ONNX Runtime
By Microsoft
ONNX Runtime is a cross-platform inference and training engine for executing machine learning models in the ONNX format. Developed by Microsoft and released as open source, it runs models across a wide range of hardware and operating…
Definition
ONNX Runtime is a cross-platform inference and training engine for executing machine learning models in the ONNX format. Developed by Microsoft and released as open source, it runs models across a wide range of hardware and operating systems, applying graph optimizations and hardware-specific execution providers to accelerate performance beyond what a naive graph interpreter would achieve on the same model.
Overview
Having a standard model format like ONNX solves the interoperability problem of moving models between tools, but a format alone does not execute anything; something has to actually run the graph efficiently on real hardware. ONNX Runtime is Microsoft's answer to that need: an execution engine purpose-built to load ONNX models and run them as fast as possible across CPUs, GPUs, and specialized accelerators without requiring the original training framework to be present. Mechanically, ONNX Runtime parses an ONNX model's computation graph, applies a series of graph-level optimizations such as operator fusion and constant folding to reduce redundant computation, and then dispatches execution through a pluggable system of "execution providers," backends tuned for specific hardware, including CPU, NVIDIA GPUs via CUDA or TensorRT, and various vendor-specific accelerators. This lets the same ONNX model run efficiently on very different hardware simply by selecting the appropriate execution provider at load time, without changing the model itself or re-exporting it for each target. Where ONNX is the neutral format, ONNX Runtime is one of several engines that can execute it, sitting alongside TensorRT, which optimizes specifically for NVIDIA GPUs, and general serving platforms like Triton, which can use ONNX Runtime as one of its backends. ONNX Runtime differentiates itself with broad hardware support and being framework- and platform-agnostic, running on servers, desktops, mobile devices, and web browsers via its WebAssembly build. In practice, ONNX Runtime is used to deploy models exported from PyTorch or TensorFlow into production services, to run inference on edge devices and mobile applications where a full training framework would be too heavy, and inside larger serving platforms as one of several supported execution backends for ONNX models, often as the default choice before reaching for a more specialized engine. It is also commonly used in scenarios where a single codebase needs to target several deployment environments, from a cloud server down to a resource-constrained edge device, without maintaining separate execution paths for each. Limitations include that achieving maximum performance on a specific hardware target, such as the newest NVIDIA GPUs, sometimes requires a dedicated engine like TensorRT rather than ONNX Runtime's more general execution providers, and that any model relying on ONNX operators without a corresponding optimized kernel for a given execution provider may fall back to slower generic execution for that portion of the graph, which can be difficult to diagnose without profiling tools built for this purpose.
Key Features
- Executes machine learning models exported in the ONNX format
- Applies graph-level optimizations like operator fusion and constant folding
- Supports pluggable execution providers for CPU, GPU, and accelerators
- Runs across servers, desktops, mobile devices, and web browsers
- Is developed and maintained by Microsoft as open source
- Integrates as a backend within platforms like Triton Inference Server
- Supports both inference and, in some configurations, training acceleration
Use Cases
Alternatives
Frequently Asked Questions
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