Best AI NAS for private RAG

What is RAG and why run it on a NAS?

Retrieval augmented generation lets a language model answer questions about your own documents. An embedding model turns each document into a searchable index; when you ask a question, the most relevant passages are found and handed to the chat model with your question. Running it on a NAS keeps contracts, notes and records on hardware you own, next to the files themselves.

What does a RAG setup need from the hardware?

Memory, because two models run together. A small embedding model takes 1GB to 2GB, and the answering model takes the rest. For a household library, 32GB is workable with an 8B class chat model. For a team knowledge base, 64GB or more gives room for a 14B to 32B model and several users (Estimated).

Storage speed matters during indexing. M.2 slots for the index and documents, and 10GbE when files arrive from other machines, make the first build much quicker.

A GPU helps most with the answering model. Embedding runs acceptably on a modern CPU.

Which software builds a private RAG system?

AnythingLLM and Open WebUI both support document collections with Ollama as the model backend. QNAP's Qsirch adds a local AI mode on systems with a capable GPU. UGREEN's iDX range and Zettlab's Ultra models build semantic file search into the operating system, which covers simple document questions without extra setup.

Common questions

Can a NAS index thousands of documents?

Yes. Indexing is a one-off job per document and runs in the background. The limit is memory for the models, not the number of files.

Is RAG on a NAS private?

It is when the models run locally. Check that the chat tool points at Ollama on the NAS rather than a cloud API key.