Sizing LLM Hardware for 50 Users

A 50-person engineering team requires an optimized multi-GPU server to support concurrent inference without query latency spikes.

WHY IT MATTERS

Under-provisioning on-premise hardware leads to high queue delays, while over-provisioning wastes capital budget. Sizing hardware correctly ensures smooth concurrent performance at predictable costs.

GO DEEPER

• Concurrent usage estimate: Plan for 10 peak concurrent requests from a 50-person workforce.
• Target model selection: Deploy Llama 3.3 70B quantized to 4-bit precision.
• GPU memory requirement: Install 2x RTX 6000 Ada (96 GB total VRAM) for model weights and KV cache.
• Inference engine choice: Use vLLM with continuous batching to maximize memory throughput.
• Host system specs: Pair GPUs with 256 GB DDR5 RAM and a 32-core EPYC processor.
• Power and cooling overhead: Budget for a 1.5 kW server unit inside a rack enclosure.

THE BOTTOM LINE

Deploying a dual-workstation GPU server with vLLM comfortably supports 50 employees with low latency.