Large Language Models (LLMs) are impressive, but their speed can be a bottleneck. Generating text word-by-word, like typing one key at a time, limits how quickly they can respond. Researchers are constantly seeking ways to make LLMs faster, and a new technique called FIRP (Faster LLM Inference via Future Intermediate Representation Prediction) offers a promising solution. Instead of predicting just the next word, FIRP attempts to predict several words at once. How? By cleverly anticipating the internal states of the LLM for future words. Think of it like predicting where your fingers will be on the keyboard several keystrokes ahead. This method allows the LLM to process information for multiple words in a single step, drastically reducing processing time. Tests show FIRP can speed up LLM inference by a factor of two to three, depending on the model and the complexity of the task. Interestingly, solving logical problems like math word problems shows even greater speed improvements, hinting that FIRP may be particularly effective in structured, predictable contexts. This research suggests a future where LLMs respond much more quickly, making them even more useful in real-time applications like chatbots and translation tools. While there are still challenges in perfectly predicting these future states, FIRP opens up exciting possibilities for a faster, more efficient AI future.