Batch Size Optimizer

Calculate optimal batch size, memory usage, and gradient accumulation strategy

GPU VRAM (GB)

24 GB

Model Parameters

7.0 B

Data Type Precision

Recommended Max Batch Size

per GPU / per accumulation step

Total Batch Size (with Grad Accumulation)

256

across accumulation steps

Memory Utilization

78%

of available VRAM

Memory Breakdown

Model Weights 28.0 GB

Activations (batch) 12.0 GB

Gradients 28.0 GB

Optimizer States 14.0 GB

Total Per-Batch 18.8 GB

Recommendation. Use batch size 32 with 8 gradient accumulation steps for a total batch size of 256. This keeps VRAM usage at ~78% with headroom for intermediate computations.

TENSOR PREFLIGHT · DOWNLOADABLE PYTORCH TOOLKIT

Take the next step in your own PyTorch model.

Trace a local forward pass, inspect tensor shapes in an HTML report, and turn a repair into a regression check. Includes eight broken-and-fixed workflows, source code and setup instructions.

Is this toolkit right for my problem?

Use the free calculator for a layer formula or a quick shape check.

Consider Tensor Preflight when you have a local PyTorch model and want a forward-pass report plus repeatable checks. Python 3.9+ and PyTorch 2.8+ are required. It does not run your model in this website or automatically repair it.

Open the sample report before buying to check the output format.

Python 3.9+ · PyTorch 2.8+ · Runs locally

Get Tensor Preflight — $29 One-time payment · ZIP download See a sample report