I want a practical way to estimate cost per training run across different GPU providers

A practical way is to estimate cost per run = (GPU hours × effective hourly rate) + storage + data transfer + overhead.

1) Use this simple formula

For one training run:

[ \text{Run Cost} = (\text{GPU count} \times \text{training hours} \times \text{price per GPU-hour}) + \text{extras} ]

Where extras may include:

• Storage for checkpoints/datasets
• Network egress if you move a lot of data out
• CPU/host costs if billed separately
• Idle time / queue time if you pay for reserved instances while not training

2) Normalize providers by “effective GPU-hour”

Different providers may quote differently:

• per GPU/hour
• per node/hour
• per instance/hour with multiple GPUs
• spot/preemptible vs on-demand
• includes/doesn’t include storage or bandwidth

Convert everything into:

[ \text{effective GPU-hour} = \frac{\text{instance hourly price}}{\text{number of GPUs on instance}} ]

Then compare providers apples-to-apples.

Example

If a 8-GPU node costs:

• Provider A: $24/hour → $3.00 per GPU-hour
• Provider B: $20/hour → $2.50 per GPU-hour

If your run uses 32 GPUs for 10 hours:

• A: 32 × 10 × $3.00 = $960
• B: 32 × 10 × $2.50 = $800

3) Add a utilization factor

If your code doesn’t use GPUs efficiently, adjust with utilization:

[ \text{Adjusted cost} = \frac{\text{raw cost}}{\text{utilization}} ]

Example:

• 70% GPU utilization means you’re effectively paying more per useful work done.

If Provider B is cheaper but has lower performance or slower networking, the run may take longer. Estimate:

[ \text{cost} = \text{hours-to-complete on provider} \times \text{hourly rate} ]

So the key is not just price/GPU-hour, but price to finish the job.

4) Compare by “cost per completed run,” not raw hourly price

Measure on a small benchmark:

• same model
• same batch size
• same precision
• same dataset subset
• same number of steps

Record:

• time per step
• throughput (tokens/sec or images/sec)
• scaling efficiency
• failure/restart rate

Then estimate:

[ \text{Run Cost} = \text{benchmark completion time} \times \text{provider hourly rate} ]

This is usually the most practical comparison.

5) Quick spreadsheet template

Columns:

• Provider
• Instance type
• GPUs/instance
• Price/hour
• Effective price/GPU-hour
• Estimated run hours
• GPU count needed
• Compute cost
• Storage cost
• Egress cost
• Buffer for retries (%)
• Total estimated cost

Formula: [ \text{Total} = \text{Compute} + \text{Storage} + \text{Egress} + \text{Buffer} ]

6) Recommended workflow

1. Pick 2–3 candidate providers.
2. Run a 15–30 minute benchmark on each.
3. Measure actual throughput and wall-clock time.
4. Convert each to cost per completed training run.
5. Choose the cheapest option that meets your speed/reliability needs.

7) If you want a very rough estimate fast

Use:

[ \text{Run Cost} \approx \text{GPU count} \times \text{hours} \times \text{effective GPU-hour price} \times 1.15 ]

The 1.15 is a simple buffer for overhead/retries.

If you want, I can give you:

• a spreadsheet template
• a Python calculator
• or a provider comparison table you can reuse.