In high-stakes domains such as health and biology, information retrieval systems must ensure accuracy while also supporting equitable access and protecting sensitive data. However, many state-of-the-art biomedical IR solutions rely on proprietary cloud infrastructures, raising concerns over cost, reproducibility, and patient privacy. We present a fully open-source retrieval-augmented question answering framework that accurately manages QA against the entire PubMed collection (over 38M documents) using modest, local hardware available in many academic and hospital settings. Inspired by BioASQ, our system combines sparse and dense retrieval with a lightweight local LLM for evidence-grounded biomedical QA. Experiments show that strong retrieval quality and real-time performance are achievable without reliance on commercial APIs or large GPU clusters. By reducing infrastructure barriers around on-premises data, this work provides a concrete path toward democratizing trustworthy biomedical IR for hospitals, universities, and healthcare organizations worldwide.