Large Language Models (LLMs) are revolutionizing how we interact with technology, powering everything from chatbots to AI-driven code generation. But these powerful models have a hidden Achilles' heel: memory management. As conversations with LLMs get longer and more complex, managing the massive memory required for context becomes a bottleneck, slowing down response times and limiting the number of users a system can handle. Researchers have been grappling with this challenge, exploring techniques like recomputing previous interactions or swapping memory between fast and slow storage. However, these methods come with their own drawbacks: recomputing wastes valuable processing power by repeating calculations, while swapping can create performance bottlenecks and load imbalances across servers. Imagine a busy call center where some operators are overloaded while others sit idle – the overall efficiency plummets. This is where Symphony comes in, a novel system designed to orchestrate LLM memory with finesse. Symphony’s secret weapon? It leverages a simple yet powerful insight: in many LLM applications, there are subtle hints that predict when a user is about to make a new request. Think of a chatbot: the moment a user starts typing, it’s a strong signal that a new query is imminent. Symphony capitalizes on these “advisory requests” to preemptively fetch the necessary memory into the fastest storage tier, ensuring the LLM is ready to respond without delay. But Symphony doesn’t stop there. It tackles several other key challenges: it prioritizes loading the most essential memory blocks first, dynamically adjusts memory allocation as needed, and ensures that servers share the load evenly. This intelligent memory management translates into significant performance gains. In experiments using real-world chatbot conversations, Symphony showed a remarkable ability to handle up to 8 times more users than existing systems, all while maintaining snappy response times. Symphony represents a major step forward in LLM memory management. Its innovative approach not only improves performance and scalability but also opens doors for more sophisticated scheduling policies and seamless integration with existing LLM serving frameworks. As LLMs continue to grow in size and complexity, solutions like Symphony will be crucial to unlocking their full potential and delivering truly responsive and scalable AI experiences.