mlsanityData to 7 Oct 2026

Bang for the Buck: Vector Search on Cloud CPUs

arXiv 2505.07621 ↗First posted 12 May 2025cs.DB cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Vector databases have emerged as a new type of systems that support efficient querying of high-dimensional vectors. Many of these offer their database as a service in the cloud. However, the variety of available CPUs and the lack of vector search benchmarks across CPUs make it difficult for users to choose one. In this study, we show that CPU microarchitectures available in the cloud perform significantly differently across vector search scenarios. For instance, in an IVF index on float32 vectors, AMD's Zen4 gives almost 3x more queries per second (QPS) compared to Intel's Sapphire Rapids, but for HNSW indexes, the tables turn. However, when looking at the number of queries per dollar (QP), Graviton3 is the best option for most indexes and quantization settings, even over Graviton4 (Table 1). With this work, we hope to guide users in getting the best "bang for the buck" when deploying vector search systems.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 44 references that are in the map. First posted on arXiv 12 May 2025.

Builds on

17 of 44 references.

References · 17

  1. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
  2. 2018ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithmscited by 130
  3. 2021A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Searchcited by 82
  4. 2021A Survey on Locality Sensitive Hashing Algorithms and their Applicationscited by 40
  5. 2024When Large Language Models Meet Vector Databases: A Surveycited by 39
  6. 2024ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Datacited by 31
  7. 2023High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operationscited by 25
  8. 2023Similarity search in the blink of an eye with compressed indicescited by 20
  9. 2023Vector Search with OpenAI Embeddings: Lucene Is All You Needcited by 12
  10. 2024Locally-Adaptive Quantization for Streaming Vector Searchcited by 10
  11. 2025PDX: A Data Layout for Vector Similarity Searchcited by 8
  12. 2021Low-Precision Quantization for Efficient Nearest Neighbor Searchcited by 6
5 more
  1. 2018uops.info: Characterizing Latency, Throughput, and Port Usage of Instructions on Intel Microarchitecturescited by 5
  2. 2024CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestioncited by 4
  3. 2024Subspace Collision: An Efficient and Accurate Framework for High-dimensional Approximate Nearest Neighbor Searchcited by 4
  4. 2025SQUASH: Serverless and Distributed Quantization-based Attributed Vector Similarity Searchcited by 3
  5. 2024Efficient Data Access Paths for Mixed Vector-Relational Searchcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025VIBE: Vector Index Benchmark for Embeddingscited by 6