Mind the Delay: Latency-aware Query Routing for Heterogeneous LLM pools
Large language model (LLM) query routers typically assign each query to a suitable model that balances response accuracy and monetary cost. Subsequently, separate system-level load balancers based on policies such as join-the-shortest-queue or round-robin distribute the queries across replicas of each model to reduce latency. However, decoupling accuracy-cost and latency considerations can lead to suboptimal routing decisions. We introduce the Batch Evolution Aware (BEA) estimator to provide a fine-grained estimate of the latency, that is, the time-to-first-token (TTFT) experienced by an arriving query. Unlike existing load balancers that rely on coarse signals such as queue lengths, per-...
| Authors | Shivam Patel, Akaash R. Parthasarathy, Akash Dhasade et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 May 2026 |
| Last updated at source | 15 October 2026 |
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