Local-First Information Retrieval: Keeping Search Private on Consumer Hardware
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[Submitted on 28 Jun 2026]
Summary
This paper proposes "local-first IR" (information retrieval), a design philosophy where search indexes, models, and inference all reside on user devices rather than remote servers, addressing privacy concerns in retrieval-augmented generation systems. The authors present a framework organizing retrieval architectures along three dimensions (privacy/control, capability, and accessibility), and share experimental results on consumer hardware across five benchmarks scaling from 1K to 1M documents. Key findings include dense retrieval maintaining over 91% nDCG@10 up to 100K documents, approximate HNSW indexes extending to 1M documents with only 2% quality loss, and a 7B local language model reaching within 4 points of a cloud baseline on answer quality. The paper argues the real tradeoff is scope rather than quality — what matters is what you can search, not how well you can search it.
Source
Twitter / XLocal-First Information Retrieval: Keeping Search Private on Consumer Hardwarearxiv.orgKey quotes
· 5 pulledThe sensitive information in personal documents, legal files, and medical records is among the most valuable things to search, yet current retrieval-augmented generation systems still require sending content to remote servers.
We propose local-first IR, a design philosophy where indexes, models, and inference reside on user devices, treating remote services as optional.
Dense retrieval keeps over 91% nDCG@10 up to 100K documents, with approximate HNSW indexes extending this to 1M with only 2% quality loss.
The real tradeoff is scope rather than quality: what matters is what you can search, not how well you can search it.
A 7B local language model reaches within 4 points of a cloud baseline on answer quality.
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