Once your organisation has decided the language model should run inside it rather than at an outside provider, the question moves from “why?” to “what does it take?”. We covered the reasons in what is an on-premises LLM. This guide is about the practical side: what you need to run an Arabic language model inside the organisation, whether you call it a private LLM, an internal LLM or a self-hosted model, and who runs it after launch.

Not a weights file: six parts

  1. The model itself: one that understands Arabic the way your staff and customers write it, formal and dialect, with a fixed version that does not change until you decide to update it.
  2. Retrieval from your documents: the model alone does not know your policies and regulations. It needs an index of your documents to answer from and cite, and Arabic OCR for scanned files.
  3. Hardware and serving: sizing follows the number of concurrent users and the volume of documents. The serving stack matters as much as the model, because it decides how many users the same hardware serves at once.
  4. Systems and identity: sign-in with the organisation’s identity through SAML or OIDC, connections to ERP, CRM and databases, and approvals for sensitive actions that follow your delegation of authority.
  5. Safety and logs: a safety classifier and policy rules your regulator can read, with a log of every decision from the user’s question to the source. More in five layers, with a security control at each one.
  6. Evaluation and operation: a test set from your data, run before launch and after every update, and a team that monitors and fixes the system. More in evaluated before launch.

Why “Arabic” specifically

A model that is English at its core, with Arabic as a second language, may write acceptable formal Arabic and still stumble on what staff and customers actually write: dialects, Arabic mixed with English, internal jargon. A tokenizer not designed for Arabic splits Arabic words into more pieces, so the same text uses more capacity. Ask how the model was trained on Arabic, then test it on your data, as in how to evaluate an Arabic language model.

How big to start

Do not start with an organisation-wide platform. Pick one use case in one department, with known users and documents, so the hardware is sized from real numbers. Before you commit, ask for a three-year total cost model covering hardware, power, licences and operation, as in where your Arabic AI runs. The comparison with pay-per-use is in on-premises or cloud.

Who runs it

A model inside the organisation needs someone to monitor it, update it and fix what breaks. You do not need a full AI team from day one: a partner can deploy and operate it with you while your team learns the system, and the data and logs stay yours throughout.

How we do it at NoorAI

Orchestra is a 14-billion-parameter Arabic language model, trained from scratch at NoorAI with an Arabic-designed tokenizer, covering Modern Standard Arabic, Gulf, Egyptian and Levantine. It runs on one GPU-class node rather than a cluster, and the model and its serving stack are optimised together to serve more users on the same hardware. It comes with citation-grounded retrieval and Arabic OCR, a low-code agent platform, and a safety layer that logs every decision.

Our engineers deploy it in your data centre or an in-country private cloud, in three phases: Discover, Deploy, Operate, with pass criteria agreed up front and a parallel run before cut-over. We work with banks, government, telecom operators and healthcare.

Frequently asked questions

Do we need to retrain the model on our data?

Not necessarily. In many use cases it is enough for the model to retrieve the answer from your documents and cite the source, so the documents can be updated without retraining. We establish in the Discover phase whether your case needs more.

Can it run in a private cloud instead of our data centre?

Yes, in a private cloud or a dedicated VPC with a provider in your country, with the same security controls.

How is this different from an open model we download and run ourselves?

You can, but the model is one part of six. Open models of similar quality need serious on-premises hardware, while Orchestra runs on one node and comes with retrieval, safety, orchestration and engineers who run it with you.

Do we get the model weights?

Orchestra is not sold as a weights file. NoorAI’s engineers deploy it in your environment with the retrieval, safety and orchestration layers, everything stays on your hardware, and the data, indexes and logs are yours.

Related pages