Balcony introduces a practical framework that addresses computational and latency constraints when deploying Large Language Models by enabling dynamic depth-based inference.

• Preserves model quality while reducing computational requirements by freezing pretrained models and adding lightweight adapters
• Enables flexibility to adjust model behavior based on available resources and performance needs
• Achieves efficiency without complex hardware modifications or significant performance degradation
• Provides practical solution for deploying LLMs in resource-constrained environments

This engineering innovation matters because it creates a path for wider adoption of LLMs in real-world applications where computational resources and latency requirements are strict constraints.

Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models