Part 10/12:
JAX's design supports parallel computations within a single device or across multiple GPUs, harnessing the full power of hardware accelerators. For multi-GPU setups, more complex configurations are involved, but JAX provides tools to streamline the process.
Practical Use Cases and When to Use JAX
The speaker emphasized scenarios where JAX truly shines:
High-speed training—especially when microsecond reductions matter.
Research and prototyping—where flexibility with derivatives and custom gradients is needed.
Distributed/accelerated computing—on TPUs or multi-GPU setups.
Developing new algorithms—like higher-order gradients or custom optimizers.
Conversely, JAX may not be ideal for:
- Rapid prototyping for beginners unfamiliar with functional paradigms.