Large Language Models (LLMs) are impressive, but their size presents challenges for deployment. What if we could make them smaller and faster without sacrificing performance? New research explores a training-free approach to finding optimal subnetworks (subnets) within pre-trained LLMs. This innovative method identifies efficient architectures by calculating the importance of existing weights, similar to finding the most load-bearing parts of a bridge. Instead of laboriously training a new model, researchers use a clever algorithm to “inherit” the best parts of the original LLM. This approach also reduces the need for extensive retraining, saving significant computational resources and time. Think of it as carefully extracting the most powerful engine components from a race car and fitting them into a smaller, more fuel-efficient vehicle. The researchers found their subnets outperformed existing methods in terms of speed and accuracy across different LLM families and sizes, opening up exciting possibilities for deploying LLMs on more devices. While model size will likely continue to grow, these techniques offer a way to make the most of those massive networks by extracting their most efficient cores.