---
title: "Computer Vision Cloud GPUs — YOLO Training from $0.02/epoch | PowerGPU"
description: "Train YOLO, detection and segmentation models or sweep image archives: RTX 4090 at $0.327/hr, budget cards from $0.103/hr. NVDEC pipelines, per-second billing."
url: https://powergpu.ai/use-cases/computer-vision
last_modified: 2026-09-14T11:04:20+00:00
prices_as_of: 2026-09-14
site: PowerGPU (powergpu.ai)
---

Use case · computer vision

# Computer vision GPUs: detection budgets measured in epochs

Vision workloads are bursty by nature — a training sprint, a giant batch job, then nothing. Per-second billing fits perfectly: a YOLO11 epoch for about **$0.02**, an archive sweep for the price of coffee, zero idle spend between sprints.

## The vision cards, ranked

| Tier | GPU | VRAM | On-demand | Interruptible | Why this card |  |
| --- | --- | --- | --- | --- | --- | --- |
| (Good) | [RTX 3090](https://powergpu.ai/gpu/rtx-3090) | 24 GB | $0.108 | $0.054 | 24 GB at rock-bottom pricing — big batches for augmentation-heavy training. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-3090) |
| (Better) | [RTX A5000](https://powergpu.ai/gpu/rtx-a5000) | 24 GB | $0.161 | $0.080 | ECC + blower cooling for week-long training queues; the reliability pick. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-a5000) |
| (Best) | [RTX 4090](https://powergpu.ai/gpu/rtx-4090) | 24 GB | $0.327 | $0.163 | Fastest epochs per dollar — the default for YOLO, segmentation and ViT fine-tunes. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-4090) |

Batch-inference fleets: [Tesla T4](https://powergpu.ai/gpu/tesla-t4) from $0.103/hr — TensorRT detectors barely notice the smaller card.

## Two typical jobs, costed

- **Train YOLO11m, 100 epochs**: 50k images @ 640px, RTX 4090 interruptible · ≈ $1.96
- **Sweep 1M archived photos**: TensorRT detector, 4 × Tesla T4 in parallel · ≈ $0.18
- **Dataset volume 150 GB**: images + labels + runs, one month · $12.00

## Pipelines that scale down to zero

- **Ultralytics/MMDetection in the PyTorch template** — pip install and train.
- **Export to TensorRT** before batch runs — 3–5× throughput on the same card.
- **GPU video decode** — NVDEC keeps CPU out of the hot path for camera streams.
- **Weights on a volume**, instances disposable — retrain Fridays, pay Fridays only.

## Computer vision GPUs: FAQ

**Which GPU for training YOLO models?**

A RTX 4090 trains YOLO11m on a 50k-image dataset at roughly 7 minutes per epoch — about $0.02/epoch interruptible. 24 GB fits big batch sizes at 640px; step up to multi-GPU only past a few hundred thousand images.

**What about batch inference over an archive?**

Cheap cards shine: a Tesla T4 at $0.103/hr pushes hundreds of frames/s with a TensorRT-exported detector. A million images costs a few dollars — spread the queue over several instances and it finishes over lunch.

**Do you support video pipelines?**

Yes — NVDEC/NVENC are exposed in containers and VMs, so decode → detect → encode runs entirely on GPU (DeepStream, PyAV, ffmpeg builds in the PyTorch template).

---

*About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API.

Source: https://powergpu.ai/use-cases/computer-vision · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt