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arXiv · 2604.07173

InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models

Hongyu Chen·Letian Ruan·Zilin Xu·Yuchen Li·Xinyu Chen·Jingwen Leng·Bingsheng He·Minyi Guo·Shixuan Sun
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Abstract

LoRA enables efficient customization of LLMs and is widely used in multi-tenant and multi-task serving. However, emerging model architectures such as MoE significantly increase LoRA memory cost, making existing coupled LoRA serving designs poorly scalable and prone to tail-latency inflation. We present InfiniLoRA, a disaggregated LoRA serving system that decouples LoRA execution from base-model inference. InfiniLoRA introduces a shared LoRA Server with parallelism-aware execution, SLO-driven provisioning, and critical-path optimizations, including GPU-initiated communication and hardware-specialized LoRA kernels. Experiments show that InfiniLoRA can achieve an average $3.05\times$ increase in serviceable request rate under strict latency SLOs, and improve the percentage of LoRA adapters satisfying the SLO requirement by 54.0\%.

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Hongyu Chen, Letian Ruan, Zilin Xu, Yuchen Li, Xinyu Chen, Jingwen Leng, Bingsheng He, Minyi Guo, Shixuan Sun. 2026-04-08. InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models. https://arxiv.org/abs/2604.07173

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