Part 4/12:
A major advantage lies in JAX's ability to run seamlessly across different hardware accelerators without code changes. It accomplishes this via a proprietary compiler called XLA (Accelerated Linear Algebra), which optimizes code on the fly, reducing memory consumption and increasing throughput.
Automatic Differentiation
Like PyTorch and TensorFlow, JAX supports automatic differentiation (autograd), but with a more elegant and flexible implementation. With simple function passes—such as grad for gradients and jit for compilation—users can compute derivatives of complex functions effortlessly, even higher-order derivatives.