WebGPU is a new web graphics and computing API that provides the underlying infrastructure for running machine learning inference in the browser by leveraging the parallel computing power of the GPU more efficiently. 1. WebGPU improves parallel computing efficiency, making ML core tasks such as matrix operations faster execution; 2. It provides a cross-platform unified execution environment to ensure consistency on different devices; 3. Reduces dependence on JavaScript, moves more logic to the GPU side, and reduces the burden on the main thread; 4. Currently, projects such as TensorFlow.js, ONNX Runtime Web and WASI-NN are exploring their applications; 5. Developers can start by learning the basics of WebGPU, paying attention to framework progress, trying open source projects, and combining WASM construction processes.
The H5 WebGPU itself is not a technology directly used for machine learning reasoning, but it is an important foundation for allowing machine learning models to run faster and more efficiently in the browser. As the WebGPU standard advances, more and more front-end frameworks are trying to use it for high-performance computing tasks, such as running lightweight ML model inference in the browser.

What is WebGPU?
WebGPU is a new web graphics and computing API that is more modern and performant than WebGL, and can better utilize the parallel computing power of the GPU. It allows developers to write high-performance rendering and computing code that is close to the capabilities of native hardware. This is good news for tasks that require a lot of computing resources, such as image processing, physical simulation, and machine learning inference.
Although WebGPU itself does not directly support machine learning, it provides the underlying infrastructure for building ML inference frameworks.

How does WebGPU help browser-side ML reasoning?
Higher parallel computing efficiency
Machine learning models usually rely on matrix operations, and GPUs have natural advantages in this regard. WebGPU provides stronger control than WebGL, and can better schedule these computing tasks.-
Cross-platform unified execution environment
Using WebGPU allows ML inference logic to maintain consistent behavior across devices, whether it is desktop or mobile, as long as there is a modern browser. Reduce dependence on JavaScript
In the past, many ML inference libraries relied on JavaScript for data processing, which slowed down on the CPU. WebGPU allows more logic to be moved to the GPU side, thereby reducing the burden on the main thread.
What projects are currently using WebGPU for ML reasoning?
There is currently no mature mainstream framework that fully supports WebGPU for ML reasoning, but some cutting-edge projects are already trying:
- TensorFlow.js : The official is exploring the improvement of performance through the WebGPU backend.
- ONNX Runtime Web : Microsoft is also pushing ONNX Runtime to support WebGPU as backend acceleration.
- WASI-NN WebGPU Experimental Integration : Some experimental projects attempt to combine WASI-NN with WebGPU to run small neural network models in the Web.
These are still in their early stages, but may become mainstream in the coming years.
If you want to try it, how to get it?
If you want to start trying WebGPU-based ML reasoning now, you can refer to the following steps:
- ? Learn the basics of WebGPU, learn how to write Shader and use Compute Pipeline
- ? Follow the progress of WebGPU support for TensorFlow.js or ONNX Runtime Web
- ? Try some open source experimental projects, such as community-driven attempts like webgpu-native
- ? Build your own inference process using WASM WebGPU (suitable for advanced users)
Note: The documentation and support are not perfect enough, and the debugging tools are limited, so it is more suitable for developers who are willing to toss.
Basically that's it. WebGPU opens new doors for ML reasoning in browsers, but there is still a way to go before it can be truly popularized. If you are a front-end engineer or AI developer, you might as well pay attention to this direction and plan future web intelligent applications in advance.
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