SHARP is a novel technique that accelerates LLM inference by intelligently sharing parameters across adjacent layers while maintaining model quality through strategic recovery parameters.

• Reduces memory requirements by sharing transformer parameters between adjacent layers
• Introduces low-rank recovery parameters to preserve model capabilities
• Achieves significant inference speedup with minimal performance degradation
• Enables more efficient deployment on resource-constrained devices like mobile phones

This engineering breakthrough addresses a critical challenge in LLM deployment, making advanced AI more accessible across a wider range of hardware environments without sacrificing capabilities.

SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters