Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices
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Computer Science > Machine Learning
Title:Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices
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.
| Comments: | Hassan Dbouk and Matthias Reisser contributed equally to this work |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.19528 [cs.LG] |
| (or arXiv:2606.19528v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19528
arXiv-issued DOI via DataCite (pending registration)
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