Training and inference usually live in separate worlds. Models are typically trained in Python then exported to an open format like ONNX or optimized for production engines like vLLM, ONNX Runtime, or TensorRT. This export step is often brittle and lossy, ruling out complex architectures and advanced deployment use cases. Burn unifies the two. By executing multi-platform tensor operations via a si

