Grouped Adaptive Weight Sharing (GAWS): An Inference-Efficient Adaptation Method for Large Language Models

摘要

Although Low-Rank Adaptation (LoRA) revolutionized parameter-efficient fine-tuning, it often incurs an inference overhead due to the extra computation required by adapter layers. While most literature focuses on maximizing accuracy or minimizing parameter counts, this paper prioritizes single-request inference performance in the unmerged adapter setting, where adapters must remain decoupled from the base model at runtime. By analyzing LoRA adapters on GPUs, we identify segmented function calls as the primary source of this latency. To address this, we propose Grouped Adaptive Weight Sharing (GAWS), a novel adapter design based on structured Kronecker product decomposition. Experiments on T5-3B, GPT-2 Large, LLaMA3.2-3B, and RoBERTa-Large show that GAWS reduces latency to about 40% of the gap between the unmerged LoRA and the base model, while maintaining parameter efficiency and comparable accuracy. This positions GAWS as a Pareto-efficient solution for deploying adapted LLMs in latency-sensitive settings, balancing the high latency of compressed adapters with the accuracy of LoRA. The source code is available at:https://github.com/SamsungLabs/GAWS .