JAX vs ONNX Runtime
JAX
docs.jax.devONNX Runtime
onnxruntime.aiSide by side
| Figure | JAX | ONNX Runtime |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.3 (#7) | 8.4 (#6) |
| Integrations | The documentation lists ecosystem libraries including Flax, Keras, Optax, TensorFlow Datasets, Hugging Face Datasets, NumPyro, and PyMC. | The ecosystem documentation lists integrations with Azure Machine Learning, Azure Custom Vision, Azure SQL Edge, Azure Synapse Analytics, ML.NET, and NVIDIA Triton Inference Server. |
| Platforms | The installation guide lists CPU support for Linux, macOS and Windows, with accelerator support varying by operating system and hardware. | The site says ONNX Runtime runs on Linux, Windows, Mac, iOS, Android, and web browsers. |
| Support | The installation guide directs users with problems using prebuilt wheels to the JAX GitHub issue tracker. | Documentation questions are directed to issue filing, and the project invites users to report bugs, suggest features, and submit pull requests on GitHub. |
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.