Breaking the Cost Barrier: How Quantization Enables Efficient Development and Deployment of LLMs for Public Healthcare

The clinical promise of Large Language Models (LLMs) is often unrealized due to pro-hibitive computational costs. These costs create barriers not only to deployment in patient care but also to the vital process of fine-tuning models for specialized medical tasks and local patient populations. This study investigates 4-bit quantization as a methodology to make the entire clinical AI lifecycle--from development to implementation--both financially and practically viable. We performed a cost-benefit analysis using the Gemma 3 model family on the HealthQA-BR medical benchmark. We compared the diagnostic accuracy and computational resource requirements of standard full-precision models against their 4-bit quantized counterparts during both inference (clinical use) and QLoRA-based fine-tuning (model development). Quantization enabled massive efficiency gains with a clinically negligible impact on performance. For the 12B-parameter model, we observed a mere 1.3% absolute drop in accuracy. In exchange, computational requirements were reduced by 80% for fine-tuning and 69% for inference. This translates to a more than three-fold improvement in performance per unit of computational cost, accelerating research and development cycles. 4-bit quantization is a pivotal enabling technology for clinical AI. By drastically lowering the resource barrier for model customization and deployment, it empowers medical institutions to rapidly develop and validate specialized AI tools on-site. This approach holds particular promise for large-scale public health systems like Brazils SUS and provides a viable blueprint for similar health systems worldwide to transform AI from a theoretical possibility into a practical and equitable reality in patient care.