Batch Size Calculator
Calculate the maximum and recommended batch size based on GPU memory, model size, and sample size. Optimize your deep learning training setup.
How to Use the Batch Size Calculator
- Enter your total dataset size (number of samples).
- Enter available GPU memory in GB.
- Enter model size in MB.
- Enter average sample size in MB.
- Click Calculate to get max and recommended batch size.
Casos de Uso
- •Configuring deep learning training runs on GPUs.
- •Comparing training efficiency at different batch sizes.
- •Estimating steps per epoch for learning rate schedules.
- •Planning hardware requirements for ML workloads.
Fórmula
Available memory = GPU memory − model size × 3 (optimizer states). Max batch = available / sample size. Recommended = nearest power of 2.
Preguntas Frecuentes
Why use a power-of-2 batch size?
Powers of 2 (32, 64, 128) align well with GPU tensor cores and typically yield better performance.
What happens if batch size is too large?
The GPU runs out of memory (OOM). The model size × 3 accounts for gradients and optimizer states.
How do I estimate sample size in MB?
Multiply image dimensions × channels × bytes per pixel and divide by 1,048,576.