Download ONNX Runtime – High-Performance AI Model Inference Engine

ONNX Runtime is a cross-platform, high-performance inference engine for machine learning models developed by Microsoft. It is designed to accelerate AI inference across a wide variety of hardware — from cloud servers and edge devices to mobile and embedded systems — using the open ONNX (Open Neural Network Exchange) format.

By supporting models trained in frameworks such as PyTorch, TensorFlow, scikit-learn, and many others, ONNX Runtime lets developers deploy optimized AI models without being locked into a specific framework. It leverages hardware-specific accelerators including CUDA, TensorRT, DirectML, OpenVINO, and NNAPI to squeeze maximum performance from every platform.

ONNX Runtime is used in production by Microsoft across products like Office, Azure, and Windows, and is available as an open-source project with strong community support. It is ideal for developers who need fast, efficient, and portable AI model deployment.

Key Features

• Accelerates inference for ONNX models trained in PyTorch, TensorFlow, Keras, and other frameworks
• Supports hardware acceleration via CUDA, TensorRT, DirectML, OpenVINO, CoreML, and NNAPI
• Cross-platform — runs on Windows, Linux, macOS, Android, iOS, and WebAssembly
• Python, C++, C#, Java, and JavaScript APIs for flexible integration
• Quantization and graph optimization tools to reduce model size and boost speed
• Training API available for fine-tuning and incremental learning scenarios
• Production-proven at scale in Microsoft Azure and Office products

How to Install

1. Click the download button below to get the ONNX Runtime package for your platform.
2. For Python, install via pip: pip install onnxruntime (or onnxruntime-gpu for CUDA support).
3. For C++ or other languages, download the prebuilt binaries from the releases page and link against your project.
4. Load your ONNX model using the InferenceSession API and run inference with your input data.
5. Optionally configure execution providers (CUDA, TensorRT, etc.) to enable hardware acceleration.