How to deploy client-side AI applications that work offline on any consumer device

This task can be performed using Jax JS

Pure-JS machine learning with WebGPU speed, straight in-browser.

Best product for this task

Jax JS

oss

Jax-js is a pure JavaScript machine learning framework and compiler for the browser, targeting WebGPU and WebAssembly for high-performance numerical computing. It offers a JAX-like API, zero dependencies, and fully client-side execution for fast, portable ML experiences on consumer devices.

browser jaxwebgpu frameworkjavascript autodiff

Discover Jax JS

Read Reviews

What to expect from an ideal product

1. Run machine learning models directly in the browser without internet connection using pure JavaScript and WebGPU acceleration
2. Deploy once and work across all modern devices since it requires no special installations or native app stores
3. Get desktop-level performance on phones and tablets by tapping into device GPU power through WebAssembly fallbacks
4. Build lightweight apps that start instantly because everything runs locally without server roundtrips or cloud dependencies
5. Scale to any consumer hardware automatically since the framework adapts between WebGPU and WebAssembly based on device capabilities

More topics related to Jax JS