GPU-Accelerated Vector Index Store

Data storeKnowledge & DataKnowledge & Dataarc:GPUAcceleratedVectorIndexStore

A vector store that builds approximate-nearest-neighbour indices and executes similarity search and metadata filtering in parallel on GPUs.

Responsibility. Serves low-latency vector search and index builds using GPU parallelism.

Also known as: GPU-native vector search

Variant of Vector Index Store abstract

When to choose. Choose for large (up to billion-scale) collections, continuous ingestion with frequent index rebuilds, or multi-hop agent workflows querying many times per request under tight latency.

Direct neighbourhood (hover for relationship types)

Relationships

deployed on structural

GPU Node abstract Ch2.7 Ref2.07

alternative to variability

CPU Vector Index Store Ch2.7

Quantitative guidance

As stated by the sources; verify before use.

10-100x faster similarity search than CPU-only databases; sub-100 ms retrieval at billion scale (Ch2.7).
Index build 10-50x faster: IVF_FLAT for 100M 768-d vectors takes 4 h on CPU vs 12 min on an A100 (Ch2.7).
Sub-10 ms p95 on a 10M-vector collection on an A100 vs 100-300 ms on CPU, including with filter predicates (Ch2.7).

Classification

Patterns: Approximate nearest neighbour indexing
Technologies: MilvusNVIDIA RAPIDS cuVS
Quality attributes: Performance efficiency (ISO/IEC 25010)
Risks mitigated: Retrieval latency bottlenecks

Sources

Ch2.7: T. Nguyen, "Multimodal RAG Approaches," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.7. ISBN: 9798244538229.
Ref2.07: NVIDIA Developer, "Building multimodal AI RAG with LlamaIndex, NVIDIA NIM, and Milvus | LLM app development," YouTube. Accessed: Sep. 26, 2026. [Online Video]. Available: https://www.youtube.com/watch?v=NaT5Eo97_I0