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ONNX Runtime is a high-performance inference engine for deploying machine learning models.

Key points about ONNX Runtime

1. Open standard: ONNX Runtime is built to support the Open Neural Network Exchange (ONNX) format, an open standard for representing machine learning models.

2. Cross-platform compatibility: It allows models from various frameworks like TensorFlow, PyTorch, scikit-learn, and others to be exported to ONNX format and run on different platforms and devices.

3. Performance optimization: ONNX Runtime is optimized for both cloud and edge deployments, working across Linux, Windows, and Mac. It provides significant performance gains, with Microsoft services reporting an average 2x performance improvement on CPU.

4. Language support: While written in C++, ONNX Runtime offers APIs for C, Python, C#, Java, and JavaScript (Node.js), enabling usage in diverse environments.

5. Hardware acceleration: It integrates with various hardware accelerators like NVIDIA GPUs (via TensorRT), Intel processors (via OpenVINO), and Windows DirectML.

6. Deployment options: ONNX Runtime can be deployed in various scenarios, including cloud services, edge devices, and even directly in web browsers using ONNX Runtime Web.

7. Model optimization: It includes features for optimizing model performance, such as precision calibration, quantization, and layer fusion.

8. Wide adoption: ONNX Runtime is used in high-scale Microsoft services like Bing, Office, and Azure AI, as well as in Windows Machine Learning, Azure SQL, and ML.NET.

9. Execution Providers: ONNX Runtime uses an extensible Execution Providers (EP) framework to work with different hardware acceleration libraries, optimizing model execution for specific hardware platforms.

ONNX Runtime aims to provide a unified, high-performance solution for deploying machine learning models across a wide range of platforms and devices, making it easier for developers to optimize and scale their AI applications.