LoRA Fine-Tuning Without GPUs

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

This project addresses a critical barrier in LLM democratization: the GPU requirement for fine-tuning. Our research develops a theoretically grounded approach that enables effective LoRA fine-tuning on standard laptop CPUs, making advanced AI accessible to researchers with limited computational resources.

Key Innovation

The core breakthrough lies in our meta-learning approach that learns to map any input dataset (represented as a probability distribution) to a set of LoRA weights by leveraging a large bank of pre-trained adapters. Instead of performing new gradient-based updates, our pipeline constructs adapters via lightweight combinations of existing LoRAs directly on CPU.

Technical Highlights

Meta-Operator Design: Novel architecture that maps datasets to optimal LoRA combinations
CPU-Optimized Pipeline: Eliminates GPU dependency while maintaining fine-tuning effectiveness
Theoretical Foundation: Rigorous mathematical framework supporting the meta-learning approach
Practical Implementation: Demonstrated on Mistral-7B-Instruct-v0.2 model

Research Impact

Democratized Access: Makes LLM customization available to researchers without expensive hardware
Sustainable AI: Reduces computational costs and energy consumption
Academic Recognition: Accepted to ICML 2025 Workshop on Efficient Systems for Foundation Models

Technical Stack

Framework: PyTorch, Transformers (Hugging Face)
Model: Mistral-7B-Instruct-v0.2
Optimization: CPU-specific algorithmic improvements
Evaluation: Comprehensive experiments across diverse NLP tasks

“LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs”
Reza Arabpour, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios
Accepted to ICML 2025 Workshop on Efficient Systems for Foundation Models
arXiv:2507.01806

This research represents a significant step toward democratizing AI by reducing computational barriers to LLM customization.