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

2025-07-02

AI Summary

Fine-tuning large language models (LLMs) heavily relies on GPU resources, hindering efficient deployment on CPU-only devices. Method: This paper proposes the first CPU-native LoRA meta-generation framework, abandoning gradient-based updates. Instead, it constructs meta-operators from a pre-trained LoRA library, models input task characteristics via probabilistic distribution, and synthesizes adapters on-the-fly through low-rank matrix composition and lightweight weighted fusion. Results: Evaluated on Mistral-7B-Instruct-v0.2, the generated adapters consistently outperform the base model on downstream tasks—though marginally trailing GPU-based fine-tuning—while drastically reducing hardware requirements. This work establishes the first GPU-free paradigm for efficient, task-specific LoRA customization, offering a practical, lightweight, and scalable adaptation approach for resource-constrained environments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Enables LoRA fine-tuning without GPU reliance
• Provides CPU-efficient adapter generation for LLMs
• Offers accessible alternative to GPU-based fine-tuning

Innovation

• CPU-efficient meta-generation for LoRA fine-tuning
• Leverages pre-trained adapters for weight mapping
• Lightweight LoRA combinations on standard CPUs