> ONNX converts models while Carton wraps them. Carton uses the underlying framework (e.g. PyTorch) to actually execute a model under the hood. This is important because it makes it easy to use custom ops, TensorRT, etc without changes. For some sophisticated models, "conversion" steps (e.g. to ONNX) can be problematic and require additional validation. By removing these conversion steps, Carton enables faster experimentation, deployment, and iteration.

> With that said, we plan to support ONNX models within Carton. This lets you use ONNX if you choose and it enables some interesting use cases (like running models in-browser with WASM).

More broadly, Carton can compose with other interesting technologies in ways ONNX isn't able to because ONNX is an inference engine while Carton is an abstraction layer.