# 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://pulse-forge.io/vs/lmcache-vs-mooncake
- **1-Click $0 Free Starter valkey-cluster.prod.conf + CLAUDE.md**: https://pulse-forge.io/api/launch-kit/free-claude-md?repo=lmcache/lmcache
- **1-Click $4.99 Drop-In Migration Config Pack (3 Core Files - Stripe Checkout)**: https://pulse-forge.io/buy/micro_pass?repo=lmcache/lmcache&source=md_vs
- **1-Click $9.99 Complete 9-File Production Config .ZIP (Stripe Checkout)**: https://pulse-forge.io/buy/repo_pass_999?repo=lmcache/lmcache&source=md_vs_999
- **1-Click $49 Builder Repo Hardening & Launch Kit (Stripe Checkout)**: https://pulse-forge.io/buy/instant_boost?repo=lmcache/lmcache
- **1-Click $149 Category Leader Pro + 30-Day Pin (Stripe Checkout)**: https://pulse-forge.io/buy/category_leader?repo=lmcache/lmcache
- **Specimen #1 Production Config Preview (`valkey-cluster.prod.conf`)**: `+ [valkey-cluster.prod.conf] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)`

## 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