Milvus 教你怎么省内存，效果实测
SK Hynix 股价自2025年底涨近三倍，内存成本成向量搜索痛点。Milvus 提供 IVF_RABITQ 索引，在 1000 万 768 维向量基准中达到 94.7% 召回率，QPS 比 IVF_FLAT 高 3.6 倍，向量内存仅用约 1/32。还支持 SQ8/PQ 量化、mmap 按需分页、分层存储及 DiskANN 将索引移到 SSD，多种技术可叠加使用。
Korean memory stocks are going crazy. SK Hynix has nearly tripled since the end of 2025. If you run ...
Korean memory stocks are going crazy. SK Hynix has nearly tripled since the end of 2025. If you run vector search at scale, memory is often one of the biggest cost drivers: billions of embeddings, indexes kept hot, and serving nodes sized around RAM. 𝗠𝗶𝗹𝘃𝘂𝘀 𝗴𝗶𝘃𝗲𝘀 𝘆𝗼𝘂 𝘀𝗲𝘃𝗲𝗿𝗮𝗹 𝘄𝗮𝘆𝘀 𝘁𝗼 𝗿𝗲𝗱𝘂𝗰𝗲 𝗺𝗲𝗺𝗼𝗿𝘆 𝗽𝗿𝗲𝘀𝘀𝘂𝗿𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗴𝗶𝘃𝗶𝗻𝗴 𝘂𝗽 𝘃𝗲𝗰𝘁𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲: • 𝗜𝗩𝗙_𝗥𝗔𝗕𝗜𝗧𝗤 • Select it as your index type. Compress vectors to 1 bit per dimension. In a Milvus 2.6 benchmark on 10M 768-dimensional vectors, IVF_RABITQ reached 𝟵𝟰.𝟳% 𝗿𝗲𝗰𝗮𝗹𝗹 𝘄𝗶𝘁𝗵 𝟯.𝟲𝘅 𝗵𝗶𝗴𝗵𝗲𝗿 𝗤𝗣𝗦 𝘁𝗵𝗮𝗻 𝗜𝗩𝗙_𝗙𝗟𝗔𝗧, 𝘄𝗵𝗶𝗹𝗲 𝘂𝘀𝗶𝗻𝗴 𝗿𝗼𝘂𝗴𝗵𝗹𝘆 𝟭/𝟯𝟮 𝗼𝗳 𝘁𝗵𝗲 𝘃𝗲𝗰𝘁𝗼𝗿 𝗺𝗲𝗺𝗼𝗿𝘆. • 𝗦𝗤𝟴 / 𝗣𝗤 • Use lighter quantization when you need a tighter recall-cost balance. These options trade some precision for lower memory usage without going all the way to 1-bit compression. • 𝗺𝗺𝗮𝗽 • Use memory-mapped I/O so vector data can be paged in on demand instead of loading everything into RAM upfront. Useful when your dataset is much larger than the hot working set. • 𝗧𝗶𝗲𝗿𝗲𝗱 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 • Keep hot data close to compute, move colder data down to cheaper storage, and avoid paying memory prices for data that is rarely queried. • 𝗗𝗶𝘀𝗸𝗔𝗡𝗡 • Move more of the index path to SSD, reducing DRAM dependency for large-scale datasets that do not fit cleanly in memor milvus.io/blog/turboquan… aren't mutually exclusive; they stack up. 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗲 𝘁𝗵𝗲𝗺 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿, 𝗮𝗻𝗱 𝘆𝗼𝘂𝗿 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗰𝗼𝘀𝘁𝘀 𝗱𝗼𝗻'𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝘄𝗶𝘁𝗵 𝗺𝗲𝗺𝗼𝗿𝘆 𝗽𝗿𝗶𝗰𝗲𝘀. → 𝗙𝘂𝗹𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻: https://t.co/Liz0wHgxEB 💬 0 🔄 0 ❤️ 0 👀 73 ⚡