Capacity over TFLOPS: Memory-Centric Deployment of Large Clinical Language Models on Consumer CPUs

The dominant barrier to running large clinical language models at the point of care is not arithmetic throughput but graphics memory: consumer GPUs cap at 4-16 GB, while the working set of a 27-billion-parameter model in 4-bit precision is 17-25 GB. We argue that the edge node should be treated as capacity-enabled (system RAM) rather than compute-limited (GPU VRAM), and we test this on commodity hardware. Using an INT4 build of a 27B open model as an architecture-faithful stand-in for a 27B medical model, we measure the memory footprint, inference latency, energy, and federated communication cost of a fully CPU-based pipeline driven by synthetic Fast Healthcare Interoperability Resources (FHIR) prompts. On two independent consumer machines (a desktop-class machine with 67 GB of RAM and a 6 GB GPU, and a mobile workstation with 34 GB of RAM and a 4 GB GPU) the model loads entirely into system RAM (37% and 60% utilization, respectively) while both GPUs are out-of-memory before the first step. Inference runs at overnight-batch speed (0.6-2.4 tokens/s for the 27B model), and federated fine-tuning would transmit only a 22.6 MB low-rank adapter, a 99.96% reduction over full weights. The capacity result is independent of CPU microarchitecture, RAM size, operating system, and GPU. We further identify an open, CPU- and NPU-optimized inference runtime as the natural production path for this approach, indicating a privacy-preserving and broadly deployable route for on-premises clinical AI. All data is synthetic; this is a research artifact and not a medical device.