ONNX Runtime is a high-performance inference engine, open sourced by Microsoft, that executes models saved in ONNX, the Open Neural Network Exchange format. ONNX is an open standard for describing a trained model's computation graph, so a model built in PyTorch, TensorFlow, or scikit-learn can be exported once and then run anywhere ONNX Runtime does: Windows, Linux, macOS, mobile devices, browsers, and a wide range of hardware through pluggable execution providers for CUDA, TensorRT, Intel's OpenVINO, and others.
The value is decoupling training from deployment. Without a standard interchange format, the framework a data science team trained in dictates the production stack, and every deployment target needs its own conversion work. With ONNX, the model becomes a portable artifact, and the runtime handles hardware-specific optimization: graph simplification, operator fusion, and support for quantized models that run faster and smaller. In practice ONNX Runtime often delivers substantially better throughput and latency than serving a model from its original training framework, which translates directly into lower serving cost. It is widely used for computer vision, speech, and tabular models, and also serves transformer models, though the largest LLMs are more often deployed on specialized engines such as vLLM or TensorRT-LLM.
At arosplatforms ONNX export is a standard step when we take client models from notebook to production, especially for edge inference on constrained or heterogeneous hardware. We benchmark the exported model against the original for both speed and output fidelity, so the optimization gain never comes with a silent accuracy loss.