Same run priced across GPU types
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AI & LLM Cost
AI Training Cost Calculator
Estimate what it costs to train or fine-tune a model. Choose a GPU, set the number of GPUs and training hours, and see total cost, GPU-hours and cost per GPU with a rate comparison.
Estimated training cost
$0
GPU-hours consumed—
Cost per GPU-hour—
Cost per hour (all GPUs)—
Effective compute hours—
Cost per GPU—
What the AI Training Cost Calculator does
Training and fine-tuning cost is driven by GPU time. You rent accelerators by the hour, and the bill is simply the number of GPUs multiplied by the hours you run them and the hourly rate for that chip. Modern accelerators range from a modest T4 at a few cents an hour to a B200 at several dollars an hour, so the chip you pick matters as much as the run length.
This AI training cost calculator turns your fleet size and run time into a total, shows the GPU-hours consumed, and compares every common accelerator on the same job. It works for full training, fine-tuning and large batch inference jobs where you rent dedicated GPUs.
Heads up on pricing. The rates built into this tool are public list estimates for July 2026 and can change without notice. Providers also offer batch discounts, prompt caching and volume tiers that lower real costs, so treat the output as a planning estimate, not a quote.
The formula
GPU-hours = number of GPUs × training hours
total cost = GPU-hours × hourly rate per GPU
total cost = GPU-hours × hourly rate per GPU
Worked example
Fine-tuning on 8 H100 GPUs for 120 hours is 8 × 120 = 960 GPU-hours. At about $2.99 an hour that is 960 × $2.99 = about $2,870. The same run on A100 80GB at $1.79 would be roughly $1,718.
GPU on-demand rates used (per GPU hour, July 2026 estimates)
| GPU | Memory | Approx rate / hour |
|---|---|---|
| NVIDIA T4 | 16 GB | $0.35 |
| NVIDIA L4 | 24 GB | $0.70 |
| A100 | 40 GB | $1.10 |
| A100 | 80 GB | $1.79 |
| H100 | 80 GB | $2.99 |
| H200 | 141 GB | $3.49 |
| B200 | 192 GB | $5.50 |
Rates are July 2026 on-demand estimates and vary by cloud and region. Spot or reserved capacity and multi-week commitments can cut these by 40 to 70 percent.
Ways to lower your AI costs
- Match the model to the task. Route simple requests to a small model like GPT-5 mini, Gemini Flash or DeepSeek V3, and keep premium models for complex reasoning where quality pays off.
- Cache repeated context. Prompt caching means you stop paying full price for the same system prompt or documents on every call.
- Cap output length. Output tokens cost several times more than input, so limiting response length is often the fastest saving you can make.
- Trim prompts and history. Summarize old conversation turns and drop context the model does not actually need to answer well.
- Batch non-urgent work. Many providers offer batch pricing at around half the standard rate for jobs that can wait a few hours.
Frequently asked questions
How much does it cost to train an AI model?
Anything from a few dollars for a small fine-tune to millions for a frontier model. It comes down to GPU count, hours and the accelerator. Enter your run above for an estimate.
What is a GPU-hour?
One GPU running for one hour. Eight GPUs for ten hours is 80 GPU-hours. It is the standard unit for pricing training and heavy inference jobs.
How can I train more cheaply?
Use spot or preemptible instances, reserve capacity for long runs, choose the smallest chip that fits your model, and use mixed precision to finish in fewer hours.
Is fine-tuning cheaper than full training?
Far cheaper. Fine-tuning updates a small part of a pretrained model, often finishing in a few GPU-hours instead of thousands, so it usually costs tens to hundreds of dollars.
Are these GPU rates current?
They are July 2026 on-demand estimates. Actual prices vary by provider and region, so confirm before you commit a large run.