SwiftEmbed: Ultra-Fast Text Embeddings via Static Token Lookup for Real-Time Applications
This enables real-time embedding applications where sub-5ms latency is critical, such as in high-throughput systems, but it is incremental as it builds on existing embedding methods with optimizations.
The paper tackled the problem of generating text embeddings with ultra-low latency for real-time applications, achieving 1.12 ms p50 latency and 60.6 MTEB average score, which is 89% of contextual model quality.
We present a static token lookup methodology for text embedding generation that achieves 1.12 ms p50 latency for single text embeddings while maintaining 60.6 MTEB average score across 8 representative tasks, corresponding to 89% of contextual model quality. The Rust implementation delivers 50,000 requests per second throughput through static embedding lookup, optimized mean pooling, and zero-copy IEEE754 binary serialization. Evaluation demonstrates exceptional duplicate detection performance (90.1% AP), strong semantic similarity (76.1% Spearman correlation), and domain-specific performance ranging from 75% to 131% of baseline across specialized domains. The system enables real-time embedding applications where sub-5ms latency is critical.