LoRA Is Slower Than You Think

TL;DR

This paper critically examines LoRA's actual speed benefits in fine-tuning large language models, revealing inconsistencies and proposing more efficient alternatives that maintain performance while improving training speed.

Contribution

The paper provides a comprehensive analysis of LoRA's performance limitations and introduces new methods for more consistent and efficient LLM fine-tuning.

Findings

LoRA does not always improve training speed across different models.

Proposed methods achieve similar or better performance than LoRA.

New approaches offer more consistent training speed improvements.

Abstract

Low-Rank Adaptation (LoRA) is one of the most widely used techniques for fine-tuning large language models (LLMs). By introducing a small number of trainable low-rank weight matrices, LoRA substantially reduces the number of parameters that need to be updated, offering significant advantages in memory consumption and computational efficiency compared to full fine-tuning. However, we observed that LoRA does not consistently provide speed improvements across all model architectures and training setups. Motivated by this inconsistency, we conduct a comprehensive analysis of LoRA's performance and investigate the underlying factors limiting its speedup. Based on our findings, we propose several methods for more efficient fine-tuning of LLMs. We empirically evaluate these methods and compare them to LoRA, demonstrating that our approach achieves comparable or superior performance while delivering more consistent training speed improvements. Our work offers valuable insights and practical guidelines for practitioners seeking to optimize LLM fine-tuning under resource constraints.