Qdrant Builds a 10-Billion-Document Vector Search Benchmark Dataset on Vultr

Qdrant has released Qdrant-Fineweb-10B, a public benchmark dataset containing dense and sparse embeddings for 10 billion FineWeb documents and exact ground-truth results for 120,000 queries up to a depth of 1,000, enabling developers to evaluate vector search systems under production-scale conditions. Intended for applications such as retrieval-augmented generation, semantic search, recommendation engines, and AI agents, the dataset addresses limitations of smaller or synthetic benchmarks by supporting measurement of retrieval accuracy, indexing, memory, sharding, and scaling behavior on real-world data. Qdrant generated the embeddings using Alibaba’s gte-multilingual-base model on Vultr infrastructure, combining parallel CPU tokenization, GPU processing, and object storage to sustain roughly 19,000 rows per second per node. The full workflow processed the corpus in about five days, producing approximately 25 TB across 500,000 files, and highlights the importance of coordinating compute, preprocessing, and storage for billion-record AI workloads.

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