Overview
ONNX Runtime (ORT) is a high-performance engine designed to accelerate machine learning models across a vast spectrum of hardware and operating systems. Originally developed by Microsoft, it serves as the industry-standard execution engine for models exported in the Open Neural Network Exchange (ONNX) format. By 2026, ORT has solidified its position as the critical middleware between high-level frameworks like PyTorch or TensorFlow and hardware-specific accelerators. Its architecture utilizes Execution Providers (EPs) to interface with hardware-specific libraries such as NVIDIA CUDA, TensorRT, Intel OpenVINO, and Apple CoreML. This modularity allows developers to 'write once, deploy anywhere' without sacrificing performance. Beyond inference, ORT Training enables accelerated distributed training on edge devices and in the cloud. With the rise of Generative AI, ORT has evolved to include specific optimizations for Large Language Models (LLMs) via DirectML and specialized kernel fusions, making it the preferred choice for local LLM execution in browser environments (WebAssembly) and mobile applications. Its 2026 market position is defined by its ubiquity in production-grade AI pipelines where latency, throughput, and hardware flexibility are non-negotiable requirements.