PromptDistill is a training-free method that improves LLM inference efficiency by selectively retaining only the most informative tokens in intermediate processing layers.

• Identifies critical tokens based on attention interactions in early transformer layers
• Preserves important hidden states while reducing computational burden
• Achieves significant inference speedup without requiring model retraining
• Maintains generation quality while addressing memory and computational bottlenecks

This engineering breakthrough directly tackles one of the biggest practical limitations in deploying large language models: the high computational cost of processing long documents and complex tasks. By intelligently managing token processing, PromptDistill makes efficient LLM deployment more accessible.

PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model Inference