). Caffe2 Quick Start Guide: Modular and scalable deep learning made easy. Packt. pp. 91–98. ISBN 978-1789137750.

## External links

* on GitHub

* v
* t
* e

Intel Software

Items in italics are no longer maintained or have planned end-of-life dates.

Development|

* Parallel Studio
* C++ Compiler
* Fortran Compiler
* Debugger
* Advisor
* Inspector
* VTune
* Array Visualizer
* Cilk Plus

Components|

* Concurrent Collections for C++ (CnC)
* Data Analytics Acceleration Library (DAAL)
* Indeo
* Integrated Performance Primitives (IPP)
* Math Kernel Library (MKL)
* Parallel Building Blocks (PBB)
* Intel Array Building Blocks (ArBB)
* Ct
* Threading Building Blocks (TBB)

Open source|

* Cilk Plus
* Concurrent Collections for C++ (CnC)
* Data Analytics Acceleration Library (DAAL)
* MeeGo
* Moblin
* Threading Building Blocks (TBB)
* Tizen

Software programs|

* Telekinesys Research1
* Havok1
* Vision1
* Wind River Systems
* Simics
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Organizations|

* Developer Zone
* AppUp
* Research

* 1Sold to Microsoft

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Deep learning software

Open-source|

* Apache SINGA
* Caffe
* Deeplearning4j
* Dlib
* Keras
* Microsoft Cognitive Toolkit
* MXNet
* OpenNN
* PyTorch
* TensorFlow
* Theano
* Torch
* ONNX

Proprietary|

* Maple
* Neural Designer
* Wolfram Mathematica
* Apple Core ML

* Category
* Comparison

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