Gradient accumulation fakes a big batch on small memory — .grad already sums, so scale each micro-batch by 1/N and step once
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A big batch gives a smoother, less noisy gradient and lets you train at the batch size a recipe was tuned for — but the activation memory of a forward/backward pass scales with the number of samples in it, so a batch of B can simply not fit on your GPU. Gradient accumulation fakes the big batch on small memory: split the effective batch into N small micro-batches, run forward and backward on each, and let the gradients pile up. I built a demo that proves the equivalence numerically. Here's the…
1Key Takeaways
- A big batch gives a smoother, less noisy gradient and lets you train at the batch size a recipe was tuned for — but the activation memory of a forward/backward pass scales with the number of samples in it, so a batch of B can simply not fit on your GPU.
- Gradient accumulation fakes the big batch on small memory: split the effective batch into N small micro-batches, run forward and backward on each, and let the gradients pile up.
- I built a demo that proves the equivalence numerically.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that a big batch gives a smoother, less noisy gradient and lets you train at the batch size a recipe was tuned for — but the activation memory of a forward/backward pass scales with the number of samples in it, so a batch of B can simply not fit on your GPU.
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