[Paper] When More Cores Hurts: The Vector Database Scaling Paradox in HPC

Source: arXiv - 2606.08950v1

Overview

Vector databases have been designed and optimized for cloud environments; however, emerging scientific AI workloads (e.g., molecular search, meteorological trajectory detection, and literature-driven hypothesis generation) demand efficient, scalable execution on HPC systems. We present a large-scale evaluation of three state-of-the-art vector databases — Qdrant, Milvus, and Weaviate — on two production supercomputers, scaling to 256 distributed workers across 64 compute nodes. We evaluate representative workload patterns — mixed read/write and write-then-read — using popular benchmarks, multimodal embeddings, and a novel real-world scientific dataset. Our results reveal that workload characteristics can limit latency reduction, additional cores can reduce query throughput by up to 30.67%, and scaling from 16 to 256 workers (16x) only yields a 5.46x improvement. This scaling paradox exposes the fundamental mismatch between cloud-oriented designs and HPC systems, highlighting the need for new, HPC-aware vector database designs.

Key Contributions

This paper presents research in the following areas:

• cs.DC
• cs.DB

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.DC.

Authors

• Seth Ockerman
• Song Young Oh
• Amal Gueroudji
• Rochana Chaturvedi
• Philip Carns
• Nicholas Chia
• Matthieu Dorier
• Robert Latham
• Tanwi Mallick
• Swan Perarnau
• Robert Underwood
• Kyle Chard
• Ian Foster
• Robert Ross
• Shivaram Venkataraman

Paper Information

• arXiv ID: 2606.08950v1
• Categories: cs.DC, cs.DB
• Published: June 8, 2026
• PDF: Download PDF