POLAR: Online Learning for LoRA Adapter Caching and Routing in Edge LLM Serving
Edge deployment of large language models (LLMs) increasingly relies on libraries of lightweight LoRA adapters, yet GPU/DRAM can keep only a small resident subset at a time. Serving a request through a non-resident adapter requires paging its weights from storage, incurring measurable latency. This creates a two-timescale online control problem: on a slow timescale, the system selects which adapters remain resident in fast memory, while on a fast timescale it routes each request to an adapter whose context-dependent utility is unknown a priori. The two decisions are tightly coupled: the cache determines the cost of exploration, and the router determines which adapters receive informative f...
| Authors | Shaoang Li, Jian Li |
|---|---|
| Primary category | cs.LG |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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