Capacity Planning for Resource-Constrained Clinical LLM Serving

Yi Tang, Geoffrey Martin, Yiye Zhang, Yifan Peng

Deploying large language models (LLMs) under a fixed accelerator budget requires jointly deciding how computational capacity is allocated across model sizes and how incoming requests are routed across that capacity. Although prior work has extensively studied LLM routing and serving efficiency, the relative importance of fleet composition and routing under resource constraints remains less understood. We investigate this question using a controlled four-GPU serving environment with heterogeneous clinical NLP workloads spanning short factual verification, biomedical semantic matching, and long-context clinical summarization. We systematically vary fleet composition, routing policy, and offered load while holding the workload trace and hardware budget fixed. Across 24 configurations, descriptive main-effect shares attribute more variation to fleet composition than to the evaluated routing policies in time to first token, latency, and long-context coverage. All-small fleets provide the best responsiveness and throughput but substantially lower long-context coverage, whereas larger-model capacity improves coverage at the cost of serving efficiency. Heterogeneous fleets provide…