Gradient Accumulation Calculator for PyTorch Training

Match a target (effective) batch size on limited GPU memory. Enter your micro-batch, GPU count, and the batch size you want to simulate — the tool returns the accumulation steps and the real effective batch live.

How the gradient accumulation formula works

Gradient accumulation lets you train at a large effective batch size that would otherwise overflow GPU memory. Instead of one big backward pass, you run several smaller micro-batches, sum their gradients, and only call optimizer.step() once after the group. The math the calculator uses is straightforward:

Because accum_steps must be a whole number, the achievable effective batch is usually a little above or below your exact target. The rounding selector controls that trade-off: ceil guarantees you reach at least the target (slightly larger batch), floor keeps you under your memory-derived ceiling, and round minimizes the gap. The tool reports both the chosen steps and the resulting batch so you can see the drift before you launch a run.

One detail people miss: when you accumulate, you must scale the loss by 1 / accum_steps before each backward(), otherwise summed gradients are accum_steps× too large and your effective learning rate explodes. Also, in distributed data-parallel training only the final micro-batch of each group should trigger gradient synchronization — wrap the earlier ones in model.no_sync() to avoid wasted all-reduce traffic. The tokens_per_step figure is handy for LLM work: it tells you how many tokens contribute to each optimizer update, which is the quantity learning-rate schedules and Chinchilla-style compute budgets are actually defined against. A higher effective batch generally needs a proportionally higher peak learning rate (linear scaling rule) and a longer warmup.