This research introduces a novel technique to compress Large Language Models through activation sparsity, enabling powerful AI to run efficiently on edge devices.

• Targets Feed-Forward Networks (FFN) which consume up to 66% of model parameters
• Achieves significant memory and computational savings without sacrificing performance
• Enables deployment of more capable AI models directly on smartphones and other resource-constrained devices
• Reduces dependency on cloud servers while enhancing privacy and reducing response latency

This engineering advancement represents a critical step toward bringing powerful AI capabilities to everyday devices without requiring constant cloud connectivity.

Activation Sparsity Opportunities for Compressing General Large Language Models