Imagine asking an AI to describe a scene from a movie. Behind the scenes, massive language models (LLMs) are crunching through tons of video data—a computationally expensive task. But what if we could make these AI giants slimmer and faster without sacrificing their smarts? That's the goal of a new technique called PruneVid. Video data is inherently redundant. Think about a static background in a scene: it doesn't change much from frame to frame, yet traditional LLMs process every frame individually. PruneVid tackles this inefficiency by merging similar visual information across both space and time, essentially compressing the video's essence. It's like creating a highlight reel for the AI. But PruneVid goes further. It leverages the LLM's own reasoning abilities to identify the most important visual cues related to a given question. For example, if you ask, "What happened after the person took the phone?", PruneVid helps the LLM focus on the hand movements and surrounding objects, rather than wasting resources on irrelevant background details. This selective attention allows for drastic pruning of up to 80% of the visual tokens (the pieces of information the LLM processes) while maintaining, and sometimes even improving, accuracy. Tested on several video understanding benchmarks, PruneVid consistently boosted efficiency, reducing processing time and memory usage. This innovation opens doors for faster, more responsive video AI applications, especially on resource-constrained devices. While challenges remain in fine-tuning the balance between pruning and performance, PruneVid represents a significant leap towards making powerful video LLMs more accessible and practical for everyday use.