ONNX Runtime

ONNX Runtime is a high-performance inference engine developed by Microsoft for running machine learning models. It supports models exported in the ONNX (Open Neural Network Exchange) format — an open standard that enables models trained in one framework (PyTorch, TensorFlow, scikit-learn) to run in any ONNX-compatible runtime. The runtime applies automatic optimisations to models, including graph optimisation (simplifying the model's computational graph), operator fusion (combining multiple operations into single efficient operations), and hardware-specific acceleration (utilising GPU, CPU SIMD instructions, or specialised AI accelerators). ONNX Runtime supports multiple execution providers — backend implementations optimised for specific hardware. This includes CUDA for NVIDIA GPUs, DirectML for Windows GPUs, OpenVINO for Intel hardware, and CoreML for Apple devices. This portability allows the same model to run efficiently across different deployment targets.

ONNX Runtime addresses a practical deployment challenge: models are often trained using one framework but need to run on different hardware and platforms in production. The ONNX standard and runtime decouple model training from deployment, giving organisations flexibility in their production infrastructure. Performance improvements from ONNX Runtime can be substantial — 2-5x speedup over default framework inference is common, with some models seeing 10x or greater improvements. This translates directly to lower latency, higher throughput, and reduced compute costs. For organisations deploying models at the edge (on devices, in browsers, or on IoT hardware), ONNX Runtime's cross-platform support is particularly valuable. A single model can be optimised and deployed across cloud servers, desktop applications, mobile devices, and web browsers.