AI server hosting

Alojamiento de Stable Diffusion GPU 8+ GB VRAM required — SDXL needs 12+ GB

GPU requerida Dedicated

Configuraciones recomendadas

Stable Diffusion generates photorealistic images from text prompts. GPU-only — CPU is too slow for practical use. SD 1.5 runs on 8 GB VRAM; SDXL and Flux need 12–24 GB. No per-image fees — generate thousands of images for the cost of the server.

Hobbyist — SD 1.5

SD 1.5 and compatible models Personal use, experimentation
desde €69.00/mo
Dedicated server
RTX 3070 (8 GB VRAM)
CPU
4 cores
RAM
16 GB RAM
Almacenamiento
80 GB NVMe
Red
1 Gbps unlimited
24–72h

Good starting point for SD 1.5 models

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Recomendado

Professional — SDXL

SDXL, SD 1.5, ControlNet Recommended for most users
desde €199.00/mo
Dedicated server
RTX 4090 (24 GB VRAM)
CPU
6 cores
RAM
32 GB RAM
Almacenamiento
150 GB NVMe
Red
1 Gbps unlimited
24–72h

Recommended — runs all current SD models at full speed

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Studio — Flux + multi-model

Flux, SDXL, multiple models simultaneously Production, commercial studio use
desde €599.00/mo
Dedicated server
A100 (80 GB VRAM)
CPU
8 cores
RAM
64 GB RAM
Almacenamiento
300 GB NVMe
Red
1 Gbps unlimited
24–72h

Maximum VRAM for Flux and concurrent model loading

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¿Buscas una configuración GPU específica?

Ver todos los servidores dedicados GPU →

Por qué Stable Diffusion necesita el servidor adecuado

GPU is non-negotiable

CPU inference for Stable Diffusion takes 5–30 minutes per image. A GPU with 8+ GB VRAM generates the same image in 3–15 seconds. CPU mode is not practical for regular use.

SDXL needs 12+ GB VRAM

SDXL produces significantly higher quality images than SD 1.5 but requires 8–12 GB VRAM minimum. An RTX 4090 with 24 GB VRAM runs SDXL and ControlNet simultaneously without compromise.

Storage matters with models

Each Stable Diffusion model is 2–7 GB. If you work with multiple models, checkpoints, and LoRAs, storage adds up fast. Plan for 100–300 GB NVMe if you maintain a model library.

No per-image fees

Midjourney charges per image and has monthly limits. Self-hosting Stable Diffusion means unlimited generation — thousands of images a day if needed — for a fixed server cost.

Preguntas frecuentes

Can I run Stable Diffusion on CPU?

Technically yes, but it takes 5–30 minutes per image. CPU inference is impractical for regular use. A GPU with 8+ GB VRAM generates the same image in 3–15 seconds. GPU is effectively required for a usable Stable Diffusion setup.

Which Stable Diffusion model should I use?

SD 1.5 is the most compatible, with the largest ecosystem of LoRAs and extensions. SDXL produces significantly better quality. Flux (12B) is the current state-of-the-art for photorealism. Start with SDXL on an RTX 4090.

What web UI should I use?

Automatic1111 is the most popular choice — it has thousands of extensions and a large community. ComfyUI is more powerful for complex workflows but has a steeper learning curve. Both run headlessly on a remote server and are accessed via browser.

How much storage do I need?

Plan for at least 80 GB for a basic setup (OS + a few models). If you work with multiple checkpoints, LoRAs, and custom nodes, 150–300 GB NVMe is more comfortable. Models range from 2 GB (small LoRAs) to 7 GB (full SDXL checkpoints).

Can I use ControlNet, IP-Adapter, and other extensions?

Yes. With root access, install any extension Automatic1111 or ComfyUI supports. ControlNet, IP-Adapter, upscalers, face restoration — all work normally. Each extension adds VRAM usage; an RTX 4090 handles them all simultaneously.

Stable Diffusion is the leading open-source image generation model, capable of producing photorealistic images, illustrations, and artwork from text prompts. Unlike cloud-based generators that charge per image, self-hosting means unlimited generation at a fixed monthly cost. SD 1.5 runs on a GPU with 8 GB VRAM; SDXL requires 12+ GB for comfortable use; Flux models need 12–24 GB. An RTX 4090 with 24 GB VRAM handles every current Stable Diffusion variant. Use Automatic1111 or ComfyUI as the web interface — both are browser-based and work headlessly on a remote server.

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