Day: 20 January 2026

In Part 1 of this series, we explored what ONNX is, why it was created, and how it decouples model training from deployment by providing a standardized, framework-agnostic model format. That foundation is essential for understanding the next step in the ONNX ecosystem.

In this article, we focus on ONNX Runtime—the execution engine that brings ONNX models to life. You will learn how ONNX Runtime loads and optimizes ONNX models, selects the appropriate execution provider, and runs inference efficiently across CPUs, GPUs, and NPUs.

ONNX (Open Neural Network Exchange) is an open standard designed to make machine learning models portable and interoperable across frameworks, tools, and hardware platforms. Its primary purpose is to decouple model training from model deployment, allowing models trained in popular frameworks such as PyTorch or TensorFlow to be exported into a common format and executed efficiently in different production environments.
This blog post is structured into two main parts. Part 1 focuses on ONNX, providing an overview of the standard, its design goals, and its role in enabling model portability and interoperability across machine learning frameworks. Part 2 covers ONNX Runtime, examining how ONNX models are executed in production, with an emphasis on performance optimization, hardware acceleration, and deployment considerations.