Position-Aware Depth Decay Decoding ($D^3$) introduces a novel approach that makes large language models 1.5x more computationally efficient during inference while preserving output quality.

• Implements token-position aware layer skipping that dynamically reduces computational depth
• Achieves significant operational savings through a training-free pipeline
• Maintains performance quality while reducing resource requirements
• Offers practical solution to the growing inference costs of large language models

This engineering innovation addresses a critical challenge in LLM deployment: reducing computational demands without compromising model capabilities—making advanced AI more accessible and sustainable.

Position-Aware Depth Decay Decoding ($D^3$): Boosting Large Language Model Inference Efficiency