Gradient Accumulation Calculator
Steps to simulate a target global batch from your micro-batch and GPU count — with tokens-per-step and LR advice.
Llama-class pretraining uses ~4M tokens per optimizer step; the defaults hit 4.19M. Accumulation is mathematically identical to a bigger batch (for batch-independent losses) — just slower, since each update spans more forward/backwards.
Formula
About Gradient Accumulation Calculator
Gradient accumulation is the poor GPU's large batch: run several micro-batches, sum their gradients, then step the optimizer once — numerically equivalent to the big batch you couldn't fit. This calculator does the division everyone gets backwards at 2 a.m. (accumulation = global ÷ micro ÷ GPUs), flags when your target isn't a clean multiple, and reports tokens-per-update — the number that actually matters for comparing against published training recipes like Llama's ~4M-token steps.
How to use Gradient Accumulation Calculator
- 1Enter your values into Gradient Accumulation Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Gradient Accumulation Calculator?
- ✓Computes Gradient Accumulation instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: accumulation = global_batch ÷ (micro_batch × n_GPUs) — gradients sum across micro-steps; optimizer steps once.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Is accumulation REALLY identical to a larger batch?+
For mean-reduced losses over independent samples, yes — identical gradients up to floating-point order. Watch two exceptions: BatchNorm (statistics see only the micro-batch — a real difference; LLMs use LayerNorm, immune) and any loss normalized per-micro-batch with variable token counts.
Should the learning rate change with accumulation?+
Not if you hold the GLOBAL batch fixed — LR pairs with global batch, however assembled. If accumulation is how you're INCREASING global batch, apply the usual scaling heuristics (linear or √, see our LR-scaling calculator) with warmup.
Does accumulation slow training?+
Per optimizer step, yes — k micro-steps take ~k× longer. Per token processed, throughput is nearly unchanged (you skip k−1 optimizer steps and, with no_sync, k−1 gradient all-reduces — a small win). It trades wall-clock per update for memory, never FLOPs.
What micro-batch should I pick?+
The largest that fits after activations (see our activation-memory tool) — bigger micro-batches improve GPU utilization and reduce accumulation overhead. Then set accumulation to hit your recipe's global batch. Odd remainders silently change effective batch; this tool shows the exact achieved value.
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