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.
What to expect from an ideal product
- Uses WebGPU to run machine learning calculations directly on your graphics card instead of the slow CPU, giving you speeds close to native desktop apps
- Compiles JavaScript ML code into optimized WebGPU shaders that execute math operations in parallel across hundreds of GPU cores simultaneously
- Eliminates the overhead of data transfers between JavaScript and external libraries by keeping everything in native browser APIs
- Provides a JAX-compatible interface that automatically maps high-level ML operations like matrix multiplication to efficient GPU compute shaders
- Runs everything client-side without external dependencies, so there's no network latency or server bottlenecks slowing down your computations
