LLM Training Cost Analyzer

Cloud GPU demand makes it easy to lose track of true experiment costs. Provide the aggregate GPU hours and rate, then layer in orchestration overhead and checkpoint storage to see the full budget plus the expected wall-clock runtime based on how many accelerators you can secure in parallel.

Sum of GPU hours across all nodes for the training job.
Spot or on-demand rate per GPU hour.
Defaults to 16 GPUs to estimate wall-clock runtime when blank.
Defaults to 12% of compute spend for cleaning, orchestration, and QA.
Defaults to $800 to represent object storage, snapshots, and egress.

Examples

  • 640 GPU hours at $4.25/hr, 16 GPUs, 12% overhead, $800 storage ⇒ Compute $2,720.00, total $3,846.40, runtime 40.0 hours (1.7 days).
  • 2,400 GPU hours at $2.95/hr, 32 GPUs, 8% overhead, $0 storage ⇒ Compute $7,080.00, total $7,646.40, runtime 75.0 hours (3.1 days).

FAQ

How do I treat reserved or committed-use discounts?

Adjust the GPU rate input to the blended cost after discounts so the compute spend reflects your true effective rate.

Can I compare two hardware types?

Yes. Run the calculator separately for A100 versus H100 or TPU pricing and compare total spend plus runtime to decide which queue to enter.

Where do fine-tuning datasets fit in?

If your data prep work is minimal, lower the overhead percentage. If you purchase curated corpora, include that in the storage line or add it to the total after running the calculation.

Additional Information

  • GPU hours should include all nodes multiplied by the time they run so multi-node distributed jobs are fully captured.
  • Overhead percentage covers data cleaning, orchestration, QA labeling, monitoring, and human review tasks around the training run.
  • Storage line item can be zero if you delete checkpoints immediately, or increased to account for multi-region replication and retention policies.

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