Scale JAX models to multi-GPU systems

This tutorial demonstrates how to scale JAX deep learning models across multiple GPUs using JAX's modern compiler-driven sharding approach. Viewers will learn to use Mesh, PartitionSpec, and NamedSharding to declare array layouts across device meshes, implement automatic multi-GPU gradient synchronization, and manage state with Flax NNX and Orbax checkpoints. The video is ideal for ML practitioners familiar with JAX basics who want to distribute their training across multiple GPUs without writing complex device communication code.