distributed training.distributed training.shared progress.shared progress.

one model. many workers.

Distributed training spreads computation across multiple machines or accelerators. In data-parallel training, each worker runs a copy of the model and processes a different batch of data.

Each worker calculates gradients: signals that guide how the model’s weights should change. Combining those gradients lets the next update reflect the work of the whole group. Every replica then applies the same update and continues from the same weights.

The illustration shows this exchange conceptually. The number of workers, hardware and communication layout can vary from one training run to another.

balance the work.

A cluster works best when its workers finish their assigned work at roughly the same time. Faster devices can process larger batches, while smaller batches help devices with less memory stay within their limits.

When workers process different amounts of data, their contributions need to be weighted appropriately. Otherwise, a smaller batch can have more influence than intended.

In data-parallel training, each worker still needs room for its model and working tensors. Adding machines spreads the computation; it does not automatically turn their memory into one shared pool.

what happens in one step?

A step can accumulate gradients from several smaller batches before updating the weights. This helps a run work within each device’s memory budget while building a larger effective batch.

keep the workers in sync.

The connection between workers carries the gradients needed for a shared update. Communication methods and network layouts depend on the hardware and framework.

More workers can reduce the time spent computing, but exchanging gradients also takes time. Workload balance and communication overhead both affect how much faster a cluster can train.

On Apple Silicon, MLX provides distributed communication primitives for coordinating computation across machines. A training framework uses these operations to keep the workers aligned.

measure more than speed.

Throughput shows how much data a run processes. Training loss shows how well the model predicts its training tokens. Held-out validation helps assess whether that learning carries over to data outside the training batches.

Generated samples and benchmarks add another view of the model’s behavior. Checkpoints preserve training state so a run can resume and earlier results can be compared.

Good distributed training balances efficiency, consistent updates and useful learning. A faster run matters most when its results can be inspected and reproduced.

Learn more in the MLX data-parallel training guide and distributed communication documentation.