Large Language Models (LLMs) are impressive but computationally expensive. Imagine if an LLM could dynamically choose which parts of its complex calculations to perform, based on the task at hand. This is the promise of dynamic inference, and a new research paper explores how to make it work more efficiently. One approach is called 'dynamic layer skipping,' where the model selectively bypasses certain layers of computation for each input. Researchers found that simply skipping layers strategically is more effective than another method called 'early exiting,' which stops the entire computation early. The surprising discovery was that having individual layers decide to skip based on the current input provided little benefit. It was almost as effective to just have a fixed schedule for skipping each layer. This suggests that the complexity of individual words or phrases isn’t the primary driver of computational need. Instead, the overall complexity of the task or prompt seems more important. The researchers then developed a theoretical 'oracle' system that perfectly allocated computational resources to different prompts, finding it could match the full model's performance using only 23.3% of the computational layers! This points to a future where LLMs could dynamically adjust their effort, becoming much faster and cheaper to run, without sacrificing quality.