# LMCache vs Mooncake: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: LMCache wins as a plug-and-play vLLM KV-cache compression and non-prefix RAG chunk reuse layer that cuts multi-turn TTFT by 6.5x across GPU, CPU DRAM, and Redis with 12.1k+ GitHub stars (96.4), while Mooncake wins as a full RDMA/NVMe disaggregated prefill-and-decode transfer engine for large GPU clusters (powering Moonshot Kimi) across 6.8k+ GitHub stars (96.1).

- **Category**: Cross-Node Non-Prefix vLLM KV-Cache Layer vs Disaggregated RDMA KV-Cache Pool Shootout
- **Canonical URL**: https://exactbaseline.com/vs/lmcache-vs-mooncake
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## LMCache Key Advantages
- 12,099+ GitHub stars under Apache-2.0 reusing KV caches for ANY repeated text chunk (even non-prefix RAG passages in the middle of prompts)
- Compresses and tiers KV caches across GPU VRAM, CPU RAM, Redis, and local NVMe to cut TTFT by 6.5x and GPU prefill load by 78%
- 1-line integration with standard vLLM and Kubernetes production deployments

## Mooncake Key Advantages
- 6,818+ GitHub stars under Apache-2.0 built by Moonshot AI and Tsinghua KVCache.AI as the production serving backbone of Kimi
- Disaggregates prefill and decode nodes over zero-copy RDMA/RoCEv2, DRAM, and NVMe KV-cache pools for 5.8x faster long-context TTFT
- High-speed C++/Rust Transfer Engine integrated directly with vLLM, SGLang, and KTransformers