How do we get started with an AI factory?

Tell us your use case, the models or model sizes you plan to run, how many users or jobs you need to support, and where the hardware will be installed. Our pre-sales team will suggest a starting configuration and quote it in AED.

What is NVIDIA DGX Spark used for?

DGX Spark is a desktop AI system built on the NVIDIA GB10 Grace Blackwell Superchip with 128 GB of unified memory. Developers and researchers use it to prototype, fine-tune and run models locally before moving work to larger systems.

Should we choose a DGX system or a GPU server from another brand?

DGX systems are integrated NVIDIA platforms with a consistent hardware and software stack. GPU servers from brands such as Dell Technologies, HPE, Lenovo and Supermicro offer more configuration choice and may fit your existing support arrangements. We can compare both for your workload.

Why are high-performance GPUs important?

Training and running modern AI models involves huge numbers of parallel calculations. GPUs handle this kind of work far better than general-purpose processors, and their memory determines how large a model you can run.

What are NVLink and NVLink Switch used for?

They connect GPUs directly to each other at high speed, so several GPUs can share work on one large model without being held back by slower connections.

Do we need InfiniBand for AI?

Not always. InfiniBand is common for large-scale distributed training, while high-performance Ethernet suits many inference and mid-sized training clusters. A single GPU server needs neither for communication between its own GPUs.

Can we test the hardware before we buy?

Yes. You can request a proof of concept through our Experience Centre to evaluate a proposed configuration with your own models and data.

AI Factory

Data Centre Solutions: AI Factories

AI infrastructure for the UAE, from a single desktop AI system to multi-node GPU clusters, sized, configured and delivered by CoreFlux.

AI Infrastructure in the UAE

From GPU Hardware to a Working AI Factory

An AI factory is infrastructure built to turn data into AI output: training and fine-tuning models, then serving them to people and applications. It brings together GPU compute, a fast network between GPUs and servers, storage that can feed data quickly enough, and a software layer that schedules jobs and keeps them running. CoreFlux supplies the hardware for each of these layers and helps you design them to work together.

We supply NVIDIA DGX systems, including the desktop NVIDIA DGX Spark, GPU servers from several leading brands, high-speed networking and high-performance storage. Our pre-sales engineers help enterprises, government entities, universities and system integrators size the platform, and CoreFlux Labs configures and burn-in tests the hardware in Dubai before it is delivered.

What We Supply

AI Hardware for Every Stage

NVIDIA DGX Spark

A compact desktop AI system built on the NVIDIA GB10 Grace Blackwell Superchip with 128 GB of unified memory, for prototyping and fine-tuning models locally.

DGX Systems and GPU Servers

We supply NVIDIA DGX systems and GPU servers from Dell Technologies, HPE, Lenovo, Supermicro, GIGABYTE and ASUS for training and inference.

AI Networking

High-speed networking between GPUs and servers, with InfiniBand or high-performance Ethernet chosen to suit the size of the cluster.

AI Storage

Fast storage and NVMe drives that keep GPUs supplied with data, plus capacity for datasets, checkpoints and archives.

AI Factories

Plan, Configure and Deliver AI Infrastructure Locally

CoreFlux supplies NVIDIA AI systems and multi-brand GPU infrastructure from Dubai. We help you choose a starting point that fits your models today and the design you expect to grow into, then configure, test and deliver it across the UAE and the GCC.

Get in Touch

Know How

Why Build an AI Factory?

Start Small, Grow in Steps

Begin with a desktop AI system or one GPU server and add nodes as demand grows.

Data Stays Under Your Control

Run models on hardware you own, on-premises or in a UAE data centre.

Hardware Matched to the Workload

Size GPU memory, networking and storage for the models you actually run.

Faster Experimentation

Dedicated GPUs give your teams room to test and iterate without queuing for shared resources.

What role does the software layer play?

Hardware is only part of an AI factory. NVIDIA's AI software stack and related tools handle drivers, frameworks, job scheduling, GPU sharing and monitoring. We supply the hardware it runs on and can talk through how the software layer fits your plans.

Real-World Impact

Industry Use Cases for AI Factories

Most industries now have work that benefits from dedicated AI infrastructure. A few common examples are below.

Healthcare & Life Sciences

Train and run models for medical imaging, research data analysis and drug discovery on infrastructure that keeps sensitive data in-house.

Finance, Energy & Retail

Run private language models, forecasting and document processing close to your own data and applications.

Manufacturing & Robotics

Develop computer vision and robotics models for inspection, automation and safety, then deploy them at the edge.

Government & Education

Give research teams, students and public-sector programmes access to GPU compute for training and evaluating models.

Deployment

Where to Deploy AI Factories

Where the hardware lives depends on your data, your facilities and how quickly you need results. These are the three most common options.

On-Premises

Full control over data and performance. Check that the room can handle the power and heat of dense GPU systems.

Colocation

If your own facility lacks the power or cooling, a UAE data centre can host the hardware while you keep ownership of it.

Edge

Compact GPU systems run inference close to where data is created, such as factories, retail sites and transport hubs.

Know More

Frequently Asked Questions

Answers to the questions we hear most often when customers plan GPU and AI infrastructure.

How do we get started with an AI factory?: Tell us your use case, the models or model sizes you plan to run, how many users or jobs you need to support, and where the hardware will be installed. Our pre-sales team will suggest a starting configuration and quote it in AED.
What is NVIDIA DGX Spark used for?: DGX Spark is a desktop AI system built on the NVIDIA GB10 Grace Blackwell Superchip with 128 GB of unified memory. Developers and researchers use it to prototype, fine-tune and run models locally before moving work to larger systems.
Should we choose a DGX system or a GPU server from another brand?: DGX systems are integrated NVIDIA platforms with a consistent hardware and software stack. GPU servers from brands such as Dell Technologies, HPE, Lenovo and Supermicro offer more configuration choice and may fit your existing support arrangements. We can compare both for your workload.
Why are high-performance GPUs important?: Training and running modern AI models involves huge numbers of parallel calculations. GPUs handle this kind of work far better than general-purpose processors, and their memory determines how large a model you can run.
What are NVLink and NVLink Switch used for?: They connect GPUs directly to each other at high speed, so several GPUs can share work on one large model without being held back by slower connections.
Do we need InfiniBand for AI?: Not always. InfiniBand is common for large-scale distributed training, while high-performance Ethernet suits many inference and mid-sized training clusters. A single GPU server needs neither for communication between its own GPUs.
Can we test the hardware before we buy?: Yes. You can request a proof of concept through our Experience Centre to evaluate a proposed configuration with your own models and data.