Unverified60% confidenceBenchmarkExact time
HNSW算法通过构建多层图索引，在十亿级向量中实现毫秒级检索，同时保持95%以上召回率
2
Sources
60%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
Related Claims
Unverified主流ANN算法包括基于图结构的HNSW和基于量化的IVF-PQ，HNSW以更高内存占用换取更优召回率，IVF-PQ通过压缩降低存储成本67% similarUnverifiedEmbedding模型输出的高维向量通常为768至3072维，向量数据库通过HNSW等图算法可将亿级向量检索压缩至毫秒级响应66% similarUnverified百万Token级别的单次推理需要数十至数百GB的高端GPU显存（A100/H100级别）66% similarUnverifiedHNSW通常召回率更高但内存消耗大，IVF构建更快但需要更多nprobe参数调优65% similarUnverifiedLLM推理的Prefill阶段为计算密集型且GPU利用率接近100%，Decode阶段为内存带宽密集型，瓶颈在于HBM显存带宽63% similar
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