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An article from Chrome Developers titled “Optimizing WebAssembly and WebGPU for Faster Web AI – Part 1” presents the development of WebAssembly (Wasm) and WebGPU technologies to enhance the performance of artificial intelligence (AI) on web browsers. The focus is on on-device AI processing, which helps reduce server costs, lowers response time, and increases data privacy. (Source: Chrome for Developers)
Benefits of On-Device AI Processing
- Cost Reduction: AI processing in the browser reduces the need for high-performance servers, which are expensive—especially for running Generative AI tasks.
- Faster Response Time: Processing data on the device reduces round-trip time between the server and client, making it suitable for real-time applications such as audio or video processing.
- Improved Privacy: On-device processing means personal data doesn’t have to be sent to a server, which benefits applications that require high levels of privacy.
How AI Works on the Web Today
Developers use frameworks such as TensorFlow.js or ONNX Runtime Web to build and run AI models directly in the browser. These frameworks utilize WebAssembly for CPU processing and WebGPU for GPU processing, enabling efficient handling of large and complex data. (Source: Chrome for Developers)
Recent Improvements in WebAssembly and WebGPU
- WebGPU: Supports parallel computation on the user’s device GPU, allowing large AI models to run efficiently.
- WebAssembly: Enhancements include support for large memory management (Memory64) and integration with JavaScript Promises to boost AI model execution performance. (Source: Chrome for Developers)
These advancements enable developers to build more AI-capable web applications without relying on external servers, delivering a better user experience.
Source: developer.chrome.com
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