BitStack offers a groundbreaking approach to compress Large Language Models dynamically based on available device memory, enabling broader deployment of AI capabilities.

• Variable compression ratios that adapt to device constraints without requiring multiple model versions
• Bit-level precision control that prioritizes important weights with higher precision
• On-the-fly adjustment capabilities that respond to changing memory availability during runtime
• Minimal performance degradation while achieving significant memory savings

This engineering innovation addresses a critical bottleneck in AI deployment, shifting focus from model capability to accessibility across diverse hardware environments.

BitStack: Any-Size Compression of Large Language Models in Variable Memory Environments