TensorDock GPU pricing: rental cost per hour by GPU and region

TensorDock rents GPU instances from about $0.17 per GPU-hour on-demand (V100 in published). This page tracks 6 instance rows across 6 GPU models (A100-80GB, A100-80GB-SXM, H100-SXM, L40, RTX6000-Ada, V100) and 1 regions, from the ByteCosts GPU catalog. Per-GPU rate is the instance hourly price divided by GPU count, so multi-GPU boxes can look cheaper per card. Spot and preemptible capacity is not guaranteed and can be reclaimed, so size on-demand for steady jobs and treat spot as a discount for interruptible work.

TensorDock GPU rental at a glance

• Cheapest on-demand: $0.17 per GPU-hour (V100, v100-sxm2-16gb, published)
• Spot pricing not published for TensorDock in this catalog
• 6 GPU models and 1 regions tracked
• 6 instance rows, updated August 22, 2026

Cheapest TensorDock rate by GPU model

Lowest on-demand per-GPU-hour rate for each GPU model TensorDock offers, from the ByteCosts catalog. Per-GPU rate is the instance hourly price divided by GPU count.

When TensorDock GPUs are cost-effective

Headline per-hour price is only half the story for GPU rental. The variables that move a training or inference bill are GPU utilization (idle reserved capacity is pure waste), whether your job tolerates spot interruption, region and egress fees, and the storage and networking attached to the instance.

TensorDock tends to win when you can keep the GPU busy, batch interruptible work onto spot, and pick a region close to your data. It tends to lose when capacity sits idle between jobs, when egress moves large datasets out repeatedly, or when a smaller or older GPU would clear your throughput target at a lower rate.

Hidden costs to watch with TensorDock GPUs

The instance hourly rate rarely equals your invoice. Budget for these before committing:

• Idle time: a reserved GPU bills whether or not a job is running, so low utilization multiplies effective cost per useful hour.
• Spot reclaim: interruptible capacity can be taken back mid-job, forcing checkpoints and restarts that add wall-clock and waste.
• Storage and networking: attached disks, snapshots, and inter-node bandwidth for multi-GPU training are billed on top of the GPU.
• Egress: moving model weights or datasets out of the region adds per-GB transfer fees.
• Region spread: the same GPU can cost noticeably more in scarce regions, so the cheapest row may not be where your data lives.

Frequently asked questions

TensorDock GPU rental pricing. ByteCosts. Updated August 22, 2026. https://bytecosts.com/gpu/tensordock/

Sources

• SkyPilot GPU catalog (v8)