{"paper":{"title":"RAGDoll: Efficient Offloading-based Online RAG System on a Single GPU","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.OS"],"primary_cat":"cs.DC","authors_text":"Junfeng Liu, Ningyi Liao, Siqiang Luo, Weiping Yu","submitted_at":"2025-04-17T15:04:47Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) enhances large language model (LLM) generation quality by incorporating relevant external knowledge. However, deploying RAG on consumer-grade platforms is challenging due to limited memory and the increasing scale of both models and knowledge bases. In this work, we introduce RAGDoll, a resource-efficient, self-adaptive RAG serving system integrated with LLMs, specifically designed for resource-constrained platforms. RAGDoll exploits the insight that RAG retrieval and LLM generation impose different computational and memory demands, which in a traditional s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.15302","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.15302/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}