Shrinking Billion-Scale Vector Indexes: Combining Matryoshka Embeddings with Binary Quantization
At enterprise scale, vector search hits a hard physical wall: RAM capacity. Storing 100 million document chunks at 1,536 dimensions in full 32-bit floating-point precision demands over 600 gigabytes of raw memory before allocating a single byte for graph indexes like HNSW. When infrastructure bills outpace retrieval budgets, scaling requires algorithmic compression rather than throwing more high-memory nodes at the cluster. ...