New latency-aware router boosts LLM accuracy-cost by 40%
Lightweight estimator predicts time-to-first-token for smarter query routing
Deep Dive
Researchers developed a latency-aware LLM query router that jointly optimizes latency, accuracy, and cost. Their lightweight estimator simulates autoregressive token batch processing to predict time-to-first-token (TTFT). Results show up to 40% improvement in accuracy-cost utility while maintaining the same latencies as standard load-balancing approaches.
Key Points
- Lightweight latency estimator simulates token batch processing to predict time-to-first-token (TTFT) for each query
- Router jointly optimizes latency, accuracy, and inference cost—unlike existing routers that ignore latency
- Up to 40% improvement in accuracy-cost utility while matching standard load-balancing latency levels
Why It Matters
Enables real-time AI applications to reduce inference costs without degrading user-perceived latency.