Server for enterprise RAG
AI infrastructure for search and work with corporate documents and knowledge bases.
What it is
A RAG contour consists of several components: an embeddings server, a vector database, an optional reranker and a generative model. Such a server is designed around corpus size, query intensity and answer quality requirements — not just the LLM size.
When it fits
- Internal search over a large document corpus
- A knowledge base with natural-language answers
- Assistants working with regulations and contracts
- RAG inside a protected perimeter
What to keep in mind
- RAG quality depends on knowledge structure and chunking as much as on hardware
- Embeddings can sometimes run on CPU — GPU is not always required
- The vector DB must be designed for corpus growth
- A reranker noticeably improves quality but adds resource cost
Frequent questions
Do we need a GPU for embeddings?+
Not always. For a modest corpus and moderate query rate, embeddings can run on CPU. A GPU becomes justified for mass indexing and high query rates.
How large a corpus can one server handle?+
Vector indexes for millions of chunks fit on a single server with the right architecture. The limit is usually query rate, not corpus size.
Can RAG work without an LLM?+
Technically yes, as semantic search. But most of the value appears when paired with a generative model that produces answers from the retrieved fragments.
Let’s discuss your task
Tell us what the AI contour needs to solve — we will match the configuration, infrastructure and next step.