GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search
Summary: GPU-native IVF-RaBitQ combines IVF clustering with low-bit RaBitQ quantization for fast, compact ANNS without raw-vector reranking. Fused GPU kernels deliver ~3× CAGRA QPS, 14.7× faster builds, and >4.5× IVF-PQ throughput at ~0.95 recall. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Jifan Shi (Nanyang Technological University)
- 2. Jianyang Gao (Nanyang Technological University)
- 3. James Xia (NVIDIA)
- 4. Tamás Béla Fehér (NVIDIA)
- 5. Cheng Long (Nanyang Technological University)
BibTeX Citation
@article{shi_vldb26,
title = {{GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search}},
author = {Shi, Jifan and Gao, Jianyang and Xia, James and Fehér, Tamás Béla and Long, Cheng},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3454--3467},
doi = {10.14778/3836663.3836701},
url = {https://doi.org/10.14778/3836663.3836701},
year = {2026}
}
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