ONNX Runtime vs PyTorch
ONNX Runtime
onnxruntime.aiPyTorch
pytorch.orgSide by side
| Figure | ONNX Runtime | PyTorch |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | Yes |
| Android | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Deep Learning Software | 8.4 (#6) | 7.3 (#29) |
| Languages | The site lists support for Python, C#, C++, Java, JavaScript, and Rust, among other languages. | PyTorch offers Python and C++ front ends, and the installer lists Python and C++/Java language choices. |
| Purpose | ONNX Runtime is a cross-platform machine-learning model accelerator with interfaces for hardware-specific libraries. | PyTorch enables fast, flexible experimentation and efficient production through a user-friendly front end, distributed training, and an ecosystem of tools and libraries. |
| Support | Documentation questions are directed to issue filing, and the project invites users to report bugs, suggest features, and submit pull requests on GitHub. | The Foundation directs users with technical questions to the PyTorch discussion community. |
Plans
In their own words
ONNX Runtime
ONNX Runtime is a free engine for machine-learning inference and training within existing technology stacks. It can run models from frameworks including PyTorch, TensorFlow/Keras, TFLite, and scikit-learn. For inference, it optimizes latency, throughput, memory use, and binary size through graph transformations, accelerator-aware partitioning, and optimized computation kernels. Its Execution Providers connect ONNX models to hardware acceleration libraries for CPUs, GPUs, FPGAs, and specialized NPUs. Listed options include NVIDIA CUDA and TensorRT, Intel OpenVINO, DirectML, Qualcomm QNN, Android NNAPI, Apple CoreML, and WebGPU. Deployment spans cloud servers, edge and mobile devices, and browsers; its web runtime supports browser models and its mobile runtime supports Android and iOS. Supported languages include Python, C#, C++, Java, JavaScript, and Rust. Developers can create smaller web or mobile packages by including only the operators and opsets their models need. The open-source runtime is available on Android, iOS, Linux, macOS, self-hosted environments, web, and Windows.