ONNX Runtime
ONNX Runtime is a runtime maintained by Microsoft215. Its source code is public1 under the MIT License21.
ONNX Runtime is Microsoft's open-source, cross-platform engine for running and accelerating machine-learning models in the ONNX format. It runs models exported from frameworks such as PyTorch and TensorFlow/Keras and from classical libraries such as scikit-learn, LightGBM, and XGBoost, applying graph optimizations and hardware-specific execution providers; it also offers accelerated training for transformer models on NVIDIA GPUs.15
- Repository: Repository (external site: github.com)
- Documentation: Documentation (external site: onnxruntime.ai)
- License: LICENSE (MIT) (external site: github.com)
- Release notes: Releases (external site: github.com)
Availability and license
Overall availability
Documented as available to the general public. Access conditions and license terms may still apply.13
Availability is separate from permission: read the license before using or redistributing.
The README states that the project may collect usage data and send it to Microsoft, and refers to the repository's privacy statement for details.1
Component reuse rights
- weights
- Unknown — no complete fact-level rights review
- code
- Unknown — no complete fact-level rights review
- data
- Unknown — no complete fact-level rights review
- documentation
- Unknown — no complete fact-level rights review
No complete system-rights review is recorded for this release.
Public materials checklist
| Item | Status | Notes and evidence |
|---|---|---|
| Source codeIs the source code publicly readable? | Public | 1 |
| DocumentationIs user documentation published? | Public | Usage documentation and tutorials are published at onnxruntime.ai/docs.13 |
| InstallationAre installation instructions or packages publicly available? | Public | Packages are published for pip, NuGet, npm, Maven Central, and CocoaPods, with separate GPU builds (CUDA, DirectML) where applicable.3 |
| Supported platformsAre supported operating systems or hardware documented? | Public | Windows, Linux, macOS, Android, iOS, and web browsers; execution providers cover NVIDIA CUDA and TensorRT, Intel OpenVINO and oneDNN, DirectML, Qualcomm QNN, Apple Core ML, and WebGPU.34 |
| Release statusAre versioned releases published? | Public | Versioned releases are published on GitHub and PyPI; version 1.30.0 was released on PyPI on September 10, 2026.15 |
What it is useful for
Deploying trained models for inference on servers, desktops, mobile devices, and in web browsers, with bindings for Python, C#, C/C++, Java, JavaScript, Objective-C, and Kotlin.3
Organization context
In the news
ONNX Runtime CUDA Plugin EP 0.3.0 adds sparse attention and INT2 QMoE support
On October 9, 2026 Microsoft published version 0.3.0 of the ONNX Runtime CUDA Plugin EP on the ONNX Runtime GitHub repository. This is a separate release from ONNX Runtime 1.31.0, which the notes give as the matched core version. The release notes list sparse attention and packed continuous-batching support, INT2 and mixed-width QMoE execution, new QSA/CSA attention operators, expanded paged speculative decoding, and opt-in MatMul auto-tuning. The notes warn that the core and the plugin must use the same contributed-operator schemas and recommend building both from the same ONNX Runtime revision. USASI has not tested this release.
ONNX Runtime 1.31.0 released with stable model-package and EPContext data APIs
On October 9, 2026 Microsoft published ONNX Runtime 1.31.0 on GitHub. The release notes say the model-package API and EPContext data callbacks were promoted to stable C and C++ APIs, and that CoreML can now take part in device-based execution-provider selection, including Apple Neural Engine selection through
PREFER_NPU. The notes also announce that the ACL execution provider is deprecated, that out-of-range indices inGatherBlockQuantizednow produce zero output slices, and that supported native builds now default to 1DS telemetry, which can be disabled with--no_telemetry. The ONNX dependency stays at 1.22.0. USASI has not tested these changes.ONNX Runtime 1.30.0 adds Go bindings and expands CUDA and WebGPU generative AI support
Microsoft's ONNX Runtime project published version 1.30.0 on GitHub on September 10, 2026. The release notes highlight Go bindings for the ONNX Runtime C API, expanded CUDA inference support including speculative decoding in paged XQA and INT4 paged KV caches, WebGPU PagedAttention improvements with GPT-OSS support, and fused CPU LinearAttention kernels for AVX-512, Arm64 NEON, and SVE. The notes say FP4 QMoE kernels are now enabled by default in CUDA builds, and they list hardening of model loading and input validation. USASI has not tested this release.
U.S. eligibility
Eligible · basis: U.S. headquarters
Microsoft Corporation holds the copyright, publishes the project in its microsoft GitHub organization, and is listed as the package author on PyPI. Microsoft Corporation lists Redmond, Washington as its address on the cover page of its Form 10-K for fiscal year 2026.2156
Sources
This listing is not an endorsement, a safety assessment, or a federal approval.
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APA style
USASI. (2026, September 29). ONNX Runtime. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/onnx-runtime/
BibTeX
@misc{usasi_onnx_runtime,
author = {{USASI}},
title = {ONNX Runtime},
year = {2026},
month = sep,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/onnx-runtime/}},
note = {United States of America Superintelligence. Last updated 2026-09-29}
}The date is when this page was last updated. Add the date you read it if your style needs one.
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