In the fast-paced world of interactive AI, users expect instant responses. Large Language Models (LLMs), while powerful, face challenges in meeting these real-time demands. Enter TRAIL, a new technique designed to make LLMs faster and more responsive. Imagine a busy restaurant. The traditional "first-come, first-served" approach can lead to long waits if a large group arrives before you. Similarly, with LLMs, longer requests can hold up shorter ones, causing delays. TRAIL tackles this issue by predicting the "size" of incoming LLM requests—how long it will take to generate a response—and prioritizing shorter tasks. This avoids the "head-of-line blocking" problem, where short requests get stuck behind longer ones. But how does TRAIL predict request size? The magic lies in the LLM itself! As the LLM processes a request, TRAIL analyzes its internal workings (layer embeddings) to dynamically refine its prediction of the remaining processing time. This continuous refinement ensures that the system always prioritizes the quickest tasks, maximizing efficiency. To further enhance performance, TRAIL introduces limited preemption, allowing short requests to jump ahead only in the early stages of processing. This avoids the memory overhead associated with frequent interruptions, as storing the state of partially completed requests consumes valuable GPU memory. TRAIL offers a significant boost in LLM responsiveness, making interactive AI applications smoother and more engaging. It's like giving the restaurant host the ability to quickly estimate dining times and seat smaller groups first, ensuring a quicker turnaround for everyone.