Enough with the Bad Benchmarks: Tools for Production-Grade Research

Qdrant Labs, in partnership with Vultr, has released Qdrant-FineWeb-10B, an open-source vector-search benchmark containing more than 10 billion dense and sparse vectors, roughly 25 TiB of vector data, and exact top-1,000 nearest-neighbor ground truth generated through more than a quadrillion distance calculations. The release aims to address limitations of smaller, synthetic, proprietary benchmarks by supporting evaluation of high-recall, high-throughput, low-latency production workloads, including filtering and deep retrieval. Qdrant also introduced PubMed-Multi-Vector for comparing dense, sparse, and ColBERT-style hybrid retrieval over a shared corpus, and Coyo-Vector-Embeddings for multimodal image-text retrieval. Accompanying the datasets is Supernova, a fully open-source distributed framework that automates embedding generation, brute-force ground-truth computation, database ingestion, and benchmark evaluation across systems including Qdrant, Milvus, and Elasticsearch. Supernova uses GPU-native processing and SkyPilot-based infrastructure orchestration to scale workloads across major cloud providers, Kubernetes, and HPC clusters through shared YAML configurations.

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