Large language models (LLMs) are powerful but slow. Their autoregressive nature, where they generate text one token at a time, creates a bottleneck. Imagine trying to write a novel by only adding one word at a time – it would take forever! That's the challenge LLMs face during inference. However, a clever technique called speculative decoding offers a solution by introducing parallelism. This approach uses a smaller, faster “draft” model to predict multiple tokens at once. The larger, main LLM then verifies these “draft” tokens in parallel, leading to a significant speed boost. However, existing speculative decoding methods often rely on a fixed “draft length,” which can lead to inefficiencies. If the draft model isn't very accurate, the main LLM wastes time verifying incorrect predictions. Conversely, if the draft model is highly accurate, it could be generating even more tokens, further accelerating the process. This is where a new method called AdaEDL (Adaptive Entropy-based Draft Length) comes in. AdaEDL intelligently determines the optimal number of draft tokens based on the confidence level of the draft model. By estimating a lower bound on the acceptance probability of the draft model's predictions, AdaEDL dynamically adjusts the draft length, minimizing wasted effort and maximizing the speedup. Tests across various tasks like creative writing, translation, and summarization show that AdaEDL consistently outperforms existing methods, boosting the token generation rate by a significant margin. Even with larger, more complex LLMs, AdaEDL still delivers an impressive performance increase. Furthermore, AdaEDL is robust and doesn’t require extensive tuning or training on specific datasets, making it easy to integrate into existing LLM systems. This breakthrough not only makes LLMs faster but also paves the way for even more efficient architectures in the future, as it allows us to explore the use of larger draft models, further pushing the boundaries of LLM inference speed.