Chimera: Efficient Multi-Vector Retrieval via GPU-CPU Co-Processing

[2608.23553] Chimera: Efficient Multi-Vector Retrieval via GPU-CPU Co-Processing Skip to main content Search Submit Donate Log in Computer Science > Databases arXiv:2608.23553 (cs) [Submitted on 24 Aug 2026 ( v1 ), last revised 26 Aug 2026 (this version, v2)] Title: Chimera: Efficient Multi-Vector Retrieval via GPU-CPU Co-Processing Authors: Yanqi Chen , Juelin Liu , Alexandra Meliou , Xiao Yan View PDF HTML (experimental) Abstract: Multi-vector retrieval has become an important primitive for fine-grained matching in information retrieval, with emerging applications in areas such as recommender systems and bioinformatics. However, its high computational complexity and memory costs make low-latency retrieval difficult. Prior systems have attempted to optimize query latency, but their designs remain CPU-centric. While GPUs offer substantial computational advantages, their limited memory capacity necessitates a heterogeneous architecture in which the dataset resides in host memory and the GPU serves as an accelerator. Existing GPU-based system, PLAID, is bottlenecked by CPU-GPU data movement, as vector data must be transferred from host memory to the GPU at query time. We propose Chimera, a GPU-CPU co-processing system for multi-vector retrieval that eliminates this transfer bottleneck. Chimera stores highly compressed, low-precision quantization codes on the GPU while maintaining high-precision data in CPU memory. At query time, it leverages GPU-resident data for efficient candidate generation and filtering, and further refines results through a GPU-CPU collaborative scoring scheme that completely avoids vector data transfer while enabling computation overlap. Experiments on real-world datasets demonstrate that Chimera significantly outperforms existing approaches, achieving up to 59.5x higher QPS at the same recall level. Subjects: Databases (cs.DB) Cite as: arXiv:2608.23553 [cs.DB] (or arXiv:2608.23553v2 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2608.23553 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yanqi Chen [ view email ] [v1] Mon, 24 Aug 2026 17:54:50 UTC (1,587 KB) [v2] Wed, 26 Aug 2026 18:20:07 UTC (1,607 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.DB < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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