This New Vector Quantization Breaks the Memory vs Recall Trade-off 🚀

Vector search performance often comes down to a painful trade-off: memory usage versus recall. In Milvus 2.6, we introduced RaBitQ — a new quantization method designed to break that trade-off. It significantly reduces memory usage for high-dimensional vectors while keeping recall loss minimal: • 10%+ higher accuracy compared to traditional binary quantization • 2–3× faster search than product quantization • 90%+ recall when combined with refine (up to ~95% on some datasets) • Up to 30× lower memory usage This makes RaBitQ especially attractive for large-scale production workloads where cost and performance both matter. 📺 Full Milvus 2.6 webinar replay: https://www.youtube.com/watch?v=Guct-UMK8lw ▼ ▽ JOIN THE COMMUNITY - MILVUS Discord channel Join this active community of Milvus users to get help, learn tips and tricks on how to use Milvus, or just get to be part of a vibrant community of smart developers! https://discord.com/invite/8uyFbECzPX ▶ CONNECT WITH US X: https://twitter.com/zilliz_universe LINKEDIN: https://www.linkedin.com/company/zilliz/ WEBSITE: https://zilliz.com/ PODCAST: https://creators.spotify.com/pod/show/chloe-williams8/episodes/Inside-the-AI-Agent-Revolution-e2ug0dh/a-abp1mtd