Product Quantization

Also known as: PQ

Product quantization compresses high-dimensional vectors by splitting them into subvectors and replacing each subvector with the index of its nearest centroid in a learned codebook. The result is a compact code that enables fast approximate distance computation using lookup tables. PQ can compress 768-dimensional float32 vectors by 8–32× with modest recall loss, allowing billion-scale indexes to fit in RAM that would otherwise require terabytes of storage.