data parallelism

Not the data, not the weights: the gradients, once per step, in a ring that moves almost twice the model's size through every GPU. The derivation, the per-step byte count for an 8B model, and why it still hides behind the backward pass.

Three ways to split a training job, one table, and the rule that places each of them: activations on NVLink, gradients on the fabric, stage boundaries in between. With the byte counts that justify the placement.

A synchronous training job across two clouds runs at the speed of the link between them, a thousand times slower than the fabric inside either. The arithmetic that kills the idea for one job, the two cases where it is tolerable, and what a platform does instead: partition the work, not the job, and move data ahead.