# JAX

> JAX is Google's high-performance computing library for AI research, powering custom training and novel architectures.

Source: https://metavert.io/jax  
Published: 2026-03-16  
Updated: 2026-03-16

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Layer 5: Knowledge & Substrate as JAX

**JAX** is a high-performance numerical computing library developed by Google that combines NumPy's familiar interface with automatic differentiation, GPU/TPU acceleration, and just-in-time (JIT) compilation. JAX has become a preferred framework for AI research, particularly at Google DeepMind and in the research community.

JAX's functional programming model and composable transformations (jit, grad, vmap, pmap) make it particularly well-suited for cutting-edge research that requires custom training loops, novel architectures, and efficient scaling across hardware accelerators. Many breakthrough AI models, including Google's Gemini, were developed using JAX.

In the model-building toolchain layer, JAX represents the research-oriented complement to PyTorch — a framework that prioritizes mathematical elegance, composability, and hardware efficiency for those pushing the boundaries of AI capabilities.

## Related Topics

[PyTorch](https://metavert.io/pytorch) · [Google DeepMind](https://metavert.io/google-deepmind) · [AI Model Training](https://metavert.io/ai-model-training)

## Further Reading

[JAX on GitHub](https://github.com/google/jax)