Lodestar: An Online-Learning LLM Inference Router
Efficiently serving large language model (LLM) inference tasks is crucial both for user-perceived latency such as time-to-first-token (TTFT) and for GPU utilization. However, LLM request routing, that is, assigning each inference request to a GPU instance, is particularly challenging: execution is highly input-dependent; batching and KV-cache reuse create strong cross-request coupling; and latency responds nonlinearly to context length, model/engine settings, and heterogeneous accelerators. As a result, simple traditional load balancing algorithms, and even heuristics tailored for LLM inference, fail to achieve good performance. We present Lodestar, a novel learning-based request routing ...
| Authors | Gangmuk Lim, Wanyu Zhao, Brighten Godfrey et al. |
|---|---|
| Primary category | cs.DC |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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