Team Server Inference

Why this build

When 5–15 people share a self-hosted LLM, the bottleneck shifts from single-request throughput to concurrent throughput. vLLM's continuous batching can serve multiple simultaneous requests from a shared GPU pool, but it needs substantial VRAM headroom above the model weights to maintain quality of service across concurrent sessions. This dual-GPU build gives Llama 3.3 70B at Q4 (which needs ~40 GB VRAM) a few gigabytes of breathing room for KV caches across simultaneous conversations. The 64 GB system RAM handles vLLM's process overhead and allows the scheduler to queue requests without hitting swap. A high-wattage PSU and server-grade cooler are non-negotiable: these GPUs run at full TDP continuously, not just during peaks. This build isn't a desktop workstation — it's meant headless, rack-adjacent, always on. If you need interactive desktop use alongside serving, the Dual 4090 Workstation is a better fit.