LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

• URL: http://arxiv.org/abs/2507.01806v1
• Date: Wed, 02 Jul 2025 15:24:47 GMT
• Title: LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs
• Authors: Reza Arabpour, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios,
• Abstract summary: Low-Rank Adapters (LoRAs) have transformed the fine-tuning of Large Language Models (LLMs) by enabling parameter-efficient updates.<n>We propose a theoretically grounded approach to LoRA fine-tuning designed specifically for users with limited computational resources.
• Score: 8.397730500554047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-Rank Adapters (LoRAs) have transformed the fine-tuning of Large Language Models (LLMs) by enabling parameter-efficient updates. However, their widespread adoption remains limited by the reliance on GPU-based training. In this work, we propose a theoretically grounded approach to LoRA fine-tuning designed specifically for users with limited computational resources, particularly those restricted to standard laptop CPUs. Our method learns a meta-operator that maps any input dataset, represented as a probability distribution, to a set of LoRA weights by leveraging a large bank of pre-trained adapters for the Mistral-7B-Instruct-v0.2 model. Instead of performing new gradient-based updates, our pipeline constructs adapters via lightweight combinations of existing LoRAs directly on CPU. While the resulting adapters do not match the performance of GPU-trained counterparts, they consistently outperform the base Mistral model on downstream tasks, offering a practical and accessible alternative to traditional GPU-based fine-tuning.

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