![[PWL PHL] Lou Kratz on Locally Optimized Product Quantization](https://i.ytimg.com/vi/RgxCaiQ-kig/mqdefault.jpg)
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Last Updated: October 17, 2026
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In this video, we talk about a vector compression technique called Vector similarity search can require huge amounts of memory. Indexes containing 1M dense vectors (a small dataset in today's ... Are you struggling with high-dimensional data in your vector database? In this video, we dive deep into Unlike tree-based indexes used for ANN, a k-NN search with a 11:20 Scalar Quantization 23:30 Locally-Adaptive Quantization 31:56 How do we store millions of AI vectors without using massive storage? In this video, I explain how 100 million vectors × 3072 dimensions × 4 bytes = 1.2 terabytes. That's just the vectors. Not the metadata, not the index. And ... FDE Full Course In the previous video, we understood why we need Vector Databases and explored KNN and Vector Search. Lou Kratz presents the paper Locally Optimized Jegou, Herve, Matthijs Douze, and Cordelia Schmid. "
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