ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring
Abstract
A serving system for late-interaction retrieval models uses memory-mapped indexes and multi-stage hybrid scoring to cut RAM usage and latency for concurrent queries.
We study serving retrieval models, specifically late interaction models like ColBERT, to many concurrent users at once and under a small budget, in which the index may not fit in memory. We present ColBERT-serve, a novel serving system that applies a memory-mapping strategy to the ColBERT index, reducing RAM usage by 90% and permitting its deployment on cheap servers, and incorporates a multi-stage architecture with hybrid scoring, reducing ColBERT's query latency and supporting many concurrent queries in parallel.
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