Large Language Models (LLMs) are transforming how we interact with AI, but their complexity can lead to slow response times, especially when integrating external data sources through APIs. Imagine asking an AI assistant a question that requires pulling information from the web. The delay while the AI fetches and processes that information can be frustrating. Researchers have been grappling with this latency issue, and a new framework called LAMPS offers a promising solution. LAMPS stands for LLM API- and Memory-based Predictive Scheduling. In simpler terms, it's like a smart traffic controller for your AI's brain. It predicts how long different parts of a request will take, including API calls, and prioritizes them based on their memory usage over time. This is crucial because LLM operations are often memory-bound, meaning the availability of memory directly affects how quickly the AI can respond. Traditional methods either process requests in the order they arrive (first-come, first-served) or prioritize based on the expected length of the response. But these approaches become inefficient with API calls, where a short request might involve a long API call, or a longer request might need minimal external data. LAMPS tackles this challenge by considering both the length of the request and how it handles memory during API calls. It predicts whether it's more efficient to keep information in memory while waiting for an API response, discard it and recompute later, or temporarily offload it to free up space. The researchers tested LAMPS on various datasets, comparing it to existing systems like vLLM and INFERCEPT. The results? LAMPS significantly reduced both average and worst-case latency, often by a substantial margin. It also improved throughput, meaning it can handle more requests per second. This makes LAMPS a significant step towards making LLMs faster and more responsive, paving the way for a smoother and more interactive AI experience. While challenges remain in terms of prediction accuracy and handling complex multi-API requests, LAMPS provides a foundation for future research and development in optimizing LLM performance.