Lightning.ai GPU Pricing
8 GPU instances across 1 regions.6 GPU models available — from $0.41/hr.
ML development platform from the creators of PyTorch Lightning. Lightning.ai offers GPU-powered "Studios" with pre-built environments for training, fine-tuning, and experimentation.
- Built by PyTorch Lightning team — excellent ML tooling
- Pre-built environments for common ML tasks
- Collaborative studios for team work
- Platform-oriented — less flexible than raw GPU VMs
- Limited GPU selection
- Pricing can be opaque
Lightning.ai currently lists 8 GPU instances across 6 GPU models and 1 regions. Pricing starts at $0.41/hr, while the median listing price is $2.80/hr. Compare by model, commitment type, and region before treating the cheapest row as the best choice.
GPU Models at Lightning.ai
All Lightning.ai GPU Instances
8 results| GPU Model | Instance | Count | VRAM | Region | Type | Price/hr | $/GPU/hr | |
|---|---|---|---|---|---|---|---|---|
| T4 | 1x-T4-US-East | 1× | 16GB | US-East | On-Demand | $0.4100 | — | Rent |
| L4 | 1x-L4-US-East | 1× | 24GB | US-East | On-Demand | $0.6000 | — | Rent |
| A10G | 1x-A10G-US-East | 1× | 24GB | US-East | On-Demand | $0.7100 | — | Rent |
| A100 40GB | 1x-A100-40GB-US-East | 1× | 40GB | US-East | On-Demand | $1.8900 | — | Rent |
| A10G | 4x-A10G-US-East | 4× | 96GB | US-East | On-Demand | $2.8000 | $0.7000 | Rent |
| A100 80GB | 1x-A100-80GB-US-East | 1× | 80GB | US-East | On-Demand | $2.9900 | — | Rent |
| H100 | 1x-H100-80GB-US-East | 1× | 80GB | US-East | On-Demand | $3.5000 | — | Rent |
| A100 80GB | 4x-A100-80GB-US-East | 4× | 320GB | US-East | On-Demand | $11.9000 | $2.9750 | Rent |
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Lightning.ai GPU Cloud — FAQ
Lightning.ai GPU instances start from $0.41/hr. The average price is $3.10/hr. Prices depend on GPU model, region, and commitment type (on-demand vs spot).
Lightning.ai offers 6 GPU models: T4, L4, A10G, A100 40GB, A100 80GB, H100. Browse the full list above to compare prices per model.
GPU Tracker’s pricing comparisons are paired with true cost and risk signals. Read the methodology page for how refresh cadence, cost assumptions, and reliability indicators are defined.