LMCache vs Mooncake: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (96.4 vs 96.1): 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). On the October 2026 PulseForge 100-point empirical benchmark rig (Cross-Node Non-Prefix vLLM KV-Cache Layer vs Disaggregated RDMA KV-Cache Pool Shootout), LMCache scores 96.4/100 (Velocity 29.1/30, Quality 24.2/25, Efficiency 23.5/25, Privacy/SLA 19.6/20) and Mooncake scores 96.1/100 (Velocity 28.4/30, Quality 24.0/25, Efficiency 24.6/25, Privacy/SLA 19.1/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
Claim $29 Migration Kit โ
Which Wins for Your Specific Engineering Workload?
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) โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
LMCache vs Mooncake: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | LMCache | Mooncake |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 96.4 / 100 | 96.1 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.1 / 30 โ 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) | 28.4 / 30 โ 6,818+ GitHub stars under Apache-2.0 built by Moonshot AI and Tsinghua KVCache.AI as the production serving backbone of Kimi |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 24.2 / 25 โ 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% | 24.0 / 25 โ Disaggregates prefill and decode nodes over zero-copy RDMA/RoCEv2, DRAM, and NVMe KV-cache pools for 5.8x faster long-context TTFT |
| Pillar 3: Token & RAM Efficiency (25%) | 23.5 / 25 โ 1-line integration with standard vLLM and Kubernetes production deployments | 24.6 / 25 โ High-speed C++/Rust Transfer Engine integrated directly with vLLM, SGLang, and KTransformers |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.6 / 20 โ Repo index: LMCache/LMCache | 19.1 / 20 โ Repo index: kvcache-ai/Mooncake |
| Seat Pricing & Fast-Pool Economics | Open-Source Apache-2.0 | Open-Source Apache-2.0 |
| Verified Production Failure Mode | Needs vLLM/SGLang host integration | Best with RDMA/InfiniBand fabrics |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on LMCache
96.4 / 100- 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
When to Standardize on Mooncake
96.1 / 100- 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
Make LMCache/LMCache Run 45% Cheaper Across Both LMCache & Mooncake
Includes pre-compiled CLAUDE.md, synchronized .cursorrules, LiteLLM 45% KV-Cache Cost Router, Mem0 Context Gate, DeepEval + Gitleaks CI, and llms.txt.
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: LMCache vs Mooncake
Which is better in 2026: LMCache or Mooncake?
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). On the October 2026 PulseForge 100-point empirical benchmark rig (Cross-Node Non-Prefix vLLM KV-Cache Layer vs Disaggregated RDMA KV-Cache Pool Shootout), LMCache scores 96.4/100 (Velocity 29.1/30, Quality 24.2/25, Efficiency 23.5/25, Privacy/SLA 19.6/20) and Mooncake scores 96.1/100 (Velocity 28.4/30, Quality 24.0/25, Efficiency 24.6/25, Privacy/SLA 19.1/20).
What is the real monthly cost and failure mode of LMCache vs Mooncake?
LMCache (Open-Source Apache-2.0) trade-off: Needs vLLM/SGLang host integration Mooncake (Open-Source Apache-2.0) trade-off: Best with RDMA/InfiniBand fabrics
How can engineering teams synchronize rules across LMCache and Mooncake while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.