LLM Training Run Budget Calculator

Quickly scope the cash required to spin up an LLM training or fine-tuning run. Combine GPU runtime, hourly pricing, human-in-the-loop costs, checkpoint storage, and expected egress so you can secure budget approvals before booking capacity.

Sum of all accelerator hours across the full training or fine-tuning job (GPU count × runtime).
On-demand, reserved, or amortised cost per GPU hour in USD.
Labeling, cleaning, evaluation, and prompt engineering labour allocated to this run.
Leave blank to assume 420 GB of checkpoints and logs; override with your pipeline’s storage footprint.
Leave blank to assume 14 days of retention; extend for compliance or multi-run comparisons.
Leave blank to assume $0.26 per GB-month on premium SSD tiers; adjust for object storage or cold tiers.
Leave blank if checkpoints stay in-region; otherwise enter total outbound GB across collaborators or regions.
Leave blank to assume $0.09 per GB for public cloud egress; input your provider’s negotiated rate if lower.

Planning aid only; confirm pricing with your cloud provider or hardware vendor before committing spend.

Examples

  • 13B fine-tune using 290 GPU hours at $12/hr, $650 data prep, 420 GB stored for 14 days, 80 GB egress ⇒ $4,188.16 total run budget
  • 7B base training with 512 H100 hours at $21.50/hr, $500 data prep, 600 GB stored for 21 days, no egress ⇒ $11,617.20 required

FAQ

How do I account for spot interruptions?

Increase GPU hours by your expected interruption overhead or add a contingency percentage to the final result.

Can I include inference evaluation costs?

Yes. Add the extra GPU hours and data prep spent on eval suites into the same inputs so the budget covers the whole experiment.

What if I pay in another currency?

Convert your provider’s hourly rate to USD (or your reporting currency) before entering it, then reconvert the result as needed.

Additional Information

  • GPU hours already include parallelism—double the accelerator count doubles the hours consumed.
  • Storage multipliers cover checkpoints, logs, and tensorboard data; adjust retention after each sprint.
  • Add contingency (e.g., +15%) outside the calculator if you want headroom for reruns or spot preemptions.
  • Blend multiple GPU price tiers by averaging on-demand and reserved rates weighted by expected usage.

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