# Exploring Distributed Vector Databases Performance on HPC Platforms: A Study with Qdrant

arXiv: https://arxiv.org/abs/2509.12384 · HTML: https://mlsanity.com/paper/2509.12384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [ZeBROD: Zero-Retraining Based Recognition and Object Detection Framework](https://mlsanity.com/paper/2512.04888/) (2025)

## Abstract (arXiv)

Vector databases have rapidly grown in popularity, enabling efficient similarity search over data such as text, images, and video. They now play a central role in modern AI workflows, aiding large language models by grounding model outputs in external literature through retrieval-augmented generation. Despite their importance, little is known about the performance characteristics of vector databases in high-performance computing (HPC) systems that drive large-scale science. This work presents an empirical study of distributed vector database performance on the Polaris supercomputer in the Argonne Leadership Computing Facility. We construct a realistic biological-text workload from BV-BRC and generate embeddings from the peS2o corpus using Qwen3-Embedding-4B. We select Qdrant to evaluate insertion, index construction, and query latency with up to 32 workers. Informed by practical lessons from our experience, this work takes a first step toward characterizing vector database performance on HPC platforms to guide future research and optimization.