[Submitted on 17 Oct 2026]
Title:Do We Need the Largest LLMs for Every Clinical Decision?
View a PDF of the paper titled Do We Need the Largest LLMs for Every Clinical Decision?, by Ali H. Lazem and 1 other authors
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Abstract:Large clinical language models often require multi-GPU infrastructure, limiting local deployment. We introduce CERTA, a decision-level statistical certificate that determines when a quantized model may substitute for its native BF16 reference and escalates decisions that cannot be certified to it. Across eleven instruction-tuned models, both general-purpose and domain-adapted, from three families (7-72B), we evaluated 2,500 clinical-verification decisions from de-identified patient contexts (2,484 patients), with patient-disjoint calibration and testing. In the higher-power operational analysis, round-to-nearest 8-bit quantization at BF16 storage served 99.8-100% of held-out decisions across all eleven models; formal finite-sample guarantees were assessed separately using the exact split-sample procedure. In the same operational analysis, the class-conditional 4-bit frontier reduced the amortized, BF16-equivalent deployment cost to 25.8% and 27.3% of the BF16 reference for Qwen2.5-72B and HuatuoGPT-o1-72B, respectively, with measured peak memory of 43.0 and 42.7 GB, below the nominal capacity of a single 48 GB accelerator. Deployment on that specific hardware was not directly tested. Under the exact split-sample certificate, which also accounts for configuration selection, these costs were 26.7% and 27.9%. On external disease named-entity benchmarks against human annotations, the frontier-selected NF4 configurations had a mean absolute F1 change of 0.045 on both datasets, with substantial model-specific variation. CERTA converts compression from a fixed precision choice into a risk-controlled deployment policy for this verification task.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2610.26937 [cs.CL]
(or arXiv:2610.26937v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.26937
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ali Lazem [view email] [v1] Sat, 17 Oct 2026 22:07:43 UTC (344 KB)
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