Gradient Accumulation Calculator
Calculate the effective batch size achieved through gradient accumulation steps.
About Gradient Accumulation Calculator
Gradient accumulation simulates training with a large batch size on hardware that can only fit a small one: effective_batch = micro_batch_size × accumulation_steps × gpu_count. Instead of updating weights after every forward/backward pass, gradients are summed (accumulated) over several micro-batches before a single optimizer step is taken, so peak GPU memory stays at the micro-batch level while the optimizer still sees the statistical benefit of a much larger effective batch. This is the standard technique for training large models on memory-constrained GPUs, or for matching a specific large-batch training recipe without needing proportionally more hardware.
Formula
effective_batch_size = micro_batch_size * accumulation_steps * gpu_count
- micro_batch_size
- Samples per GPU per forward/backward pass
- accumulation_steps
- Number of micro-batches before an optimizer update
- gpu_count
- Number of GPUs in the training cluster
Worked example
With the default values below, Gradient Accumulation Calculator returns Effective Batch Size of 128. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Micro Batch Size (per GPU, per step)accepts 1–100,000 samples
- 4 samples
- Gradient Accumulation Stepsaccepts 1–100,000 steps
- 8 steps
- Number of GPUsaccepts 1–100,000 GPUs
- 4 GPUs
- Target Effective Batch Size (optional)accepts 0–10,000,000 samples
- 128 samples
Results
- Effective Batch Size
- 128 samples
- Batch Size Without Accumulation
- 16 samples
- Effective Batch Multiplier at Same Memory
- 8 ×
- Accumulation Steps Needed for Target
- 8 steps
Step by step
- 1Effective batch size: micro batch × accumulation steps × GPU count= 128
- 2Batch size without accumulation: micro batch × GPU count= 16
- 3Effective scale-up from accumulation (same peak memory)= 8×
Assumptions and limits
- The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to whole numbers, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Does gradient accumulation slow down training?
It increases wall-clock time per optimizer step (since you run multiple forward/backward passes before each update) roughly proportionally to the number of accumulation steps, but it doesn't increase total training compute — it trades time for the ability to use a larger effective batch size on limited memory.
Does accumulation change the training results compared to a true large batch?
Numerically it's very close — gradients are summed/averaged the same way — though details like batch normalization statistics (computed per micro-batch rather than per full batch) can introduce small differences from true large-batch training.
How do I choose the number of accumulation steps?
First find the largest micro-batch size that fits in GPU memory (see the Batch Size Calculator), then divide your target effective batch size by (micro_batch × GPU count) to get the required accumulation steps.
Does gradient accumulation reduce memory below a single micro-batch's footprint?
No — peak memory is set by the largest single micro-batch forward/backward pass; accumulation lets you reach a larger effective batch without exceeding that peak, but it doesn't reduce the micro-batch memory requirement itself.