Batch Size Calculator Form
Single-column page structure with a responsive calculator grid inside the form.
Example Data Table
| Scenario | Dataset | Memory / Sample | Devices | Precision | Suggested Device Batch | Global Batch |
|---|---|---|---|---|---|---|
| Small classifier | 25,000 | 12 MB | 1 | FP16 | 128 | 128 |
| Transformer fine-tuning | 50,000 | 36 MB | 2 | FP16 | 240 | 960 |
| Long-context model | 100,000 | 95 MB | 4 | BF16 | 64 | 512 |
| Quantized experiment | 80,000 | 18 MB | 2 | INT8 | 320 | 1,280 |
These rows are sample planning references. Real memory usage changes with optimizer state, sequence length, activations, and model structure.
Formula Used
Usable Memory = Available Device Memory × (1 − Safety Reserve ÷ 100)
Adjusted Memory Per Sample = Memory Per Sample × Precision Factor × Checkpoint Factor
Memory-Limited Device Batch = floor(Usable Memory ÷ Adjusted Memory Per Sample)
Global Batch Size = Recommended Device Batch × Gradient Accumulation Steps × Number of Devices
Recommended Learning Rate = Base Learning Rate × (Global Batch Size ÷ Baseline Batch Size)
How to Use This Calculator
- Enter your dataset size and the number of epochs you plan to train.
- Provide available memory per device and your estimated memory use per sample.
- Select the precision mode that matches your training setup.
- Set gradient accumulation and the number of devices to reflect distributed training.
- Keep a safety reserve to reduce out-of-memory errors during unstable peaks.
- Submit the form to see the result above the calculator, plus the Plotly graph.
- Use the CSV or PDF buttons to export the result for documentation.
- Compare the result with the example table before finalizing production settings.
Frequently Asked Questions
1. What does batch size mean in machine learning?
Batch size is the number of samples processed before one optimizer update. Larger batches can improve throughput, while smaller batches often fit memory better and may add gradient noise that sometimes helps generalization.
2. Why does memory per sample matter so much?
Memory per sample directly controls how many samples fit on each device. It includes activations, optimizer state, gradients, and model behavior. Larger sequence lengths or input sizes usually increase this value quickly.
3. What is the difference between device batch and global batch?
Device batch is the number of samples processed on one device at a time. Global batch includes every device and gradient accumulation step, so it reflects the effective batch used for each optimizer update.
4. Why should I keep a safety reserve?
A safety reserve leaves memory headroom for fragmentation, framework overhead, variable sequence lengths, and spikes from data or optimizer behavior. It reduces the chance of crashes when a run looks stable on paper but not in practice.
5. How does precision affect the result?
Lower precision modes usually reduce memory use, allowing larger batches. FP16, BF16, and INT8 often fit more samples than FP32. Actual gains depend on hardware, kernels, optimizer choices, and model implementation.
6. What does activation checkpointing change?
Activation checkpointing saves memory by recomputing parts of the forward pass during backpropagation. This often enables larger batches, although it can also reduce speed because extra compute work is introduced.
7. Should I always choose the biggest possible batch?
Not always. The biggest memory-fitting batch may hurt stability, generalization, or training dynamics. Many teams choose a slightly smaller value to preserve headroom, avoid crashes, and keep tuning flexibility during experiments.
8. Can this calculator replace profiling tools?
No. This calculator is a planning tool, not a profiler. Use it to estimate safe starting values, then verify with real monitoring, logs, framework memory reports, and short benchmark runs on your target hardware.