RAG infrastructure explained
Retrieval-augmented generation (RAG) lets a model answer using your documents instead of guessing. Here are the parts that make it work, and what it takes to run privately.
What RAG actually does
Instead of relying only on what a model learned during training, RAG retrieves relevant passages from your own documents and gives them to the model as context. The result is answers grounded in your knowledge base, with far less hallucination.
The building blocks
- An embedding model that turns text into vectors.
- A vector database that stores and searches those vectors.
- A retrieval step that finds the most relevant passages for a question.
- A language model that writes the final answer from those passages.
Why run it on private hardware
RAG is most valuable on your most sensitive content: contracts, support history, internal wikis. On a dedicated private server, the documents, embeddings and index never leave your infrastructure.
You run the pipeline; we provide and host the GPU hardware sized for your embedding and generation models, with IPMI access and full control.
Sizing the hardware
VRAM and GPU count depend on the embedding and generation models you choose and how much you index. We help size this during workload discovery so the server fits both today and your growth.
In short: RAG = embeddings + vector search + an LLM; run it privately so your documents stay in-house. Request a custom quote