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Model Fine-Tuning GPU Requirements Estimator

The Model Fine-Tuning GPU Requirements Estimator calculates the minimum whole-GPU count needed to finish a training workload within a target duration. It combines total tokens, epochs, per-GPU throughput, scaling efficiency, and a utilization allowance.

It is intended for early capacity planning when deciding between longer runs on fewer GPUs and shorter runs on a larger cluster. Real jobs must also satisfy memory, interconnect, and supported parallelism constraints.

Calculator inputs

tokens
epochs
tokens/s
%
%
hours
Result
—
Required GPUs
Raw GPU requirement—
Total token workload—
Provisioned throughput—

1. Enter the training workload
Provide tokens per epoch and the planned number of epochs.

2. Use per-GPU throughput
Enter a benchmark for the intended model, precision, sequence length, and hardware.

3. Apply scaling efficiency
Reduce the ideal linear gain to reflect communication and synchronization overhead.

4. Set utilization and deadline
Reserve operational headroom and enter the desired completion time.

5. Provision the rounded count
Use at least the displayed whole-GPU count, subject to memory and topology constraints.

Required GPUs = Total training tokens ÷ (Per-GPU throughput × Scaling efficiency × Utilization × Target seconds)

Total training tokens equal tokens per epoch multiplied by epochs. The final requirement is rounded up.

What the result means

The output is a compute-throughput estimate for meeting the specified deadline.

Scaling efficiency often changes as GPU count increases, so a single efficiency input is most reliable near the benchmarked cluster size.

Given: 36 million tokens per epoch, 3 epochs, 1,400 tokens/second per GPU, 85% scaling efficiency, 80% utilization, and an 8-hour target.

Calculation: Total workload = 108 million tokens. Effective per-GPU rate = 1,400 × 0.85 × 0.80 = 952 tokens/second. Raw GPUs = 108,000,000 ÷ (952 × 28,800) = 3.94.

Result: Round up to 4 GPUs.

Does the result guarantee the model will fit in memory?

No. The calculator covers throughput, not memory. Model weights, optimizer states, activations, and sequence length must be checked separately.

What is scaling efficiency?

It is the fraction of ideal linear speedup retained after communication and coordination overhead.

Can utilization and scaling efficiency both be used?

Yes. Scaling efficiency represents multi-GPU overhead, while utilization represents available productive compute time.

Why might more GPUs fail to reduce duration proportionally?

Communication, input pipelines, checkpointing, and small batch sizes can limit scaling.

Should the target duration include setup time?

This formula covers token processing. Subtract expected setup and teardown time from the available target window when they are material.