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arXiv · 2606.19528

Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices

Hassan Dbouk·Matthias Reisser·Prathamesh Mandke·Likhita Arun Navali·Christos Louizos
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Abstract

Fine-tuning of Large Language Models (LLMs) using Low-Rank Adaptation (LoRA) on an end-user's data offers personalized experiences while keeping data private, but faces severe memory constraints on consumer hardware. Peak memory during fine-tuning often exceeds device limits, especially for models with billions of parameters and long-context training data. This paper introduces a suite of complementary techniques to reduce memory footprint without sacrificing model quality: (1) base model quantization with on-the-fly dequantization, (2) memory-efficient checkpointing combining selective activation caching and disk offloading, (3) softmax approximation using semantically relevant token subsets, and (4) logits masking. Experiments on Llama-3.2 3B and Qwen-2.5 3B demonstrate up to $26\times$ and $28\times$ reduction in peak memory, enabling fine-tuning on resource-constrained devices.

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Hassan Dbouk, Matthias Reisser, Prathamesh Mandke, Likhita Arun Navali, Christos Louizos. 2026-06-17. Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices. https://arxiv.org/abs/2606.19528

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