Imagine having the power of a cutting-edge AI language model right in your pocket. That's the promise of large language models (LLMs), but their massive size makes them difficult to run on resource-constrained devices like smartphones. Current solutions involve shrinking the models, sacrificing accuracy, or constantly swapping data between memory and storage, creating a frustrating bottleneck. Enter Ripple, a groundbreaking new approach that dramatically accelerates LLM inference on smartphones. Ripple leverages a clever insight: neurons within these AI models tend to “fire” together in predictable patterns. By reorganizing how these neurons are stored in flash memory, Ripple allows the phone to read the right data in larger, more efficient chunks. This reduces the constant back-and-forth between memory and storage, unlocking significantly faster performance. Think of it like organizing a library: instead of scattering related books across different shelves, you group them together. When you need information on a specific topic, you can grab all the relevant books at once, saving you time and effort. Ripple achieves this through a two-stage process. First, an offline phase analyzes the LLM to understand which neurons activate together and rearranges them in flash storage for optimal access. Then, during the online phase, Ripple employs smart data access strategies to further improve efficiency. The results are impressive. Tests on various smartphones and LLMs show Ripple improving performance by up to 5.93 times compared to existing methods. This means faster response times, smoother interactions, and a significantly enhanced AI experience on your phone. Ripple represents a major step toward making powerful LLMs accessible to everyone, paving the way for more sophisticated and personalized AI applications on mobile devices.