allocateFine-tuning cost calculator
Dataset size, epochs, method. Out comes GPU-hours and dollars, so you know what owning your model costs before you start.
That buys one run. A build session quotes the whole project flat: training, serving, and the compounding loop that keeps the model improving after launch.
How it works
Common questions
How accurate is the estimate?
It assumes H100-class GPUs at current market rental rates with realistic multi-GPU scaling efficiency. Real runs vary with sequence length, batch size, and checkpointing, but the estimate lands in the right bracket for planning.
LoRA or full fine-tuning?
LoRA trains a small adapter on top of frozen weights and captures most task gains at a fraction of the cost; it is the production default. Full fine-tuning updates every weight and only pays off for deep domain shifts with large datasets.
How many epochs should I train?
Two to four epochs is typical for instruction data. More epochs on a small dataset overfits; better data beats more passes.
Is the fine-tuned model mine?
On open-weight bases, yes when your platform lets you keep the weights. On Allocate, fine-tuned weights stay inside your isolation boundary and belong to you; leave, and they go with you.
What does the same run cost on Allocate?
Training runs on Allocate are quoted flat before you commit, as part of the forecast: fees and usage in writing, no surprise at the invoice. Book a build session and we scope it on the call.
More free tools
These numbers are the rent-a-GPU path. On Allocate the same run is quoted flat in your forecast, the weights are yours, and every task your agents complete keeps training the model.