GraphRAG on Consumer Hardware: Benchmarking Local LLMs for Healthcare EHR Schema Retrieval
The paper evaluates GraphRAG, a graph-based retrieval-augmented generation model, for healthcare EHR schema retrieval using local large language models. It highlights the challenges of deploying LLMs in regulated environments and presents a systematic benchmarking of various models. The findings indicate that while GraphRAG is feasible on consumer hardware, model selection and retrieval design are crucial for effective deployment.
- ▪GraphRAG extends retrieval-augmented generation to support structured reasoning over complex healthcare data.
- ▪The study benchmarks four models on consumer hardware, revealing significant differences in performance and output quality.
- ▪Local retrieval consistently outperforms global summarization in terms of latency and factual grounding.
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Computer Science > Computation and Language arXiv:2605.20815 (cs) [Submitted on 20 May 2026] Title:GraphRAG on Consumer Hardware: Benchmarking Local LLMs for Healthcare EHR Schema Retrieval Authors:Peter Fernandes, Ria Kanjilal View a PDF of the paper titled GraphRAG on Consumer Hardware: Benchmarking Local LLMs for Healthcare EHR Schema Retrieval, by Peter Fernandes and Ria Kanjilal View PDF HTML (experimental) Abstract:Graph-based Retrieval Augmented Generation (GraphRAG) extends retrieval-augmented generation to support structured reasoning over complex corpora, but its reliability under resource-constrained, privacy-sensitive deployments remains unclear.
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