The [Open Neural Network Exchange](http://onnx.ai/) (ONNX) is an open standard for representing machine learning models. The biggest advantage of ONNX is that it allows interoperability across different open source AI frameworks, which itself offers more flexibility for AI frameworks adoption.
With ONNX Runtime Web, web developers can score models directly on browsers with various benefits including reducing server-client communication and protecting user privacy, as well as offering install-free and cross-platform in-browser ML experience.
ONNX Runtime Web can run on both CPU and GPU. On CPU side, [WebAssembly](https://developer.mozilla.org/en-US/docs/WebAssembly) is adopted to execute the model at near-native speed. ONNX Runtime Web compiles the native ONNX Runtime CPU engine into WebAssembly backend by using Emscripten, so it supports most functionalities native ONNX Runtime offers, including full ONNX operator coverage, multi-threading, [ONNX Runtime Quantization](https://www.onnxruntime.ai/docs/how-to/quantization.html) as well as [ONNX Runtime Mobile](https://onnxruntime.ai/docs/tutorials/mobile/). For performance acceleration with GPUs, ONNX Runtime Web leverages WebGL, a popular standard for accessing GPU capabilities. We are keeping improving op coverage and optimizing performance in WebGL backend.
ONNX Runtime Web currently support all operators in [ai.onnx](https://github.com/onnx/onnx/blob/main/docs/Operators.md) and [ai.onnx.ml](https://github.com/onnx/onnx/blob/main/docs/Operators-ml.md).
ONNX Runtime Web currently supports a subset of operators in [ai.onnx](https://github.com/onnx/onnx/blob/main/docs/Operators.md) operator set. See [webgl-operators.md](./docs/webgl-operators.md) for a complete, detailed list of which ONNX operators are supported by WebGL backend.
WebGPU backend is still an experimental feature. See [webgpu-operators.md](./docs/webgpu-operators.md) for a detailed list of which ONNX operators are supported by WebGPU backend.
WebNN backend is still an experimental feature. See [webnn-operators.md](./docs/webnn-operators.md) for a detailed list of which ONNX operators are supported by WebNN backend.
