A run moves from 8 to 64 GPUs. Per-device batch size and gradient accumulation stay unchanged, the loss uses mean reduction, and the learning-rate schedule is still expressed in epochs. Throughput improves, but the loss curve and final quality move.

Reconstruct the effective optimization step before and after the scale-up. Explain exactly what DDP averages, where accumulation changes the scale, and what must remain invariant to claim a mathematically comparable run.