$GPU Rental Prices.com
answered with live data · 2026-07-08

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.

GPUVRAM$/hrWhere
H100 NVL94 GB$1.40Microsoft Azure spot / interruptibleRent →
H100 PCIe94 GB$1.99RunPod community cloudRent →
H100 (unspecified)94 GB$1.99Voltage Park on-demandRent →
H100 (unspecified)94 GB$2.01Spheron on-demandRent →
H100 SXM94 GB$2.04Microsoft Azure spot / interruptibleRent →
H100 SXM94 GB$2.30Vultr on-demandRent →
H100 (unspecified)94 GB$2.50Hyperstack on-demandRent →
H100 NVL94 GB$2.59RunPod community 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.