Declarative Multi-GPU Scaling
Scaling JAX models across multiple GPUs does not require manual insertion of communication primitives like MPI or NCCL. Instead, JAX uses a compiler-driven sharding model where the developer defines how data and parameters are distributed across a physical device mesh. The core philosophy is to keep the training code identical to single-device implementations while modifying only the array placement.
The Four Pillars of JAX Sharding
To distribute workloads, you must manage four key concepts:
- Mesh: Defines the physical topology of your available devices.
- PartitionSpec: Describes how an array should be split across the mesh (e.g., sharding a batch dimension while replicating parameters).
- NamedSharding: Combines the mesh and the partition specification into a concrete plan.
- device_put: Applies the sharding plan to the array, signaling the compiler to insert necessary parallel operations, such as gradient averaging, during the
jit-compiled training step.
Verification and Performance
Scaling is not always free due to communication overhead. For small workloads, the cost of synchronizing gradients across devices can outweigh the performance gains. Use jax.debug.visualize_array_sharding to verify that your batch is correctly split and your weights are replicated as intended.
While automatic sharding is the recommended starting point for experimentation, developers requiring fine-grained control can use shard_map. This allows for explicit management of local shards and manual gradient synchronization via jax.lax.pmean.
State Management and Checkpointing
When using Flax NNX, state management involves replicating model and optimizer states across devices using nnx.state and jax.device_put. To ensure production-ready workflows, use Orbax for serializing and restoring checkpoints. When performing inference or text generation, ensure inputs are padded to a fixed maximum length to prevent the compiler from triggering expensive recompilations for every generated token.