{"repo":"vector-index-bench/vibe","free":true,"listed":false,"github":"https://github.com/vector-index-bench/vibe","clone":"git clone https://github.com/vector-index-bench/vibe.git","description":"Vector Index Benchmark for Embeddings (VIBE) is an extensible benchmark for approximate nearest neighbor search methods, or vector indexes, using modern embedding datasets.","language":"Python","stars":53,"topics":["approximate-nearest-neighbor-search","benchmark","embedding-vectors","embeddings","nearest-neighbor-search","python","vector-database","vector-index","vector-search"],"license":"MIT","category":"databases-storage","readme_excerpt":"Vector Index Benchmark for Embeddings (VIBE) is an extensible benchmark for approximate nearest neighbor search methods, or vector indexes, using modern embedding datasets. Overview - 📊 Modern vector index benchmark with embedding datasets - 🎯 Includes datasets for both in-distribution and out-out-distribution settings - 🏆 Includes the most comprehensive collection of state-of-the-art vector search algorithms - 💎 Support for quantized datasets in both 8-bit integer and binary precision - 🖥️ Support for HPC environments with Slurm and NUMA - 🚀 Support for GPU algorithms Results The current VIBE results can be viewed on our website: https://vector-index-bench.github.io The website also features several other tools and visualizations to explore the results. The results are run on Intel Xeon Gold 6230 (Cascade Lake) CPUs with support for AVX-512 instructions. All algorithms are benchmarked using a single core. The GPU algorithms are run using an NVIDIA V100 (32 GB). The next results update will use AMD Turin 9965 CPUs, while GPU algorithms will be run using NVIDIA GH200 (96 GB). Publication E. Jääsaari, V. Hyvönen, M. Ceccarello, T. Roos, M. Aumüller. VIBE: Vector Index Benchmark for Embeddings. arXiv preprint arXiv:2505.17810 , 2025. Authors VIBE is maintained by Elias Jääsaari, Matteo Ceccarello, and Martin Aumüller. Alternative Benchmarks Please check out big-ann-benchmarks (NeurIPS 2021/2023) for the state-of-the-art in billion-scale ANN and constrained ANN, such as ANN","default_branch":null,"files":null,"tree":[],"storefront":"/r/vector-index-bench","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/vector-index-bench/vibe/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}