AI Briefingjax-js: A Machine Learning Library and Compiler for the Web
Key point
jax-js has released a browser-based ML library that leverages WebGPU and WebAssembly.
Details
jax-js is a browser-based machine learning library and compiler written in JavaScript. It JIT-compiles WebGPU and WebAssembly kernels to run neural networks, image algorithms, simulations, and numerical computations in the client environment.
It works in major browsers such as Chrome, Firefox, iOS, and Android, and can be added to projects via npm install @jax-js/jax without external dependencies. It provides an API similar to NumPy and JAX, with a design inspired by JAX and PyTorch.
Key features include:
- Fully client-side execution in the browser
- Support for WebGPU, WebGL, and WebAssembly
- A lightweight compiler that transforms high-level operations into WebGPU and WebAssembly kernels
- Local LLM inference using GPU and CPU
- Interactive examples for AI models, simulations, geometry processing, and numerical analysis
Developers can use jax-js to quickly run compute-intensive applications locally on consumer hardware.
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