It is astonishing that the "Moonshot AI" team has actually published a paper of great value. This paper provides a detailed introduction to their large language model (LLM) inference service architecture. The innovative architecture named "Mooncake" plays a vital role in the entire service system, enabling the service named "Kimi" to handle more requests while also ensuring service quality.
The core idea of "Mooncake" is uniquely innovative, as it separates the prefill and decoding stages of the LLM inference process. This separation is not arbitrary but well-considered. By separating these two stages, it allows for a clearer division of tasks and functions, making the entire inference process more efficient and controllable. Moreover, "Mooncake" is optimized around KVCache (key-value caching). The key-value cache plays a pivotal role in the entire architecture, as it can store and quickly retrieve key information, significantly improving the speed and accuracy of inference.
To achieve efficient inference services, "Mooncake" fully utilizes the CPU, memory, and SSD resources in the GPU cluster. Through careful design and coordination, it has successfully built a distributed KVCache system. This system can maximize the advantages of different resources, improving resource utilization and inference efficiency. For example, the CPU can be responsible for coordinating and managing tasks, memory can quickly store and read data, and SSD can provide large-capacity storage. Through this distributed resource utilization method, "Mooncake" can maintain high performance when handling a large number of requests.
In addition, "Mooncake" also adopts a series of innovative strategies to deal with challenges such as long context and system overload. In dealing with long context issues, it can effectively manage and process a large amount of text information, ensuring the accuracy and coherence of inference. In the face of system overload situations, it can ensure system stability and availability through intelligent load balancing and resource allocation strategies. The application of these innovative strategies has significantly improved the performance and throughput of LLM services, making "Moonshot AI's" services stand out in fierce market competition.