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GPU Server for 5 Concurrent Image generation Users: Sizing Guide

How to size a GPU server for 5 concurrent image generation users. VRAM requirements, recommended GPUs, and scaling guidance for Stable Diffusion / FLUX inference.

Update: This post originally covered the RTX 4060 series (now discontinued). Content has been updated to reflect our current RTX 5060 (£99/mo) and RTX 5060 Ti (£119/mo) SKUs. Benchmark numbers in this post were originally measured on 4060-series hardware; expect the 5060 series to perform comparably or slightly better.

GPU Server for 5 Concurrent Image generation Users: Sizing Guide

Hardware recommendations for running Stable Diffusion / FLUX inference with 5 simultaneous users on dedicated GPU servers.

Quick Recommendation

For 5 concurrent image generation users, we recommend the RTX 5060 Ti (from £119/month) as the starting configuration. Entry-level image gen.

Recommended GPU Configurations

GPUVRAMMonthly CostRecommended ModelsNotes
RTX 5060 Ti 16 GB £119/mo SDXL / FLUX.1-schnell Entry-level image gen
RTX 3090 24 GB £159/mo SDXL / FLUX.1-dev More VRAM for larger batches

VRAM & Throughput Requirements

SDXL requires 8–12 GB VRAM per concurrent generation at 1024×1024 resolution. FLUX.1-dev needs 12–16 GB, while FLUX.1-schnell fits in 8–10 GB. For 5 concurrent users, a single GPU with request queuing keeps generation times under 10 seconds per image.

Sizing Considerations

Image generation has different GPU requirements than text inference. VRAM per generation is higher, but throughput is measured in images per minute rather than tokens per second:

  • Resolution impact: 512×512 images use roughly half the VRAM of 1024×1024. If your application uses smaller sizes, you can serve more concurrent users per GPU.
  • Model choice: FLUX.1-schnell is optimised for speed (4 steps), while FLUX.1-dev produces higher quality at 20–50 steps. Choose based on your quality-speed trade-off.
  • Queue management: Image generation takes 3–15 seconds per image. A proper request queue with progress feedback keeps users informed during wait times.
  • Batch efficiency: Some diffusion frameworks support batched generation. Processing 2–4 images simultaneously is often more efficient than sequential generation.

Scaling Strategy

Start with a single GPU. As you approach 10 concurrent users, add a second node behind a reverse proxy for horizontal scaling.

GigaGPU supports seamless multi-server deployments. Start with the minimum viable configuration and scale as demand grows.

Cost Comparison

Serving 5 concurrent image generation users via API providers typically costs £225-600/month depending on usage volume. A dedicated GPU server at £119/month gives you predictable costs with no per-request fees.

Image Gen from £119/Month

Deploy a dedicated GPU server for 5 concurrent image generation users. No per-image fees, no API rate limits.

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