Large language models (LLMs) are amazing, but they can be slow, especially when dealing with long chunks of text. This is largely due to the memory bottleneck created by the key-value (KV) cache, which stores information to avoid redundant calculations. Existing methods try to solve this by focusing on the most important parts of the text (TopK attention), but this approach can actually hurt performance in certain tasks. A new research paper introduces MagicPIG, a clever system that uses a technique called Locality Sensitive Hashing (LSH) to sample information more efficiently. Instead of just picking the 'top' pieces of data, MagicPIG uses smart sampling to get a more accurate representation of the entire text. This not only leads to better performance in tasks that require understanding the whole context, but also makes LLM generation up to 5 times faster. MagicPIG cleverly splits the workload between the CPU and GPU, allowing it to handle longer text inputs and bigger batches of data. Tests show that MagicPIG maintains high accuracy across various tasks, even with significantly reduced computational effort. This breakthrough opens exciting possibilities for faster and more efficient LLMs.