ML Engineering Tools
Batch Size Memory Calculator
Enter model parameter count, precision, and optimizer to compute GPU memory requirement and effective batch size with gradient accumulation.
Calculations run locally in your browserTool
Example — Representative default scenario — batch size 64 · sample MB 0.012 · overhead factor 1.35.
Model size
14.0 GB
7000M params × 2B
With Adam optimizer
56.0 GB
4× multiplier
Total GPU memory
56.1 GB
model+grad+optimizer states
A100 fits
Yes (80GB)
About this tool
Batch Size Memory Calculator
The Batch Size Memory Calculator computes GPU memory from model size, precision, and optimizer with gradient accumulation and checkpointing guidance.
• Estimate GPU memory before a large training run
• Find max batch size for an A100 80GB
• Calculate memory savings from fp16 vs fp32
• Plan gradient accumulation steps for a large effective batch
Next step
Continue with Utility Matrix tools for the next decision in this workflow.
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FAQ
What does this tool tell you?
The Batch Size Memory Calculator computes GPU memory from model size, precision, and optimizer with gradient accumulation and checkpointing guidance.
What affects the result most?
GPU memory: params × 4B (fp32) or 2B (fp16), × 4 for Adam (params+grad+2 momentum). Adam: model_size × 4 — params + gradient + m + v. Gradient accumulation: effective_batch = micro_batch × accum_steps — fits large batch on small GPU.
How should I use the result?
The calculation is deterministic — the same inputs always produce the same output — so the most useful workflow is to vary one input at a time and see which factor moves the result most. That tells you where to focus your attention before committing to a decision.
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