# JAX

> Accelerate numerical Python with automatic differentiation and compilation

JAX is an open-source Python library for numerical computing and machine learning. It provides a NumPy-style interface for working with arrays while running computations on CPUs, GPUs, and TPUs. Researchers and engineers can write mathematical functions in Python, then apply transformations that differentiate, compile, batch, or parallelize those functions.

These transformations can be combined, making JAX useful for developing trainable models, numerical simulations, and other computation-heavy research workflows. Its documentation covers array programming, performance tuning, distributed execution, and custom accelerator kernels. A surrounding ecosystem adds tools for neural networks, optimization, probabilistic modeling, and scientific applications, letting teams build specialized systems on the same computational foundation.

## Listing details

- Canonical BetaNeon page: https://betaneon.com/products/jax
- Official website: https://docs.jax.dev/en/latest/
- Category: Developer tools / ML engineering & evaluation
- Status: Launched
- Topics: GPU, Machine learning, Open source, Python, Scientific computing
- Listed: 2026-11-10T12:14:56.031Z
- Content updated: 2026-11-10T12:14:56.065Z
- Official source: https://docs.jax.dev/en/latest/
This is a product introduction, not an endorsement. Confirm current availability, features, and prices on the official website. Community upvotes on BetaNeon are not star ratings.