Puzzle is a framework that dramatically improves LLM inference efficiency while maintaining performance, bridging the gap between state-of-the-art capabilities and practical deployability.

• Uses neural architecture search (NAS) at scale to optimize models with billions of parameters
• Employs hardware-aware optimization to address deployment constraints
• Maintains model capabilities while reducing inference costs
• Enables broader adoption of advanced LLMs in resource-constrained environments

This engineering breakthrough matters because it addresses one of the most significant barriers to LLM adoption: the high computational cost of inference. By making powerful models more accessible and deployable, Puzzle could accelerate the practical application of LLMs across industries.

Puzzle: Distillation-Based NAS for Inference-Optimized LLMs