Software:OpenVINO
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| Developer(s) | Intel Corporation |
|---|---|
| Initial release | May 16, 2018 |
| Repository | github |
| Written in | C++ |
| Operating system | Cross-platform |
| License | Apache License 2.0 |
| Website | openvino |
| As of | August 2026 |
OpenVINO is an open-source software toolkit developed by Intel for optimizing and deploying deep learning models. It supports several popular model formats[1] and workloads, including large language models, computer vision, generative AI, speech processing, and robot-policy deployment.[2]
OpenVINO is optimized for Intel hardware, but offers support for ARM/ARM64 processors.[1] It sees use in AI Sound Processing drivers when tied with Intel's Gaussian & Neural Accelerator (GNA).
Based in C++, it extends API support for C and Python, as well as Node.js.
OpenVINO is cross-platform and free for use under Apache License 2.0.[3]
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.[5]
- execute inference, using OpenVINO Runtime by specifying one of several inference modes.
OpenVINO model format
OpenVINO IR[6] 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.[7]
The supported model formats are:[8]
- 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.[9]
See also
References
- ↑ 1.0 1.1 "OpenVINO Compatibility and Support". 24 January 2024. https://docs.openvino.ai/compatibility.
- ↑ "OpenVINO Physical AI". 4 August 2026. https://docs.openvino.ai/2026/physical-ai.html.
- ↑ "License". 16 October 2018. https://github.com/openvinotoolkit/openvino?tab=Apache-2.0-1-ov-file.
- ↑ "OpenVINO Workflow". 25 April 2024. https://docs.openvino.ai/2024/openvino-workflow.html.
- ↑ "openvinotoolkit/nncf". OpenVINO™ Toolkit. https://github.com/openvinotoolkit/nncf.
- ↑ "OpenVINO IR". 2 February 2024. https://docs.openvino.ai/2023.3/openvino_ir.html.
- ↑ "Hugging Face OpenVINO Space". https://huggingface.co/OpenVINO.
- ↑ "OpenVINO Model Preparation". 24 January 2024. https://docs.openvino.ai/2023.3/openvino_docs_model_processing_introduction.html.
- ↑ "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". Proceedings of the YSIP-3 Workshop (Institute of Mathematics and Informational Technologies Volgograd State University). http://ceur-ws.org/Vol-2500/paper_5.pdf. Retrieved 9 April 2026.
- 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
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