MoDeGPT introduces a novel modular decomposition framework that compresses Large Language Models while maintaining accuracy and minimizing inference overhead.

• Achieves over 2x compression with negligible accuracy loss
• Uses structured matrix decomposition to preserve model architecture
• Outperforms existing compression methods in accuracy-efficiency tradeoffs
• Demonstrates practical deployment benefits with reduced memory footprint

This engineering breakthrough addresses a critical barrier to LLM deployment on resource-constrained devices, making advanced AI more accessible and cost-effective for real-world applications.

MoDeGPT: Modular Decomposition for Large Language Model Compression