JAX: The High-Performance Engine
Think of it this way: if a standard ML library is a reliable family car, JAX is the engine for a track-focused supercar. It's built for one thing: maximum performance at scale.
TPU Optimization
JAX and its compiler, XLA, are natively optimized for Google's Tensor Processing Units (TPUs). LLMs are extremely matrix-intensive, and JAX ensures these operations run at near-theoretical speed, which is crucial for training large models efficiently.
Functional Purity & Program Transformations
JAX enforces pure functions (functions without side effects). This predictable structure allows for a set of powerful program transformations that are key to its performance.
Compiles your Python/JAX code into highly efficient, device-specific machine code. This is what the `Run Forward Pass (JAX.jit)` button in the main application simulates.
Automatically parallelizes an operation across an array axis. For example, it allows you to apply the Feed-Forward Network to every token in a batch simultaneously, rather than one by one.
The key to multi-accelerator training. It handles model sharding and data parallelism across multiple devices (GPUs or TPUs), which is essential for scaling a massive Transformer.