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ONNX Runtime

ONNX Runtime is a cross-platform inference engine (machine learning framework) for deploying Machine Learning (ML) models in the Open Neural Network (NN) Exchange (ONNX) format across cloud, edge, and client environments.

  • High-performance ONNX model inference across Central Processing Unit (CPU), Graphics Processing Unit (GPU), and specialized accelerators (machine learning deployment)
  • Support for multiple execution providers including hardware-optimized backends from Microsoft and partners (inference optimization)
  • APIs for C, C++, C#, Python, Java, JavaScript, and other languages for integration into applications and services (developer Software Development Kit (SDK))
  • Optimizations for deep learning, classical ML, and transformer-based models in training and inference scenarios (AI/ML workloads)
  • Deployment in Windows, Linux, macOS, mobile, and edge platforms, including integration with Azure services and Windows Artificial Intelligence (AI) features (cloud and edge AI)

More About ONNX Runtime

ONNX Runtime is an inference engine (machine learning framework) created by Microsoft to execute models defined in the Open NN Exchange (ONNX) format. It addresses the need for a consistent, high-performance runtime that can run trained models across diverse hardware and operating systems while decoupling model training frameworks from deployment environments.

The project focuses on efficient model execution for production workloads (machine learning deployment). It provides an execution engine that supports operators and graph optimizations defined by the ONNX standard. ONNX Runtime is designed to run models on CPUs, GPUs, and hardware accelerators through pluggable execution providers (hardware acceleration), enabling integration with vendor-specific libraries and device runtimes. This design allows organizations to target different hardware without modifying ONNX model definitions.

ONNX Runtime exposes language bindings and SDKs for C, C++, C#, Python, Java, JavaScript, and other supported languages (developer SDK), which enables embedding inference into backend services, desktop applications, mobile apps, and web applications. It supports deployment on Windows, Linux, and macOS, as well as mobile and edge platforms such as Android and iOS where supported (cross-platform runtime). The runtime can be integrated into containerized applications and cloud services, and Microsoft documents usage with Azure services and Windows AI capabilities (cloud and edge AI).

The project includes optimizations for deep learning models such as convolutional neural networks and transformer architectures, as well as support for classical ML models converted to ONNX (AI/ML workloads). It provides graph-level optimizations, operator fusion, and execution planning (performance optimization), helping enterprises reduce inference latency and resource utilization. For some workloads, ONNX Runtime also supports training-related scenarios such as accelerated training and fine-tuning in compatible environments, as documented by Microsoft.

In enterprise environments, ONNX Runtime is used to standardize the deployment layer for models trained in different frameworks that export to ONNX (interoperability). This allows teams to train models in various tools and then deploy through a single runtime integrated with existing Continuous Integration and Continuous Deployment (CI/CD), observability, and infrastructure automation stacks. The extensible execution provider model allows hardware and cloud vendors to plug in optimized backends, and organizations can select providers that align with their infrastructure strategy.

From a directory and taxonomy perspective, ONNX Runtime fits into categories such as ML inference engine, cross-platform AI runtime, and model deployment framework. It is closely associated with the ONNX specification for model interchange and is documented by Microsoft as a core component for running ONNX models efficiently across cloud, edge, and client scenarios.