Imagine trying to access a massive library with only a narrow doorway—that's the challenge of serving large language models (LLMs) efficiently. These powerful AIs need vast memory resources, especially when dealing with long, complex prompts. The resource bottleneck creates latency and limits how many users can be served. Moonshot AI has developed an ingenious solution they call "Mooncake." It's not a dessert, but a novel serving architecture that tackles the resource bottleneck, making LLMs faster and more accessible. Mooncake cleverly disaggregates the LLM serving process, separating the initial processing ('prefill') from the actual text generation ('decoding'). This separation allows for more effective use of different hardware resources. The key ingredient? A "KVCache" – a specialized memory store holding the intermediate results of computations. Mooncake intelligently distributes and manages access to the KVCache across multiple machines. This optimizes the flow of information, minimizing bottlenecks and speeding up the entire serving process. Importantly, Mooncake tackles the problem of system overload. Instead of blindly processing every request and overwhelming the system, Mooncake includes a "prediction-based early rejection" system. By predicting which requests are unlikely to be completed within reasonable time limits, it saves valuable resources and reduces latency for the remaining requests. The experimental results are impressive. In simulated scenarios, Mooncake boosts throughput by up to a staggering 525% compared to traditional methods. In real-world tests, it enabled Moonshot AI’s “Kimi” LLM service to handle a 75% increase in requests. Mooncake is not just about serving faster AI; it's about making powerful AI more scalable and economical. By solving the resource bottleneck, Mooncake opens doors for a future where everyone can enjoy the benefits of these advanced language models.