Best Cloud GPU for Computer Vision
Most computer vision work, from detection and segmentation to classification, fits comfortably on a 24GB RTX 4090 at $0.35/hr, which is the value pick for both training and batch inference. High-resolution imagery, 3D or medical volumes, or large batch training move up to a 48GB L40S or an 80GB A100 at $1.09/hr. Lightweight, high-volume inference can run cheaper still on an L4. Size the card to your image resolution and batch, not to the model.
The picks, with live prices
| Pick | GPU | VRAM | From, $/hr | Where | |
|---|---|---|---|---|---|
| value pick | RTX 4090 | 24 GB | $0.35 | TensorDock on-demand | Rent → |
| budget inference pick | L4 | 24 GB | $0.44 | Jarvislabs on-demand | Rent → |
| scale pick | L40S | 48 GB | $0.96 | Spheron on-demand | Rent → |
| training pick | A100 | 80 GB | $1.09 | Thunder Compute on-demand | Rent → |
RTX 4090 value pick
24GB and strong throughput cover training and inference for the common CNN and vision transformer families at standard resolutions. Best price-per-image of the cards here for day-to-day CV work.
L4 budget inference pick
Low-power 24GB card built for high-volume, cost-sensitive inference. Ideal when you serve many images per second at modest resolution and want the lowest hourly rate rather than peak speed.
L40S scale pick
48GB handles large batch training and high-resolution or 3D volumes that overflow 24GB, with datacenter reliability for production pipelines. A step up when memory, not model size, is the limit.
A100 training pick
The 80GB A100 suits large-scale training runs, big batches, and memory-heavy 3D or medical imaging. Its bandwidth keeps data-augmentation-heavy pipelines fed when a workstation card stalls.
Worth knowing
- Standard 2D vision at typical resolutions rarely needs more than 24GB, so a consumer card is usually the right default.
- VRAM demand rises with image resolution, batch size, and 3D or volumetric data, not with parameter count, which is small for most CV models.
- If the data pipeline is the bottleneck, prioritize memory bandwidth and fast storage over raw compute.
- For real-time or high-volume inference, an efficiency card like the L4 often beats a faster card on cost per image.
FAQ
Usually less than LLMs. Most detection, segmentation, and classification models train fine in 24GB at standard resolutions. VRAM demand climbs with image resolution, batch size, and 3D or volumetric data, which is when a 48GB or 80GB card pays off.
For high-volume, latency-tolerant inference an L4 is often the cheapest per image thanks to its low hourly rate and efficiency. If you need peak speed on a single stream, an RTX 4090 gives more throughput per dollar.
Prices render from today's verified snapshot, not from when this guide was written. Full table on the homepage; break-even math in the calculator.