Model portability is not just saving weights; another runtime must preserve enough graph semantics to execute the model correctly. ONNX gives the AI stack a shared contract between training frameworks, deployment runtimes, and hardware vendors.
What Sets It Apart
It specifies an extensible computation graph, built-in operators, and standard data types, with a strong focus on inference. That makes it a practical baseline for model export, compatibility tests, and hardware-specific optimization paths.
Who Should Use It
Great fit if you move models between training frameworks, inference runtimes, and accelerator toolchains. Look elsewhere if your workflow relies on dynamic framework behavior that cannot be captured cleanly.
