What distributed technique for LLM development involves splitting data across multiple GPUs?

The technique for distributed LLM (Large Language Model) development that involves splitting data across multiple GPUs is known as data parallelism. In this approach, each GPU holds a complete copy of the model while processing different subsets of the data simultaneously. This allows for efficient training because multiple batches of data can be handled at once, significantly speeding up the overall training process.

When using data parallelism, gradients from each GPU are aggregated, which means that after each training step, the weights of the model are updated based on the collective information gained from all the processed data. This method maximizes the utilization of multiple GPUs by focusing on handling data input efficiently rather than changing the structure of the model itself. Consequently, it is particularly effective for large datasets, where the entire dataset may not fit onto a single GPU.

Although there are other techniques like model parallelism, which divides the model architecture across GPUs, or hybrid and layer parallelism that explore different dimensions of parallelization, they do not specifically focus on distributing data batches across multiple GPUs in the same way that data parallelism does.