Large Language Models (LLM) have increased in complexity and demand, creating significant challenges for companies seeking to provide scalable and cost-effective Models as a Service (MaaS). The rapid adoption of LLMs in various applications has led to highly variable workloads in terms of input/output duration, arrival frequencies, and service requirements. Balancing resource utilization to meet these diverse needs has become a critical challenge. Achieving this balance requires sophisticated strategies to meet different service level objectives (SLOs) for latency and throughput. Additionally, conventional LLM service architectures often assume that there are sufficient resources available to handle all requests, which becomes increasingly difficult with increasing demand, especially during peak usage hours.
The main challenge is to maximize performance without compromising latency, especially as operating costs increase and GPU resources remain limited. To address these issues, Moonshot ai developed a new architecture.
<h3 class="wp-block-heading" id="h-moonshot-ai-open-sources-its-core-reasoning-architecture-mooncake”>Moonshot ai opens up its core reasoning architecture: Mooncake
China-based ai company ai shot to the moon has officially opened its core reasoning architecture, called moon cake. Mooncake aims to address key scalability and efficiency challenges in LLM service. Moonshot ai employs a disaggregated architecture centered on KVCache, which distinguishes Mooncake from traditional LLM service platforms. Mooncake's first open source component, called transfer motorNow available on GitHub, with more components planned for future releases <a target="_blank" href="https://github.com/kvcache-ai/Mooncake?tab=readme-ov-file”>GitHub link.
At the core of Mooncake is its KVCache-centric approach to handling computational workloads. By separating the prefetch and decode clusters, Mooncake can dynamically optimize resources, making use of underutilized CPU, DRAM, and SSD resources for efficient caching. This separation is crucial to address the various computational characteristics of LLM service stages. The decision to open Mooncake reflects a commitment to transparency and community-driven improvements in LLM scalability.
Technical details
Mooncake takes advantage of a KVCache-centric pre-decode (PD) separation technique and a disaggregated storage computing architecturewhich have significantly improved the inference performance of Moonshot ai's LLM service, Kimi. The KVCache mechanism is essential to optimize both throughput and latency. Instead of keeping GPU resources involved in all aspects of model serving, Mooncake isolates KVCache usage from computational tasks, allowing it to be managed by underutilized hardware like CPUs and SSDs.
Mooncake's architecture divides the LLM service into two stages:Preloading and decoding. During the prefetch stage, scratch cache is transferred to prefetch instances, optimizing the first generation of tokens while reducing redundant computations. Then, during the decoding stage, KVCache is added, allowing for efficient batch processing. This separation has led to substantial improvements in performance.
When implementing a early rejection policy based on predictionsMooncake also helps prevent system overload during peak demand periods. This approach has been instrumental in maintaining service level objectives (SLO) for time to first token (TTFT) and time between tokens (TBT), even under high workloads. Experimental results have shown that compared to the baseline, Mooncake achieved up to five-fold increase in performance in simulated and enabled scenarios 75% more request handling under real-world workloads.
The importance of Mooncake's open source release is multi-layered. It represents an advance in the decentralizing LLM inference workloadsensuring that no hardware component becomes a bottleneck. KVCache's centric programming model balances resource loads effectively, allowing service providers to maximize performance without violating latency requirements. This efficiency is essential given the growing demand for LLM skills across industries.
The experimental results demonstrate that Mooncake achieved a five-fold increase in performance in some simulated long context scenarios while maintaining the required SLOs. In real world settings, Mooncake allowed Kimi to drive 75% more requests compared to previous architectures. These improvements highlight Mooncake's ability to scale efficiently and reduce costs. The disaggregation approach also provides greater flexibility to add computational resources on the fly, addressing variability in LLM workloads more efficiently than traditional coupled systems.
The gradual implementation of open source also encourages collaborative development. Starting with the Transfer Engine, Moonshot ai aims to gather feedback from the community before releasing additional components. This phased approach aims to lead to further optimizations and broader adoption across various sectors in need of efficient LLM service delivery solutions.
Conclusion
Moonshot ai's decision to open source Mooncake reflects a broader industry trend toward transparent and scalable ai development practices. By focusing on KVCache-centric separation, Mooncake addresses the key challenges of LLM service: latency, efficiency, and scalability. It has already shown significant performance improvements, making it a promising framework for LLM service. Mooncake's architecture balances computational and caching demands effectively, improving resource utilization, reducing latency, and improving overall performance. The phased open source approach underscores Moonshot ai's commitment to continuous improvement and community collaboration.
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