GPU Server for 25 Concurrent LLM chatbot Users: Sizing Guide
Hardware recommendations for running LLM inference with 25 simultaneous users on dedicated GPU servers.
Quick Recommendation
For 25 concurrent llm chatbot users, we recommend the RTX 3090 (from £89/month) as the starting configuration. Solid mid-range option.
Recommended GPU Configurations
| GPU | VRAM | Monthly Cost | Recommended Models | Notes |
|---|---|---|---|---|
| RTX 3090 | 24 GB | £89/mo | LLaMA 3 8B or Mistral 7B | Solid mid-range option |
| RTX 5080 | 16 GB | £109/mo | 7B models with INT8 quantisation | Higher throughput per request |
| RTX 5090 | 32 GB | £179/mo | Mixtral 8x7B or LLaMA 3 70B (INT4) | Premium single-GPU option |
VRAM & Throughput Requirements
At 25 concurrent users, VRAM for KV cache becomes the primary bottleneck rather than model size. A 7B model uses 7–8 GB for weights, but 25 active conversations can require 10–15 GB of additional KV cache memory. The RTX 3090’s 24 GB provides a comfortable buffer.
Consider INT8 quantisation to reduce model weight memory and free up more space for concurrent KV caches.
Sizing Considerations
25 concurrent users represents a mid-sized production deployment. At this scale, hardware and software choices have a direct impact on user experience:
- VRAM is the constraint: 25 active KV caches alongside model weights require careful VRAM management. The RTX 3090’s 24 GB or 5090’s 32 GB provide the best margins.
- Throughput requirements: At 25 concurrent sessions, you need sustained throughput of 50–100 tok/s aggregate to maintain acceptable response times.
- Quantisation trade-offs: INT8 reduces model footprint and frees VRAM for more concurrent sessions, with minimal quality impact.
- Monitoring essentials: At this scale, monitor GPU utilisation, queue depth, and time-to-first-token to catch capacity issues before users notice.
Scaling Strategy
A single high-VRAM GPU can handle 25 chatbot users with continuous batching. As you approach 50, 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 horizontally as your user base grows.
Cost Comparison
Serving 25 concurrent llm chatbot users via API providers typically costs £1,125-3,000/month depending on usage volume. A dedicated GPU server at £89/month gives you predictable costs with no per-request fees.
Production-Ready for 25 Users
Deploy a dedicated GPU server sized for 25 concurrent chatbot users. Predictable monthly pricing, zero per-request charges.
