onnxruntime-openvino
ONNX Runtime is a runtime accelerator for Machine Learning models
Decision gist · record as of 2026-08-14
Yes, if you run ONNX models on Intel hardware and want to accelerate inference with minimal code changes. The permissive MIT license, active maintenance, and inclusion of prebuilt OpenVINO libraries on Linux reduce friction. Windows users should verify OpenVINO installation requirements before committing. No known security vulnerabilities as of the query date.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Windows requires separate OpenVINO PyPI package installation; Linux wheels include OpenVINO 2025.4.1.
- Requires Python 3.10 or later; Ubuntu 18.04+ or Windows 10+ (64-bit).
- Medium install friction with prebuilt OpenVINO libraries on Linux wheels (version 2025.4.1 included); Windows requires separate OpenVINO installation.
License · maintenance · safety
MIT License (permissive) — MIT License (permissive) allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
last release 2026-02-26 (169 days) · last repo commit 2026-08-14 · 21,381 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 98,109 downloads/mo, #13,108 on PyPI
Alternatives
Verify before relying
pip install onnxruntime-openvino
import onnxruntime as rt
sess = rt.InferenceSession('model.onnx', providers=['OpenVINOExecutionProvider'])- Performance improvement magnitude across different Intel hardware types and model architectures
- Compatibility with specific ONNX opset versions or model complexity limits
- Whether discrete GPU support requires additional drivers or system configuration beyond standard Intel GPU drivers
What it is and what it does
onnxruntime-openvino is an execution provider plugin for ONNX Runtime that routes inference workloads to Intel hardware accelerators. It integrates OpenVINO inline optimizations into ONNX Runtime with minimal code changes—typically a single provider argument—enabling developers to accelerate ONNX models across Intel CPUs, integrated GPUs, discrete GPUs, and integrated NPUs without rewriting inference logic.
The package ships with prebuilt OpenVINO libraries on Linux (version 2025.4.1), eliminating separate installation steps there; Windows users must install OpenVINO separately. It supports Python 3.11–3.14 on 64-bit Linux and Windows, and depends on flatbuffers, numpy, packaging, protobuf, and sympy for runtime operation.
Use it for
- Accelerate ONNX model inference on Intel CPUs or integrated GPUs without changing application code
- Deploy ML models to Intel discrete GPU hardware for higher throughput inference workloads
- Route inference to Intel integrated NPUs on supported platforms to reduce power consumption
- Optimize existing ONNX Runtime applications for Intel hardware with minimal refactoring
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run ONNX models on Intel hardware and want to accelerate inference with minimal code changes.
The permissive MIT license, active maintenance, and inclusion of prebuilt OpenVINO libraries on Linux reduce friction. Windows users should verify OpenVINO installation requirements before committing. No known security vulnerabilities as of the query date.
Install
onnxruntime-openvino on PyPI
Before you install
Medium install friction with prebuilt OpenVINO libraries on Linux wheels (version 2025.4.1 included); Windows requires separate OpenVINO installation. Active maintenance with recent commits; supports Python 3.11–3.14.
Windows requires separate OpenVINO PyPI package installation; Linux wheels include OpenVINO 2025.4.1. Requires Python 3.10 or later; Ubuntu 18.04+ or Windows 10+ (64-bit).
License in practice
MIT License (permissive) allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
Quickstart
pip install onnxruntime-openvino
import onnxruntime as rt
sess = rt.InferenceSession('model.onnx', providers=['OpenVINOExecutionProvider'])Verify before relying
- Performance improvement magnitude across different Intel hardware types and model architectures
- Compatibility with specific ONNX opset versions or model complexity limits
- Whether discrete GPU support requires additional drivers or system configuration beyond standard Intel GPU drivers
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 5 packagesflatbuffersnumpypackagingprotobufsympy |
| Maintenance | Actively maintained 169 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 98,109 / month, #13,108 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: onnxruntime_openvino-1.24.1-cp311-cp311-manylinux_2_28_x86_64.whl; onnxruntime_openvino-1.24.1-cp311-cp311-win_amd64.whl; onnxruntime_openvino-1.24.1-cp312-cp312-manylinux_2_28_x86_64.whl; onnxruntime_openvino-1.24.1-cp312-cp312-win_amd64.whl; onnxruntime_openvino-1.24.1-cp313-cp313-manylinux_2_28_x86_64.whl; onnxruntime_openvino-1.24.1-cp313-cp313-win_amd64.whl
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See also cosmos-xenna · onnxruntime_extensions · onnxruntime-genai · openvino · onnxruntime-gpu · optimum-intel · onnxruntime · optimum · openvino-dev · sit4onnx