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.