Model components / Inference code

ONNX Runtime

ONNX Runtime is a cross-platform accelerator for machine learning inference and training, running models exported from PyTorch, TensorFlow/Keras, and classical ML libraries via the ONNX interchange format. It executes on CPUs, GPUs, NPUs, and custom accelerators through hardware-specific execution providers that target NVIDIA, AMD, Intel, Qualcomm, and Apple silicon. Microsoft develops it and uses it in production across Windows, Office, and Azure.

Verified 2026-08-09 via GitHub, the LICENSE body, and PyPI download statistics.

Openness

5 high confidence

5.0

license: MIT(OSI)
source: public(Microsoft)
core-gated: ungated

Fully MIT-licensed open source from Microsoft, with no feature-gated tier.

https://github.com/microsoft/onnxruntime/blob/main/LICENSE recorded 2026-08-09
MIT License, Copyright (c) Microsoft Corporation, with standard MIT permission and warranty-disclaimer text
https://github.com/microsoft/onnxruntime recorded 2026-08-09
"This project is licensed under the MIT License"; repo description "ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator"; single public repository with no separate paid or feature-gated tier referenced

Adoption

5 high confidence

5.0

onnxruntime records 86.9 million PyPI downloads in the trailing month, comfortably inside the >10M band, level 5 on the software scale.

https://pypistats.org/packages/onnxruntime recorded 2026-08-09
Downloads last month: 86,893,228

Capability

4 medium confidence

4.0

Unusually broad execution-provider coverage across CPU, GPU, IoT, edge, mobile and cloud targets, taken from the project's published summary of those providers. Scored 4 rather than 5 because the breadth is real but the purpose-built serving engines hold the frontier for transformer throughput. The one MLPerf row that mentions ONNX Runtime is an MLCommons continuous-integration test submission in the open division, entered by MLCommons itself, so it is not a benchmark result for this product and the score rests on the feature matrix instead.

https://onnxruntime.ai/docs/execution-providers/ recorded 2026-08-09
"Summary of supported Execution Providers" table listing "NVIDIA TensorRT", "Qualcomm QNN", "AMD MIGraphX" and "Android Neural Networks API" among CPU, GPU, IoT/Edge/Mobile and Other targets.

Verified 2026-08-09