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answered with live data · 2026-10-11

How much does it cost to train an LLM?

Training cost is the number of GPU hours you need multiplied by the hourly rate per GPU. Fine-tuning a small model can be a handful of GPU hours, while training a frontier model from scratch runs into millions of GPU hours across thousands of cards. The cheapest verified H100 in our index is $1.99/hr, which is the number you would plug in for a Hopper-class training run.

The cheapest on-demand H100 offers, per GPU-hour. Spot, community and marketplace-host offers are listed on the H100 page.

GPUVRAM$/GPU-hrWhere
H10080 GB$1.99Voltage Park on-demandRent →
H100 SXM80 GB$2.25TensorDock on-demandRent →
H10080 GB$2.43Prime Intellect on-demandRent →
H10080 GB$2.50Hyperstack on-demandRent →
H10080 GB$2.60GMI Cloud on-demandRent →
H10080 GB$2.64Spheron on-demandRent →
H10080 GB$2.73Massed Compute on-demandRent →
H100 PCIe80 GB$2.89RunPod secure cloudRent →

For a concrete estimate, start with the compute the run needs and the throughput of your GPU, then divide to get GPU hours. Multiply by the hourly rate, and multiply again by the number of GPUs if you train in parallel. A short fine-tune of a 7B model might take tens of GPU hours; a full pretraining run of a mid-sized model takes tens of thousands.

The hourly rate is where a price index pays off, because the same H100 hour can differ several times over between a hyperscaler and a community tier. Spot instances cut the rate further if your training checkpoints often enough to survive interruptions.

Do not forget the costs around the GPUs: storage for datasets and checkpoints, egress if you move data out, and the wall-clock overhead of failed runs and restarts. Those are easy to underestimate and can rival the raw compute bill on a long project.

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Numbers on this page come from today's verified snapshot. Full table on the homepage; method in the methodology.

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