AI BriefingAsymmetric Quantization: Near-Lossless Retrieval with 97% Storage Savings
Key point
Introduces a technique that uses asymmetric quantization to dramatically lower the storage cost of Late Interaction models while preserving retrieval quality.
Details
Late Interaction models (e.g., ColBERT) preserve fine-grained document information to improve retrieval accuracy, but they have the drawback of very high storage costs since they must store multiple vectors.
To solve this, Mixedbread Search introduced Asymmetric Quantization technology. This approach keeps query vectors at high precision while storing document vectors as Binary (1-bit) codes, maximizing efficiency.
The key results are as follows:
- Storage savings: Achieves an average 32x reduction in storage space per document, cutting it from 393 KiB to 12.28 KiB.
- Retrieval quality maintained: Compared to FP32 precision, NDCG@10 shows only a very minor drop, from 90.26 to 89.65.
- Cost-effectiveness: While still using the Multi-vector approach, storage costs are lowered to Single-vector levels, making Late Interaction models practical for large-scale search engines.
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