This research introduces a novel framework that achieves 2.2x compression ratio for already-quantized large language models, making them viable for memory-constrained environments.

• Implements compression-aware quantization that rescales model parameters before quantization
• Incorporates a pruning methodology specifically designed for post-quantization compression
• Maintains model performance while significantly reducing memory requirements
• Enables deployment of powerful LLMs on devices with limited resources

This breakthrough addresses a critical engineering challenge in AI deployment, making advanced language capabilities accessible on a wider range of hardware without performance degradation.

When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models