Large language models (LLMs) are revolutionizing how we interact with technology, generating human-quality text for everything from chatbots to creative writing. However, this power comes at a cost: LLMs are computationally intensive and can be slow. What if there was a way to make them significantly faster without sacrificing their impressive capabilities? New research explores exactly that. Scientists have developed a clever technique called Threshold-based Dynamic Activation (TDA) that makes LLMs generate text up to 25% faster! How does it work? The key lies in a surprising discovery about how LLMs process information. When generating text, LLMs activate various internal components (“neurons") as they process each word. However, it turns out that not all of these neurons are essential for generating coherent text. Some "lazy neurons" contribute little to the process. TDA leverages this insight by intelligently predicting which neurons are likely to be unimportant for a given part of a sentence and simply deactivating them. Previous attempts to deactivate neurons often required extra training and could even degrade the quality of the generated text. The beauty of TDA is that it doesn't require any extra training; it can be applied to existing LLMs right out of the box! TDA also uses clever tricks like strategically re-using activation patterns from the beginning of a text to speed up later processing. The results are impressive. In tests on a variety of LLMs, TDA consistently sped up text generation by 18-25% with only minimal changes to the accuracy of the output. This improvement in speed has significant implications for using LLMs in real-time applications like chatbots, translation, and code generation. While this research marks an important step forward, there’s still much to explore. Scientists are investigating how sequence information influences neuron activation. Further work will focus on dynamically adjusting the depth of the model during text generation, potentially unlocking even greater speed improvements. TDA is a step forward and shows how making smarter use of existing LLM resources can unlock significant gains in speed and efficiency.