MRVQ enables one quantized index to serve all dimension-rate combinations by using truncatable residual quantization, reducing memory overhead by 17.8-22x compared to training separate indices, though with modest quality trade-offs.
This paper introduces MRVQ, a quantization method that compresses embeddings for vector search while supporting flexible trade-offs between embedding dimension and compression rate. A single index can be truncated in two ways—dropping quantization stages or embedding coordinates—to adapt to different memory and latency constraints without storing multiple separate indices.