Mosaic introduces projection pruning, a novel fine-grained method that makes large language models more efficient without sacrificing performance.

• Achieves resource efficiency by reducing model size while maintaining quality
• Improves upon existing coarse-grained pruning methods that remove critical parameters
• Enables deployment of powerful LLMs on resource-constrained hardware
• Addresses both engineering and security challenges through optimized resource utilization

This research paves the way for wider LLM deployment across diverse hardware environments, making advanced AI more accessible and practical for real-world applications.

Mosaic: Composite Projection Pruning for Resource-efficient LLMs