Large language models (LLMs) like ChatGPT are memory hogs. Their impressive abilities come at the cost of storing vast amounts of data, which can lead to slowdowns and performance bottlenecks, especially during peak usage. But what if there was a clever way to manage this memory more efficiently? New research introduces "KunServe," a novel approach to LLM memory management that significantly boosts performance by strategically borrowing memory usually reserved for the model's own parameters. This "parameter-centric" strategy allows KunServe to handle sudden bursts of requests without the usual lag, keeping those chatbots snappy even when everyone's using them. The trick lies in selectively dropping replicated parameters to free up space for incoming requests, then cleverly using pipeline parallelism to reconstruct the missing pieces from other GPUs. This allows KunServe to handle more requests concurrently, dramatically reducing those frustrating wait times. While this method requires careful coordination to avoid performance hiccups, initial results show up to a 27x reduction in latency compared to traditional methods. This could be a game-changer for LLM deployment, enabling smoother, faster, and more responsive AI experiences.