Inferencia, implementación y tiempo de ejecución

Mooncake: KVCache-Centric Infrastructure for Distributed LLM Serving

kvcache-ai/mooncake

Mooncake is a C++ infrastructure project for large-scale LLM inference and training. It separates prefill, decode, and storage resources while providing high-performance transfer, distributed KV-cache storage, and fault-tolerant expert-parallel communication.

★ 6,1KEstrellas
⑂ 1KBifurcaciones
462Problemas abiertos
C++Idioma
Apache-2.0Licencia
Q@project.QualityScorePuntuación editorial

Resumen

Mooncake is the serving platform used for Kimi, Moonshot AI's LLM service. Its disaggregated architecture uses available CPU, DRAM, SSD/NVMe, network, and accelerator resources to create a shared KV-cache pool. The main components are Transfer Engine, Mooncake Store, Mooncake EP, and Mooncake Process Group. The repository reports integration with serving and distributed-computing ecosystems including SGLang, vLLM, TensorRT-LLM, LMCache, LMDeploy, NIXL, and PyTorch.

Características principales

  • Unified batched data movement across heterogeneous storage, networks, and accelerators
  • Topology-aware routing, multi-NIC bandwidth aggregation, and automatic transfer failover
  • Distributed KV-cache and model-weight management through Mooncake Store
  • DRAM and SSD/NVMe cache tiers with object replication, placement, pinning, and eviction controls
  • Disaggregated prefill and decode serving
  • Cross-instance KV-cache sharing and reuse
  • Fault-tolerant expert-parallel dispatch and combine operations
  • PyTorch ProcessGroup integration with failure reporting and rank recovery
  • Integrations described for SGLang, vLLM, TensorRT-LLM, LMCache, LMDeploy, NIXL, and related systems
  • Support described for TCP, RDMA, AWS EFA, NVMe-oF, NVLink, HIP, Barex, CXL, and Ascend-family transports

Requisitos, instalación y guía rápida

Exact installation commands are not stated in the supplied repository excerpt. The README links to project documentation and identifies published Transfer Engine package variants for CUDA, non-CUDA, NPU, MUSA, and AWS EFA environments. Select the package or source-build path that matches the deployment's accelerator and network runtime, then follow the official documentation. Do not assume that every transport or accelerator is enabled in a default build.

Uso

A concise conceptual quick start is: choose a supported serving integration such as SGLang or vLLM; provision the required transport and accelerator runtime; configure Transfer Engine between producer and consumer nodes; optionally deploy Mooncake Store for shared KV-cache storage; then configure the serving engine's Mooncake connector or backend for disaggregated prefill and decode. Exact commands, configuration files, ports, and runnable examples are not stated in the supplied repository excerpt.

Compatibilidad de modelos y casos de uso

Mooncake is infrastructure rather than a model runtime tied to one model family. The supplied data mentions use with Kimi, Kimi-K2, DeepSeek deployments, GLM 5.2 FP8, LLaMA3-70B benchmark sizing, large mixture-of-experts systems, and multimodal pipelines. This does not establish universal compatibility with every model. Exact supported model lists and version constraints are not stated in the repository metadata.

Notas sobre la licencia y los riesgos

The repository metadata identifies the license as Apache-2.0 and provides https://api.github.com/licenses/apache-2.0. Users should review the repository's license file and the licenses of integrated runtimes, drivers, transports, and serving frameworks before deployment.

Lanzamiento y mantenimiento

The supplied update history reports that Transfer Engine was open-sourced on November 28, 2024 and Mooncake Store on March 7, 2025. It also records integrations or adoption involving vLLM, SGLang, TensorRT-LLM, LMCache, LMDeploy, NIXL, vLLM-Ascend, TorchSpec, and other projects. The repository metadata reports 6,132 stars, 1,047 forks, 462 open issues, a creation timestamp of 2024-06-25T05:21:05Z, and that the repository is not archived. No formal latest release version is stated.

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