Private LLM on premise: a buyer's guide for UAE and GCC enterprises

A private LLM is an open-weight language model, for example from the Llama, Qwen, DeepSeek or Gemma families, served on infrastructure your organisation owns or controls. Every prompt, retrieved document, generated answer and usage log is processed inside your perimeter. Nothing is sent to a third-party model provider.

This is different from a dedicated or private tenancy in a public cloud. There, the provider still operates the hardware and the software stack. On premise, your team or your appointed integrator operates both, and you decide what, if anything, connects to the internet. An air-gapped deployment goes one step further: once installed, the system has no internet connection at all and updates arrive through your approved transfer process.

Most of the drivers are practical rather than ideological: control of sensitive data, predictable capacity that is not shared with other tenants, logs that stay with your own security team, and the ability to keep AI services running in restricted or offline environments.

Data rules matter as well. The UAE Personal Data Protection Law, Federal Decree-Law No. 45 of 2021, has been in force since 2 January 2022 and sets requirements for transferring personal data outside the country. Some categories, including government data and personal health and financial data covered by their own legislation, fall outside its scope and follow those sector rules instead.

Healthcare is the clearest example. Article 13 of Federal Law No. 2 of 2019 on the use of ICT in health fields states that health data related to health services provided inside the UAE may not be stored, processed, generated or transferred outside the State, except where a resolution allows it. The law sets fines of AED 500,000 to 700,000 for breaching that article. Financial free zones such as DIFC and ADGM run their own data protection regimes.

This is general information, not legal advice. Running AI on premise is a technical control. It can make compliance easier to demonstrate, but it does not make an organisation compliant on its own. Confirm your obligations with your legal and compliance team.

A production private LLM is more than a GPU server and a model file. Plan for each of these layers before you buy:

GPU memory decides which models fit and how much context and concurrency you can serve. Precision matters too: newer Blackwell-generation GPUs add hardware support for 4-bit floating point (FP4) inference, which lets larger models fit in the same memory.

As a starting point from our solution briefs: a deskside NVIDIA DGX Station with the GB300 superchip serves up to 200B-class models in FP4 and can be shared by fractions across a team. An 8-GPU NVIDIA HGX H200 server, with 1.1 TB of GPU memory, serves 70B-class models at 128k context with headroom for fine-tuning. HGX B200 and B300 servers, with 1.4 TB and 2.3 TB, take frontier open models, full fine-tuning and long-context work. Results always depend on the model, precision, context length and concurrency, which is why we size each system during a consultation.

8-GPU servers need data centre power and cooling. For reference, NVIDIA lists a maximum of 10.2 kW for its DGX H200 system, 14.3 kW for DGX B200 and 15 kW for DGX B300. HGX-based servers from other chassis makers vary, so confirm the exact figure, rack power and cooling capacity before you order.

If you do not yet have a suitable rack, a deskside DGX Station is a practical first step: it runs on standard office power and suits departmental pilots and teams that want production-grade AI without a data hall.

Building in-house means selecting hardware, operating system, drivers, Kubernetes, a GPU scheduler, inference engines, a gateway, identity integration and monitoring, then keeping all of them updated and compatible. That can suit organisations with an experienced platform team. It also tends to mean several vendors and several support contracts when something breaks.

A turnkey system arrives with the platform pre-installed and tested before shipment, is installed on site, connects to your single sign-on and is covered by one support contract for hardware and platform. The tradeoff is less freedom to assemble every component yourself.

Use these questions to compare proposals on equal terms:

Tiada Solutions supplies enterprise NVIDIA systems as an NVIDIA partner in the UAE, with the Clastiq orchestration platform pre-installed: Kubernetes, VMs, fractional GPU sharing, an OpenAI-compatible inference gateway and offline guardrails. Each system is installed on site, connected to your single sign-on, and covered by three years of hardware and platform support with next-business-day parts.

Pricing is quoted per customer after a sizing consultation, so the configuration matches your models, users and facility.