How do you reduce serving costs without making search quality collapse?
Milvus(@milvusio)138 字 (约 1 分钟)
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RaBitQ algorithm compresses float32 vectors to 1 bit per dimension through random rotation, significantly reducing RAM and SSD costs for vector search without quality loss. Jiang Chen, Zilliz Head of Developer Relations, shared this at London Unstructured Data Meetup.
入选理由：向量搜索成本高主要源于索引存储消耗大量RAM和NVMe SSD资源
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