onnxruntime

It accepts models exported to ONNX format from PyTorch, TensorFlow, scikit-learn, and other frameworks, then applies framework-agnostic graph optimizations (constant folding, operator fusion, memory layout optimization) and hardware-specific acceleration to produce inference throughput that often significantly exceeds the native framework's inference performance.

The runtime supports hardware execution providers that route computation to specialized accelerators: CUDA and TensorRT for NVIDIA GPUs, DirectML for Windows GPU hardware, OpenVINO for Intel processors and integrated graphics, CoreML for Apple Silicon, NNAPI for Android devices, and QNN for Qualcomm hardware.

This provider architecture allows the same ONNX model to be deployed across diverse hardware targets without code changesaccelerating inference on whatever hardware is available in the deployment environment through the appropriate execution provider.

ML engineers optimizing model inference latency for production services, mobile developers deploying models on Android and iOS without framework dependencies, edge AI engineers running models on embedded hardware and IoT devices, and platform teams standardizing inference infrastructure across heterogeneous hardware fleets use ONNX Runtime.

Its position as the execution engine underlying several Microsoft AI productsAzure ML, Windows ML, Edge Impulseand its adoption by Hugging Face's Optimum library for efficient transformer inference have made it one of the most widely deployed model inference engines in production AI systems.