Work out distributed training speedup instantly with clear inputs, formula shown and shareable results.
Amdahl's law caps speedup at 1 / (s + p/N), so even a 92 percent parallel workload cannot exceed 12.5 times no matter how many devices you add. Real distributed training is worse still because gradient synchronisation grows with device count, and multiplying by that communication factor gives the efficiency figure that determines whether adding hardware is worth the money.
Amdahl with communication
S = 1 / (serial + parallel/N) divided by (1 + comm overhead x (N - 1)); efficiency = S / N
Well-tuned data-parallel training holds 85 to 95 percent efficiency up to tens of GPUs on a fast interconnect. Below 70 percent, investigate the interconnect and the batch size per device.
Gradient bucketing overlapped with the backward pass, mixed-precision or compressed gradients, larger per-device batches, and topology-aware all-reduce.