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arXiv · 2505.03756

Improving the Serving Performance of Multi-LoRA Large Language Models via Efficient LoRA and KV Cache Management

Hang Zhang·Jiuchen Shi·Yixiao Wang·Quan Chen·Yizhou Shan·Minyi Guo
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Abstract

Multiple Low-Rank Adapters (Multi-LoRAs) are gaining popularity for task-specific Large Language Model (LLM) applications. For multi-LoRA serving, caching hot KV caches and LoRA adapters in high bandwidth memory of accelerations can improve inference performance. However, existing Multi-LoRA inference systems fail to optimize serving performance like Time-To-First-Toke (TTFT), neglecting usage dependencies when caching LoRAs and KVs. We therefore propose FASTLIBRA, a Multi-LoRA caching system to optimize the serving performance. FASTLIBRA comprises a dependency-aware cache manager and a performance-driven cache swapper. The cache manager maintains the usage dependencies between LoRAs and KV caches during the inference with a unified caching pool. The cache swapper determines the swap-in or out of LoRAs and KV caches based on a unified cost model, when the HBM is idle or busy, respectively. Experimental results show that ELORA reduces the TTFT by 63.4% on average, compared to state-of-the-art works.

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Hang Zhang, Jiuchen Shi, Yixiao Wang, Quan Chen, Yizhou Shan, Minyi Guo. 2025-04-19. Improving the Serving Performance of Multi-LoRA Large Language Models via Efficient LoRA and KV Cache Management. https://arxiv.org/abs/2505.03756

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