# 面壁社区在NAS上部署MiniCPM5-1B，实现本地LLM+Agent+RAG系统

- 来源：OpenBMB (@OpenBMB)
- 发布时间：2026-06-26 21:01
- AIHOT 分数：41
- AIHOT 链接：https://aihot.virxact.com/items/cmquydpy308hnsl800u22c2jv
- 原文链接：https://x.com/OpenBMB/status/2070492585639190921

## AI 摘要

面壁智能社区开发者将MiniCPM5-1B部署于QNAP NAS（型号Qu605-N150-16G），内存占用低于2GB，通过Ollama集成至Cherry Studio作为本地LLM。借助NAS MCP协议，将文件管理、共享文件夹、语义搜索等系统能力暴露给外部Agent，实现安全本地数据访问。同时利用Qsirch索引将NAS文件构建为结构化知识库，由MiniCPM5-1B在设备端执行检索增强推理，完成摘要、问答等任务。展示轻量小模型从本地推理向系统级智能体+RAG组合演进的实践。

## 正文

We're excited to see MiniCPM5-1B being used in real NAS-based local AI systems.🥳🥳
A developer in our community built a full-stack setup combining on-device LLM inference with NAS and Agent capabilities：

⚡ Lightweight local deployment
MiniCPM5-1B runs on a QNAP-Qu605-N150-16G NAS， consuming <2GB of memory. It is deployed via Ollama and integrated into Cherry Studio as a local LLM provider.

🧩NAS + Agent integration via MCP
With NAS MCP， system capabilities like file management ， shared folders ， and semantic search are exposed to external agents. This enables Coding Agents / WorkBuddy-style workflows to securely access and retrieve local data within permission boundaries.

📚Local knowledge base+ RAG pipeline
Using Qsirch indexing， NAS files can be turned into a structured local knowledge base.
MiniCPM5-1B handles retrieval-based reasoning， enabling summarization， Q&A， and extended reasoning fully on-device.

This is a great case of how efficient small models are evolving beyond local inference into real system-level intelligence.
From NAS storage → Agent operations → knowledge reasoning
Everything works together in one loop！
📖 Original post： https://mp.weixin.qq.com/s/iBeHfOrwulYsEm2hhhv7vw