| Scenario | Examples | Avg tokens | Epochs | Runs | Overhead | Train rate | Val rate |
|---|---|---|---|---|---|---|---|
| Instruction dataset baseline | 50,000 | 800 | 3 | 2 | 12% | $8.00 / 1M | $2.00 / 1M |
- DatasetTokens = Examples × AvgTokens (or enter tokens directly).
- EffectiveTokens = DatasetTokens × (1 + Overhead% / 100).
- TrainTokensTotal = EffectiveTokens × Epochs × Runs.
- TrainCost = (TrainTokensTotal / 1,000,000) × TrainRate.
- Validation: ValTokensTotal = TrainTokensTotal × (Val% / 100) or ValTokensTotal = ValTokensPerEpoch × Epochs × Runs.
- ValCost = (ValTokensTotal / 1,000,000) × ValRate.
- ComputeCost = ComputeHoursPerRun × Runs × HourlyRate.
- StorageCost = CheckpointGB × Kept × Months × Rate.
- Subtotal = Training + Validation + Compute + Storage + Labeling + DataPrep + Misc.
- Total = Subtotal + (Subtotal × Contingency% / 100).
- Pick a dataset input mode: examples or total tokens.
- Enter epochs, runs, and overhead to match your process.
- Paste your token pricing for training and validation.
- Optionally add compute, storage, data prep, and misc costs.
- Use contingency to cover reruns and extra evaluation.
- Click Calculate and review totals plus unit economics.
- Download CSV or PDF when you need to share results.
Token volume is the primary cost lever
Fine-tuning budgets start with how many tokens you will process. This calculator multiplies effective dataset tokens by epochs and runs, then applies your training rate per one million tokens. A 50,000‑example dataset averaging 800 tokens contains 40 million tokens. With 12% overhead, the effective size becomes 44.8 million tokens, before epochs and repeats. With three epochs and two runs, that volume becomes 268.8 million training tokens, a quick cross-check for invoices. Small shifts in averages compound, so refresh inputs after each tokenization pass.
Overhead represents packaging, prompts, and structure
Overhead accounts for system text, instruction wrappers, separators, truncation padding, and repeated context. Teams often underestimate this, especially for multi-turn conversations. Setting overhead at 10–20% is common when templates are stable, while rapid prompt iteration can push overhead higher. Use the overhead input to stress-test “real” token counts.
Validation tokens improve confidence, but add spend
Validation during training helps detect overfitting and regression early. You can model validation as a percentage of training tokens, or as a fixed token count per epoch. For example, 5% validation on 268.8 million training tokens adds 13.44 million validation tokens. If validation is billed at a different rate, the calculator separates validation cost from training cost.
Compute and storage matter for self-hosted workflows
If you use dedicated GPUs, include compute cost per hour and estimated hours per run. This turns wall time into a predictable line item. Checkpoint storage can also grow quickly: checkpoint size × checkpoints kept × retention months × cost per GB‑month. Keeping four 6 GB checkpoints for two months equals 48 GB‑months, which becomes measurable at scale across many experiments.
Contingency supports realistic delivery timelines
Production training rarely finishes on the first pass. Contingency covers reruns after data fixes, hyperparameter sweeps, extra evaluations, and integration testing. A 10% buffer is a practical default for early projects, while mature pipelines can reduce it after measuring variance. Use unit metrics like cost per run and cost per one million training tokens to compare scenarios consistently. This supports scenario planning across teams.
1) What should I enter for “average tokens per example”?
Use a measured average from tokenization across your dataset. Include both instruction and completion tokens, plus any consistent wrappers. If you only have a sample, average several hundred records for stability.
2) When should I use the token-based dataset mode?
Choose token mode when you already know the total token count from preprocessing. It avoids relying on example averages and is best for mixed-length data or heavily truncated conversations.
3) How do I decide validation percent versus fixed tokens?
Percent scales naturally with bigger runs and is good for standard training loops. Fixed tokens per epoch is better when your evaluation suite is a constant set of prompts or test conversations.
4) Does the calculator include inference or deployment costs?
No. It focuses on fine-tuning training, validation, optional compute, storage, and preparation costs. Add separate estimates for hosting, serving, monitoring, and downstream evaluation if you need full lifecycle budgeting.
5) Why can overhead exceed 20% in some projects?
Multi-turn formatting, long system prompts, tool schemas, and repeated context can expand tokens significantly. If you frequently adjust prompts or include large metadata blocks, overhead can rise quickly.
6) What is a good way to compare two training plans?
Compare cost per run and cost per one million training tokens. These normalize for different dataset sizes and repeat counts. Then examine which cost category changes most, such as compute hours or validation volume.