Top Tweets for #vectorquantization

Day 2 of Milvus Week: Slashing costs with 𝐑𝐚𝐁𝐢𝐭𝐐 quantization 🐰 As engineers, we want it all—higher performance, lower cost, and competitive quality. What if you could compress a vector down to just 𝟏 𝐛𝐢𝐭 𝐩𝐞𝐫 𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧 and still achieve over 𝟗𝟎% 𝐫𝐞𝐜𝐚𝐥𝐥? At first, this sounds almost reckless. But RaBitQ leverages the geometry properties of high-dimensional space and uses a theoretically grounded, unbiased distance estimator to preserve the information for accurate ANN search. To implement RaBitQ, we embedded several low-level optimizations into both open-source Milvus and the fully managed Zilliz Cloud. The result: 𝟑× 𝐟𝐚𝐬𝐭𝐞𝐫 𝐯𝐞𝐜𝐭𝐨𝐫 𝐬𝐞𝐚𝐫𝐜𝐡 𝐰𝐢𝐭𝐡 𝐧𝐨 𝐥𝐨𝐬𝐬 𝐢𝐧 𝐫𝐞𝐜𝐚𝐥𝐥, and dramatically lower infrastructure cost. ⚡ From 𝟐𝟑𝟔 → 𝟖𝟗𝟖 𝐐𝐏𝐒 with quantized queries 🎯 94.7% recall with lightweight refinement 💰 Cutting costs by using only a fraction of the memory 🔗 Learn how it's possible: https://t.co/dLXVLCXLrw