<div align="center">
<img src="https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/assets/openvino-logo-purple-black.svg" width="400px">
<h3 align="center">
Open-source software toolkit for optimizing and deploying deep learning models.
</h3>
<p align="center">
<a href="https://docs.openvino.ai/2025/index.html"><b>Documentation</b></a> • <a href="https://blog.openvino.ai"><b>Blog</b></a> • <a href="https://docs.openvino.ai/2025/about-openvino/key-features.html"><b>Key Features</b></a> • <a href="https://docs.openvino.ai/2025/get-started/learn-openvino.html"><b>Tutorials</b></a> • <a href="https://docs.openvino.ai/2025/documentation/openvino-ecosystem.html"><b>Integrations</b></a> • <a href="https://docs.openvino.ai/2025/about-openvino/performance-benchmarks.html"><b>Benchmarks</b></a> • <a href="https://github.com/openvinotoolkit/openvino.genai"><b>Generative AI</b></a>
</p>
[](https://badge.fury.io/py/openvino)
[](https://anaconda.org/conda-forge/openvino)
[](https://formulae.brew.sh/formula/openvino)
[](https://pepy.tech/project/openvino)
[](https://anaconda.org/conda-forge/openvino/files)
[](https://formulae.brew.sh/formula/openvino)
</div>
- **Inference Optimization**: Boost deep learning performance in computer vision, automatic speech recognition, generative AI, natural language processing with large and small language models, and many other common tasks.
- **Flexible Model Support**: Use models trained with popular frameworks such as PyTorch, TensorFlow, ONNX, Keras, PaddlePaddle, and JAX/Flax. Directly integrate models built with transformers and diffusers from the Hugging Face Hub using Optimum Intel. Convert and deploy models without original frameworks.
- **Broad Platform Compatibility**: Reduce resource demands and efficiently deploy on a range of platforms from edge to cloud. OpenVINO™ supports inference on CPU (x86, ARM), GPU (Intel integrated & discrete GPU) and AI accelerators (Intel NPU).
- **Community and Ecosystem**: Join an active community contributing to the enhancement of deep learning performance across various domains.
Check out the [OpenVINO Cheat Sheet](https://docs.openvino.ai/2025/_static/download/OpenVINO_Quick_Start_Guide.pdf) and [Key Features](https://docs.openvino.ai/2025/about-openvino/key-features.html) for a quick reference.
## Installation
[Get your preferred distribution of OpenVINO](https://docs.openvino.ai/2025/get-started/install-openvino.html) or use this command for quick installation:
```sh
pip install -U openvino
```
Check [system requirements](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino/system-requirements.html) and [supported devices](https://docs.openvino.ai/2025/documentation/compatibility-and-support/supported-devices.html) for detailed information.
## Tutorials and Examples
[OpenVINO Quickstart example](https://docs.openvino.ai/2025/get-started.html) will walk you through the basics of deploying your first model.
Learn how to optimize and deploy popular models with the [OpenVINO Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)📚:
- [Create an LLM-powered Chatbot using OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/llm-chatbot-generate-api.ipynb)
- [YOLOv11 Optimization](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/yolov11-optimization/yolov11-object-detection.ipynb)
- [Text-to-Image Generation](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/text-to-image-genai/text-to-image-genai.ipynb)
- [Multimodal assistant with LLaVa and OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llava-multimodal-chatbot/llava-multimodal-chatbot-genai.ipynb)
- [Automatic speech recognition using Whisper and OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/whisper-asr-genai/whisper-asr-genai.ipynb)
Discover more examples in the [OpenVINO Samples (Python & C++)](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples.html) and [Notebooks (Python)](https://docs.openvino.ai/2025/get-started/learn-openvino/interactive-tutorials-python.html).
Here are easy-to-follow code examples demonstrating how to run PyTorch and TensorFlow model inference using OpenVINO:
**PyTorch Model**
```python
import openvino as ov
import torch
import torchvision
# load PyTorch model into memory
model = torch.hub.load("pytorch/vision", "shufflenet_v2_x1_0", weights="DEFAULT")
# convert the model into OpenVINO model
example = torch.randn(1, 3, 224, 224)
ov_model = ov.convert_model(model, example_input=(example,))
# compile the model for CPU device
core = ov.Core()
compiled_model = core.compile_model(ov_model, 'CPU')
# infer the model on random data
output = compiled_model({0: example.numpy()})
```
**TensorFlow Model**
```python
import numpy as np
import openvino as ov
import tensorflow as tf
# load TensorFlow model into memory
model = tf.keras.applications.MobileNetV2(weights='imagenet')
# convert the model into OpenVINO model
ov_model = ov.convert_model(model)
# compile the model for CPU device
core = ov.Core()
compiled_model = core.compile_model(ov_model, 'CPU')
# infer the model on random data
data = np.random.rand(1, 224, 224, 3)
output = compiled_model({0: data})
```
OpenVINO supports the CPU, GPU, and NPU [devices](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html) and works with models from PyTorch, TensorFlow, ONNX, TensorFlow Lite, PaddlePaddle, and JAX/Flax [frameworks](https://docs.openvino.ai/2025/openvino-workflow/model-preparation.html). It includes [APIs](https://docs.openvino.ai/2025/api/api_reference.html) in C++, Python, C, NodeJS, and offers the GenAI API for optimized model pipelines and performance.
## Generative AI with OpenVINO
Get started with the OpenVINO GenAI [installation](https://docs.openvino.ai/2025/get-started/install-openvino/install-openvino-genai.html) and refer to the [detailed guide](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai.html) to explore the capabilities of Generative AI using OpenVINO.
Learn how to run LLMs and GenAI with [Samples](https://github.com/openvinotoolkit/openvino.genai/tree/master/samples) in the [OpenVINO™ GenAI repo](https://github.com/openvinotoolkit/openvino.genai). See GenAI in action with Jupyter notebooks: [LLM-powered Chatbot](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-chatbot) and [LLM Instruction-following pipeline](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-question-answering).
## Documentation
[User documentation](https://docs.openvino.ai/) contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications.
[Developer documentation](https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/index.md) focuses on the OpenVINO architecture and describes [building](https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md) and [contributing](https://github.com/openvinotoolkit/openvino/blob/master/CONTRIBUTING.md) processes.
## OpenVINO Ecosystem
### OpenVINO Tools
- [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - advanced model optimization techniques including quantization, filter pruning, binarization, and sparsity.
- [GenAI Repository](https://github.com/openvinotoolkit/openvino.genai) and [OpenVINO Tokenizers](https://github.com/openvinotoolkit/openvino_tokenizers) - resources and tools for developing and optimizing Generative AI applications.
- [OpenVINO™ Model Server (OVMS)](https://github.com/openvinotoolkit/model_server) - a scalable, high-performance solution for serving models optimized for Intel architectures.
- [Intel® Geti™](https://geti.intel.com/) - an interactive video and image annotation tool for computer vision use cases.
### Integrations
- [🤗Optimum Intel](https://github.com/huggingface/optimum-intel) - grab and use models leveraging OpenVINO within the Hugging Face API.
- [Torch.compile](https://docs.openvino.ai/2025/openvino-workflow/torch-compile.html) - use OpenVINO for Python-native applications by JIT-compiling code into optimized kernels.
- [OpenVINO LLMs inference and serving with vLLM](https://github.com/vllm-project/vllm-openvino) - enhance vLLM's fast and easy model serving with the OpenVINO backend.
- [OpenVINO Execution Provider for ONNX Runtime](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html) - use OpenVINO as a backend with your existing ONNX Runtime code.
- [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/openvino/) - build context-augmented GenAI applications with the LlamaIndex framework and enhance runtime performance with OpenVINO.
- [LangChain](https://python.langchain.com/docs/integrations/llms/openvino/) - integrate OpenVINO with the LangChain framework to enhance runtime performance for GenAI applications.
- [Keras 3](https://github.com/keras-team/keras) - Keras 3 is a multi-backend deep learning framework. Users can switch model inference to the OpenVINO backend using the Keras API.
Check out the [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-openvino) repository to discover a collection of community-made AI projects based on OpenVINO!
## Performance
Explore [OpenVINO Performance Benchmarks](https://docs.openvino.ai/2025/about-openvino/performance-benchmarks.html) to discover the optimal hardware configurations and plan your AI deployment based on verified data.
## Contribution and Support
Check out [Contribution Guidelines](https://github.com/openvinotoolkit/openvino/blob/master/CONTRIBUTING.md) for more details.
Read the [Good First Issues section](https://github.com/openvinotoolkit/openvino/blob/master/CONTRIBUTING.md#3-start-working-on-your-good-first-issue), if you're looking for a place to start contributing. We welcome contributions of all kinds!
You can ask questions and get support on:
* [GitHub Issues](https://github.com/openvinotoolkit/openvino/issues).
* OpenVINO channels on the [Intel DevHub Discord server](https://discord.gg/7pVRxUwdWG).
* The [`openvino`](https://stackoverflow.com/questions/tagged/openvino) tag on Stack Overflow\*.
## Resources
* [Release Notes](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino.html)
* [OpenVINO Blog](https://blog.openvino.ai/)
* [OpenVINO™ toolkit on Medium](https://medium.com/@openvino)
## Telemetry
OpenVINO™ collects software performance and usage data for the purpose of improving OpenVINO™ tools.
This data is collected directly by OpenVINO™ or through the use of Google Analytics 4.
You can opt-out at any time by running the command:
``` bash
opt_in_out --opt_out
```
More Information is available at [OpenVINO™ Telemetry](https://docs.openvino.ai/2025/about-openvino/additional-resources/telemetry.html).
## License
OpenVINO™ Toolkit is licensed under [Apache License Version 2.0](https://github.com/openvinotoolkit/openvino/blob/master/LICENSE).
By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
---
\* Other names and brands may be claimed as the property of others.
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"description": "<div align=\"center\">\n<img src=\"https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/assets/openvino-logo-purple-black.svg\" width=\"400px\">\n\n<h3 align=\"center\">\nOpen-source software toolkit for optimizing and deploying deep learning models.\n</h3>\n\n<p align=\"center\">\n <a href=\"https://docs.openvino.ai/2025/index.html\"><b>Documentation</b></a> \u2022 <a href=\"https://blog.openvino.ai\"><b>Blog</b></a> \u2022 <a href=\"https://docs.openvino.ai/2025/about-openvino/key-features.html\"><b>Key Features</b></a> \u2022 <a href=\"https://docs.openvino.ai/2025/get-started/learn-openvino.html\"><b>Tutorials</b></a> \u2022 <a href=\"https://docs.openvino.ai/2025/documentation/openvino-ecosystem.html\"><b>Integrations</b></a> \u2022 <a href=\"https://docs.openvino.ai/2025/about-openvino/performance-benchmarks.html\"><b>Benchmarks</b></a> \u2022 <a href=\"https://github.com/openvinotoolkit/openvino.genai\"><b>Generative AI</b></a>\n</p>\n\n[](https://badge.fury.io/py/openvino)\n[](https://anaconda.org/conda-forge/openvino)\n[](https://formulae.brew.sh/formula/openvino)\n\n[](https://pepy.tech/project/openvino)\n[](https://anaconda.org/conda-forge/openvino/files)\n[](https://formulae.brew.sh/formula/openvino)\n </div>\n\n\n- **Inference Optimization**: Boost deep learning performance in computer vision, automatic speech recognition, generative AI, natural language processing with large and small language models, and many other common tasks.\n- **Flexible Model Support**: Use models trained with popular frameworks such as PyTorch, TensorFlow, ONNX, Keras, PaddlePaddle, and JAX/Flax. Directly integrate models built with transformers and diffusers from the Hugging Face Hub using Optimum Intel. Convert and deploy models without original frameworks.\n- **Broad Platform Compatibility**: Reduce resource demands and efficiently deploy on a range of platforms from edge to cloud. OpenVINO\u2122 supports inference on CPU (x86, ARM), GPU (Intel integrated & discrete GPU) and AI accelerators (Intel NPU).\n- **Community and Ecosystem**: Join an active community contributing to the enhancement of deep learning performance across various domains.\n\nCheck out the [OpenVINO Cheat Sheet](https://docs.openvino.ai/2025/_static/download/OpenVINO_Quick_Start_Guide.pdf) and [Key Features](https://docs.openvino.ai/2025/about-openvino/key-features.html) for a quick reference.\n\n\n## Installation\n\n[Get your preferred distribution of OpenVINO](https://docs.openvino.ai/2025/get-started/install-openvino.html) or use this command for quick installation:\n\n```sh\npip install -U openvino\n```\n\nCheck [system requirements](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino/system-requirements.html) and [supported devices](https://docs.openvino.ai/2025/documentation/compatibility-and-support/supported-devices.html) for detailed information.\n\n## Tutorials and Examples\n\n[OpenVINO Quickstart example](https://docs.openvino.ai/2025/get-started.html) will walk you through the basics of deploying your first model.\n\nLearn how to optimize and deploy popular models with the [OpenVINO Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)\ud83d\udcda:\n- [Create an LLM-powered Chatbot using OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/llm-chatbot-generate-api.ipynb)\n- [YOLOv11 Optimization](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/yolov11-optimization/yolov11-object-detection.ipynb)\n- [Text-to-Image Generation](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/text-to-image-genai/text-to-image-genai.ipynb)\n- [Multimodal assistant with LLaVa and OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llava-multimodal-chatbot/llava-multimodal-chatbot-genai.ipynb)\n- [Automatic speech recognition using Whisper and OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/whisper-asr-genai/whisper-asr-genai.ipynb)\n\nDiscover more examples in the [OpenVINO Samples (Python & C++)](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples.html) and [Notebooks (Python)](https://docs.openvino.ai/2025/get-started/learn-openvino/interactive-tutorials-python.html).\n\nHere are easy-to-follow code examples demonstrating how to run PyTorch and TensorFlow model inference using OpenVINO:\n\n**PyTorch Model**\n\n```python\nimport openvino as ov\nimport torch\nimport torchvision\n\n# load PyTorch model into memory\nmodel = torch.hub.load(\"pytorch/vision\", \"shufflenet_v2_x1_0\", weights=\"DEFAULT\")\n\n# convert the model into OpenVINO model\nexample = torch.randn(1, 3, 224, 224)\nov_model = ov.convert_model(model, example_input=(example,))\n\n# compile the model for CPU device\ncore = ov.Core()\ncompiled_model = core.compile_model(ov_model, 'CPU')\n\n# infer the model on random data\noutput = compiled_model({0: example.numpy()})\n```\n\n**TensorFlow Model**\n\n```python\nimport numpy as np\nimport openvino as ov\nimport tensorflow as tf\n\n# load TensorFlow model into memory\nmodel = tf.keras.applications.MobileNetV2(weights='imagenet')\n\n# convert the model into OpenVINO model\nov_model = ov.convert_model(model)\n\n# compile the model for CPU device\ncore = ov.Core()\ncompiled_model = core.compile_model(ov_model, 'CPU')\n\n# infer the model on random data\ndata = np.random.rand(1, 224, 224, 3)\noutput = compiled_model({0: data})\n```\n\nOpenVINO supports the CPU, GPU, and NPU [devices](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html) and works with models from PyTorch, TensorFlow, ONNX, TensorFlow Lite, PaddlePaddle, and JAX/Flax [frameworks](https://docs.openvino.ai/2025/openvino-workflow/model-preparation.html). It includes [APIs](https://docs.openvino.ai/2025/api/api_reference.html) in C++, Python, C, NodeJS, and offers the GenAI API for optimized model pipelines and performance.\n\n## Generative AI with OpenVINO\n\nGet started with the OpenVINO GenAI [installation](https://docs.openvino.ai/2025/get-started/install-openvino/install-openvino-genai.html) and refer to the [detailed guide](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai.html) to explore the capabilities of Generative AI using OpenVINO.\n\nLearn how to run LLMs and GenAI with [Samples](https://github.com/openvinotoolkit/openvino.genai/tree/master/samples) in the [OpenVINO\u2122 GenAI repo](https://github.com/openvinotoolkit/openvino.genai). See GenAI in action with Jupyter notebooks: [LLM-powered Chatbot](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-chatbot) and [LLM Instruction-following pipeline](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-question-answering).\n\n## Documentation\n\n[User documentation](https://docs.openvino.ai/) contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications.\n\n[Developer documentation](https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/index.md) focuses on the OpenVINO architecture and describes [building](https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md) and [contributing](https://github.com/openvinotoolkit/openvino/blob/master/CONTRIBUTING.md) processes.\n\n## OpenVINO Ecosystem\n\n### OpenVINO Tools\n\n- [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - advanced model optimization techniques including quantization, filter pruning, binarization, and sparsity.\n- [GenAI Repository](https://github.com/openvinotoolkit/openvino.genai) and [OpenVINO Tokenizers](https://github.com/openvinotoolkit/openvino_tokenizers) - resources and tools for developing and optimizing Generative AI applications.\n- [OpenVINO\u2122 Model Server (OVMS)](https://github.com/openvinotoolkit/model_server) - a scalable, high-performance solution for serving models optimized for Intel architectures.\n- [Intel\u00ae Geti\u2122](https://geti.intel.com/) - an interactive video and image annotation tool for computer vision use cases.\n\n### Integrations\n\n- [\ud83e\udd17Optimum Intel](https://github.com/huggingface/optimum-intel) - grab and use models leveraging OpenVINO within the Hugging Face API.\n- [Torch.compile](https://docs.openvino.ai/2025/openvino-workflow/torch-compile.html) - use OpenVINO for Python-native applications by JIT-compiling code into optimized kernels.\n- [OpenVINO LLMs inference and serving with vLLM\u200b](https://github.com/vllm-project/vllm-openvino) - enhance vLLM's fast and easy model serving with the OpenVINO backend.\n- [OpenVINO Execution Provider for ONNX Runtime](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html) - use OpenVINO as a backend with your existing ONNX Runtime code.\n- [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/openvino/) - build context-augmented GenAI applications with the LlamaIndex framework and enhance runtime performance with OpenVINO.\n- [LangChain](https://python.langchain.com/docs/integrations/llms/openvino/) - integrate OpenVINO with the LangChain framework to enhance runtime performance for GenAI applications.\n- [Keras 3](https://github.com/keras-team/keras) - Keras 3 is a multi-backend deep learning framework. Users can switch model inference to the OpenVINO backend using the Keras API.\n\nCheck out the [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-openvino) repository to discover a collection of community-made AI projects based on OpenVINO!\n\n## Performance\n\nExplore [OpenVINO Performance Benchmarks](https://docs.openvino.ai/2025/about-openvino/performance-benchmarks.html) to discover the optimal hardware configurations and plan your AI deployment based on verified data.\n\n## Contribution and Support\n\nCheck out [Contribution Guidelines](https://github.com/openvinotoolkit/openvino/blob/master/CONTRIBUTING.md) for more details.\nRead the [Good First Issues section](https://github.com/openvinotoolkit/openvino/blob/master/CONTRIBUTING.md#3-start-working-on-your-good-first-issue), if you're looking for a place to start contributing. We welcome contributions of all kinds!\n\nYou can ask questions and get support on:\n\n* [GitHub Issues](https://github.com/openvinotoolkit/openvino/issues).\n* OpenVINO channels on the [Intel DevHub Discord server](https://discord.gg/7pVRxUwdWG).\n* The [`openvino`](https://stackoverflow.com/questions/tagged/openvino) tag on Stack Overflow\\*.\n\n\n## Resources\n\n* [Release Notes](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino.html)\n* [OpenVINO Blog](https://blog.openvino.ai/)\n* [OpenVINO\u2122 toolkit on Medium](https://medium.com/@openvino)\n\n\n## Telemetry\n\nOpenVINO\u2122 collects software performance and usage data for the purpose of improving OpenVINO\u2122 tools.\nThis data is collected directly by OpenVINO\u2122 or through the use of Google Analytics 4.\nYou can opt-out at any time by running the command:\n\n``` bash\nopt_in_out --opt_out\n```\n\nMore Information is available at [OpenVINO\u2122 Telemetry](https://docs.openvino.ai/2025/about-openvino/additional-resources/telemetry.html).\n\n## License\n\nOpenVINO\u2122 Toolkit is licensed under [Apache License Version 2.0](https://github.com/openvinotoolkit/openvino/blob/master/LICENSE).\nBy contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.\n\n---\n\\* Other names and brands may be claimed as the property of others.\n",
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