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https://github.com/openvinotoolkit/openvino

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
https://github.com/openvinotoolkit/openvino

ai computer-vision deep-learning deploy-ai diffusion-models generative-ai good-first-issue inference llm-inference natural-language-processing nlp openvino optimize-ai performance-boost recommendation-system speech-recognition stable-diffusion transformers yolo

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

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README 

        







Open-source software toolkit for optimizing and deploying deep learning models.






 Documentation • Blog • Key Features • Tutorials • Integrations • Benchmarks • Generative AI




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- **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 (OpenCL capable, integrated and discrete) 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/system-requirements.html) and [supported devices](https://docs.openvino.ai/2025/about-openvino/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/learn-openvino/openvino-samples.html) and [Notebooks (Python)](https://docs.openvino.ai/2025/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/generative-inference.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/blob/latest/notebooks/llm-chatbot/README.md) and [LLM Instruction-following pipeline](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-question-answering/README.md).



## 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](./docs/dev/index.md) focuses on the OpenVINO architecture and describes [building](./docs/dev/build.md)  and [contributing](./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://docs.vllm.ai/en/stable/getting_started/openvino-installation.html) - 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](./CONTRIBUTING.md) for more details.

Read the [Good First Issues section](./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](LICENSE).

By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.



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