Fine-tuning cost is a function of dataset size, model size, number of epochs, and GPU price per hour — all knowable before you spend anything, yet rarely estimated upfront by student projects.
A back-of-envelope method
- Estimate tokens per example times number of examples times epochs, to get total tokens processed.
- Look up (or benchmark on a small run) tokens-per-second for your model size and quantization on your target GPU.
- Divide total tokens by that throughput to estimate GPU-hours, then multiply by the hourly rate.
Why this is worth doing before committing
A rough estimate that says 'this will take 40 GPU-hours' either fits a free-tier budget comfortably or immediately tells you to shrink the dataset, use QLoRA instead of full fine-tuning, or pick a smaller base model — before any money or time is spent finding that out the hard way.
See fine-tuning on a consumer GPU: the VRAM budget.
— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.