Calculator Inputs
Enter workload, throughput, and overhead values. The result appears above this form after submission.
Example Data Table
Use these sample planning cases to compare small, medium, and large model training schedules.
| Scenario | Dataset Samples | Epochs | Effective Batch | Effective Steps/Sec | Estimated Total Time |
|---|---|---|---|---|---|
| Prototype Fine-Tune | 250,000 | 3 | 64 | 2.400 | 1 hour 37 minutes |
| Department Model Refresh | 1,200,000 | 5 | 256 | 2.815 | 3 hours 8 minutes |
| Large Scale Retraining | 18,000,000 | 8 | 1,024 | 5.950 | 10 hours 27 minutes |
Formula Used
The calculator estimates wall-clock training time by combining optimizer-step workload with operational delays.
- Effective Global Batch = Batch Per Device × Gradient Accumulation × Device Count
- Steps Per Epoch = Ceiling(Dataset Samples ÷ Effective Global Batch)
- Effective Steps Per Second = Raw Steps Per Second × Utilization × (1 − Data Loading Overhead)
- Base Training Time = (Steps Per Epoch × Epochs) ÷ Effective Steps Per Second
- Evaluation Time = Base Training Time × Evaluation Overhead
- Checkpoint Time = Ceiling(Epochs × Checkpoints Per Epoch) × Checkpoint Time Each
- Total Training Time = Base Time + Evaluation Time + Checkpoint Time + Startup Overhead + Finalization Overhead
How to Use This Calculator
- Enter the total number of dataset samples trained in one epoch.
- Set epochs, batch per device, gradient accumulation, and device count.
- Insert the measured raw steps per second from a realistic benchmark.
- Adjust utilization and data loading overhead to reflect actual system behavior.
- Add evaluation, checkpoint, startup, and finalization delays for full schedule accuracy.
- Press the calculate button to see the result above the form.
- Download the generated summary as CSV or PDF when needed.
Frequently Asked Questions
1. What does this calculator estimate?
It estimates end-to-end model training duration, not only core compute time. The output includes validation overhead, checkpoint writing, startup delays, and final wrap-up tasks.
2. Why use effective steps per second?
Raw benchmark speed rarely matches production runs. Effective steps per second adjusts that raw speed with utilization and data loading losses for a more realistic schedule.
3. How does gradient accumulation affect time?
Gradient accumulation increases the effective global batch, which lowers optimizer steps per epoch. That can reduce total runtime when throughput remains stable.
4. Should I use samples or tokens?
This page uses sample counts. For token-based planning, convert your workload into equivalent sample units or replace the dataset field logic with token counts.
5. What should I enter for utilization?
Use an observed average from similar jobs. Many well-tuned training runs land below theoretical peak because of communication, input pipelines, memory pressure, and evaluation pauses.
6. Why are checkpoint settings important?
Checkpoint writing can meaningfully stretch total job time, especially on slow storage. Frequent saves improve recovery safety but raise wall-clock duration.
7. Can this help with capacity planning?
Yes. It helps compare training scenarios before booking hardware, setting milestones, or forecasting experiment throughput for research and engineering teams.
8. Does this replace benchmark testing?
No. It works best after you measure actual steps per second on representative hardware, sequence lengths, precision settings, and dataset pipelines.