The October 28, 2025 Samsung research paper introduces **zFLoRA (zero-latency fused low-rank adapter)**, a novel parameter-efficient fine-tuning (PEFT) method designed to address the significant inference latency overheads associated with current adapter methods like **LoRA** in large language models (LLMs). The core contribution is a carefully engineered fusion of adapter blocks with the base model to achieve **zero or negligible latency overhead** during inference, leveraging optimized matrix multiplication on hardware like **NVIDIA H100 GPU** and **Samsung Galaxy S25+ NPU**. Experimental results across LLMs ranging from 1B to 7B parameters demonstrate that zFLoRA maintains **performance comparable to LoRA and Full Fine-Tuning (FFT)** across reasoning and generation tasks, while effectively eliminating the latency penalty, as visually confirmed by accompanying bar graphs. The paper details the architectural design of zFLoRA, which avoids costly expansion and merge operations present in naive fused adapter designs, and includes extensive **latency measurements** validating its efficiency on various platforms.

Source:

https://arxiv.org/pdf/2510.25784