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Guide

Best GPU Cloud for Stable Diffusion in 2026

11.3.2026
7 min read

Best GPU Cloud for Stable Diffusion in 2026

GPU Requirements by Model Version

Different Stable Diffusion versions have very different hardware requirements:

| Model | Min VRAM | Recommended VRAM | Notes |

|---|---|---|---|

| SD 1.5 | 4GB | 8GB | Runs anywhere |

| SDXL 1.0 | 8GB | 12GB+ | Benefits from speed |

| SD 3.0 / 3.5 | 16GB | 24GB+ | More demanding |

| Flux.1 Dev | 24GB | 24GB+ | High quality, VRAM hungry |

| Flux.1 Schnell | 16GB | 24GB | Faster variant |

Best Cloud Providers for Image Generation

RunPod — Best Balance

  • RTX 4090 (24GB): $0,44/hr — ideal for SDXL and Flux
  • A100 40GB: $1,19/hr — for large batches
  • Pre-built ComfyUI template available
  • Vast.ai — Best Price

  • RTX 4090: from $0,20/hr — lowest available
  • RTX 3090 (24GB): from $0,14/hr — SDXL on a budget
  • Manual Docker setup required
  • Lambda Labs — Best Stability

  • A100 40GB: $1,10/hr — great for batch generation pipelines
  • Consistent performance, professional SLAs
  • RTX 4090 vs A100 for Image Generation

    | Metric | RTX 4090 | A100 40GB |

    |---|---|---|

    | SDXL images/min | 4,2 | 7,3 |

    | Price/hr | $0,44 | $1,19 |

    | Cost/100 images (SDXL) | ~$1,05 | ~$1,63 |

    | VRAM | 24GB | 40GB |

    **Verdict:** RTX 4090 wins on cost efficiency for SDXL. A100 wins for batch jobs and models requiring >24GB VRAM.

    Setting Up ComfyUI on RunPod

    1. Go to [RunPod](https://runpod.io/?ref=t24bnbpm) → **Deploy** → Search templates for **"ComfyUI"**

    2. Select RTX 4090 or RTX 3090

    3. Set container disk to 30GB and volume disk to 50GB+

    4. Deploy and wait ~2 minutes for startup

    5. Click **"Connect"** → **HTTP Service on port 8188**

    ComfyUI will be accessible directly in your browser with no configuration needed.

    Throughput Benchmarks

    SDXL 1024×1024, 20 steps, DPM++ 2M

    | GPU | img/min | Cost/hr | Cost/100 imgs |

    |---|---|---|---|

    | RTX 3090 | 3,1 | $0,22 | $0,71 |

    | RTX 4090 | 4,2 | $0,44 | $1,05 |

    | L40S | 6,1 | $0,95 | $1,56 |

    | A100 40GB | 7,3 | $1,19 | $1,63 |

    Flux.1 Schnell 1024×1024, 4 steps

    | GPU | img/min | Cost/hr |

    |---|---|---|

    | RTX 4090 | 5,8 | $0,44 |

    | A100 80GB | 9,2 | $1,89 |

    Recommended Configurations by Use Case

    Personal Project / Experimentation

  • GPU:: RTX 3090 on Vast.ai
  • Cost:: ~$0,14–0,22/hr
  • Good for:: SDXL, ControlNet, LoRA testing
  • Professional Batch Generation

  • GPU:: RTX 4090 on RunPod
  • Cost:: $0,44/hr
  • Good for:: Client work, high volume SDXL/Flux
  • Production API / High Volume

  • GPU:: A100 40GB on Lambda Labs
  • Cost:: $1,10–1,19/hr
  • Good for:: API with consistent latency SLA, Flux.1 Dev batches
  • Tips for Maximum Efficiency

  • Enable **xformers** attention: `--xformers` flag in ComfyUI
  • Use **SDXL Turbo** or **Flux Schnell** for drafts (4–8 steps)
  • Batch multiple prompts per call to maximize GPU utilization
  • Use **network volumes** on RunPod to avoid re-downloading models
  • Conclusion

    For most image generation use cases, an RTX 4090 on RunPod or Vast.ai offers the best cost efficiency. Only upgrade to A100 when you need >24GB VRAM or guaranteed SLAs for production.

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