Alternatives to Mooncake

Open-source KV cache management layer that persists, offloads and reuses model key-value caches across requests and serving engines to reduce repeated prefill work and improve inference throughput.

llm-d is an open-source Kubernetes-native stack for distributed LLM inference with cache-aware routing and disaggregated serving. It separates prefill and decode stages across different GPU pools for optimal resource utilization, routes requests to nodes with warm KV caches, and integrates with vLLM as the serving engine. Apache-2.0 licensed with 2,900+ GitHub stars.

SGLang is an open-source serving framework for large language and vision-language models, designed for low latency and high throughput. It features RadixAttention for automatic KV cache reuse, compressed finite state machines for fast structured output generation, continuous batching, and tensor parallelism. With over 25,000 GitHub stars, it supports models like LLaMA, Mistral, Qwen, and Gemma on NVIDIA and AMD GPUs.

vLLM is an Apache-2.0 LLM inference and serving engine focused on high-throughput self-hosted model APIs. It combines PagedAttention, continuous batching, prefix caching, quantization options, OpenAI-compatible serving, structured outputs, metrics, Docker/Kubernetes deployment guidance and integrations with agent and LLM frameworks.

Open-source Kubernetes-native building blocks for deploying, routing and scaling GenAI inference, including an LLM gateway, autoscaling, LoRA management and KV-cache offloading.