GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search

• URL: http://arxiv.org/abs/2602.23999v1
• Date: Fri, 27 Feb 2026 13:23:30 GMT
• Title: GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search
• Authors: Jifan Shi, Jianyang Gao, James Xia, Tamás Béla Fehér, Cheng Long,
• Abstract summary: IVF-RaBitQ is a GPU-native ANNS solution that integrates the cluster-based method IVF with RaBitQ quantization into an efficient GPU index build/search pipeline.<n>We show that IVF-RaBitQ offers a strong performance frontier in recall, throughput, index build time, and storage footprint.
• Score: 6.459073253087106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Approximate nearest neighbor search (ANNS) on GPUs is gaining increasing popularity for modern retrieval and recommendation workloads that operate over massive high-dimensional vectors. Graph-based indexes deliver high recall and throughput but incur heavy build-time and storage costs. In contrast, cluster-based methods build and scale efficiently yet often need many probes for high recall, straining memory bandwidth and compute. Aiming to simultaneously achieve fast index build, high-throughput search, high recall, and low storage requirement for GPUs, we present IVF-RaBitQ (GPU), a GPU-native ANNS solution that integrates the cluster-based method IVF with RaBitQ quantization into an efficient GPU index build/search pipeline. Specifically, for index build, we develop a scalable GPU-native RaBitQ quantization method that enables fast and accurate low-bit encoding at scale. For search, we develop GPU-native distance computation schemes for RaBitQ codes and a fused search kernel to achieve high throughput with high recall. With IVF-RaBitQ implemented and integrated into the NVIDIA cuVS Library, experiments on cuVS Bench across multiple datasets show that IVF-RaBitQ offers a strong performance frontier in recall, throughput, index build time, and storage footprint. For Recall approximately equal to 0.95, IVF-RaBitQ achieves 2.2x higher QPS than the state-of-the-art graph-based method CAGRA, while also constructing indices 7.7x faster on average. Compared to the cluster-based method IVF-PQ, IVF-RaBitQ delivers on average over 2.7x higher throughput while avoiding accessing the raw vectors for reranking.

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