Samples/sec Calculator
Calculate training throughput in samples processed per second.
About Samples/sec Calculator
Training throughput in samples per second is the most direct measure of how fast a model trains: samples/s = batch_size / step_time_seconds. This figure directly determines how long a full epoch takes (steps_per_epoch × step_time), and is the metric most sensitive to hardware, batch size, model architecture, and data pipeline efficiency. Comparing samples/sec across different batch sizes or hardware configurations is the most practical way to evaluate real-world training speed improvements from optimizations like mixed precision, gradient checkpointing, or hardware upgrades.
Formula
samples_per_second = batch_size / step_time_seconds
- batch_size
- Number of samples per training step
- step_time_seconds
- Wall-clock time per step in seconds
Worked example
With the default values below, Samples/sec Calculator returns Throughput of 301.18. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Batch Sizeaccepts 1–1,000,000 samples
- 256 samples
- Time per Stepaccepts 0–1,000,000 seconds
- 0.85 seconds
- Dataset Size (optional)accepts 0–1,000,000,000,000 samples
- 1,000,000 samples
Results
- Throughput
- 301.18 samples/s
- Steps per Epoch
- 3,907
- Estimated Time per Epoch
- 55.35 minutes
Step by step
- 1Samples/sec: batch size ÷ step time= 301.18 samples/s
- 2Steps per epoch: ⌈dataset size ÷ batch size⌉= 3,907
- 3Epoch time: steps per epoch × step time= 55.35 min
Assumptions and limits
- The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Why does increasing batch size sometimes increase samples/sec and sometimes not?
Larger batches improve GPU utilization up to a point (better parallelism per step), increasing samples/sec, but beyond the point where the GPU is already compute-saturated, step time grows roughly proportionally with batch size, causing samples/sec to plateau rather than keep increasing.
How do I use samples/sec to estimate total training time?
Divide your total training samples (dataset size × number of epochs) by samples/sec to get total training time in seconds — this is the same calculation used by the Training Time Calculator, just expressed through throughput instead of step count.
Does samples/sec account for multi-GPU training?
If step_time_seconds is measured for the full multi-GPU setup processing one global batch, then samples/sec already reflects the combined throughput of all GPUs — just ensure batch_size refers to the full global batch, not the per-GPU micro-batch, to avoid underestimating throughput.