GPU capacity in the UAE, without the queue
NVIDIA-class GPU clusters for training, fine-tuning and inference, hosted in-country. Rent by the hour, reserve a cluster for months, or let our engineers run the whole stack for you.
- Right for you if
- Your model work keeps waiting on hyperscaler quotas, or your data cannot leave the country.
- What you get
- Dedicated GPUs with fast interconnect and storage, sized for your workload.
- Timeline
- On-demand nodes in hours. Reserved clusters typically within 1–3 weeks.
- Pricing
- Per GPU-hour on demand, or a monthly rate for reserved terms. Quoted after a short sizing call.
Why GPU projects stall
The model is rarely the bottleneck. Access to compute is. Global cloud quotas are tight, the biggest GPUs are allocated to the biggest customers first, and a training run that should take a week waits a month for capacity.
Then come the quieter problems: data that is not allowed to leave the UAE, egress bills nobody budgeted for, and a cluster that sits idle because the team spent three weeks fighting drivers and schedulers.
We fix both sides. You get capacity close to your data, and engineers who have done the setup many times before.
Signs this is you
- A training or fine-tuning run is waiting on cloud quota approval.
- Your data protection obligations make offshore GPUs awkward to justify.
- Inference costs grew faster than usage and nobody can explain why.
- Your data scientists spend more time on CUDA versions than on models.
- You need a lot of compute for three months, not a three-year commitment.
Three ways to buy compute.
By the hour
Billed per GPU-hour. No commitment.
- Single GPUs to 8-GPU nodes
- Pre-built ML images
- Shared high-speed storage
- Spin up in hours
1–36 months
Monthly rate. Guaranteed capacity.
- Dedicated multi-node clusters
- InfiniBand-class interconnect
- Private networking and storage
- Priority engineering support
We run it
Reserved capacity plus our MLOps team.
- Kubernetes or Slurm, operated for you
- Monitoring, patching, on-call
- Cost and utilisation reviews
- Help with training and serving
The GPUs, and what each is good at.
| GPU class | Memory per GPU | Best for | Plans |
|---|---|---|---|
| NVIDIA B200-class | Up to 192 GB | Large-scale training, frontier-size fine-tunes | Reserved |
| NVIDIA H200-class | 141 GB | Training and long-context inference on 70B+ models | Reserved, on demand |
| NVIDIA H100-class | 80 GB | Fine-tuning, training mid-size models, high-throughput inference | Reserved, on demand |
| NVIDIA L40S-class | 48 GB | Inference, image and video generation, rendering | On demand |
Availability changes month to month. We confirm current stock and lead times on the sizing call.
Data stays in the UAE
Hosted in UAE data centres. Useful when PDPL, DIFC or sector rules make offshore compute hard to justify.
Private by default
Dedicated nodes, private networking, encryption at rest, and documented wipe at end of term.
Your stack
PyTorch, JAX, vLLM, TensorRT-LLM, Triton, Ray, Kubernetes or Slurm.
Engineers, not tickets
Talk to the people who set up your cluster when something needs tuning.
What happens, week by week.
- Day 1
Size the workload
We look at model size, dataset, target throughput and deadline, and recommend a GPU type and node count. Often it is smaller than you expect.
You get: A written sizing note with options and indicative cost.
- Days 2–5
Provision and harden
Nodes are provisioned with your choice of image: bare metal, Kubernetes, or Slurm. Access is locked to your identity provider and network.
You get: Working cluster, access set up, quick benchmark run.
- Week 1–2
Onboard the first job
We sit with your team for the first real job: data loading, checkpointing, monitoring, cost tagging.
You get: First job running end to end, with a dashboard.
- Ongoing
Run, tune, right-size
Monthly utilisation review. We move work between on-demand and reserved so you are not paying for idle silicon.
You get: Monthly report: utilisation, spend, recommendations.
What you walk away with.
Dedicated GPU nodes
8-GPU servers with NVLink, high-speed InfiniBand or RoCE fabric for multi-node jobs.
Storage that keeps up
Parallel file storage and object storage sized so GPUs are not left waiting on data.
Your stack, ready
PyTorch, JAX, vLLM, Triton, Kubernetes or Slurm. Pre-built images or bring your own.
Observability
GPU utilisation, job health and cost per project, visible to your team.
Included
- Workload sizing before you commit
- UAE-hosted GPU capacity with in-country data residency
- Private networking, VPN or direct connect options
- Pre-configured ML images and drivers
- Monitoring, alerting and cost dashboards
- Engineer onboarding for your first workloads
- Support from people who run training jobs themselves
Not included
- Crypto mining or non-ML workloads
- Building your models (see Generative AI Modeling)
- Guaranteed capacity on on-demand plans (reserve for that)
- Hardware resale or colocation of your own servers
How you'll know it worked.
- GPU utilisation across the term
- Time from request to first job running
- Cost per training run or per million tokens served
- Idle hours flagged and recovered
Where this fits best.
From our insights.
How many GPUs do you actually need? A sizing guide for fine-tuning and inference
Most teams over-buy for training and under-plan for inference. Some simple arithmetic gets you close before you talk to anyone.
RAG or fine-tuning? How to choose for your business data
They solve different problems. Picking the wrong one is the most expensive mistake we see in generative AI projects.
Questions we hear a lot
Which GPUs do you offer?
NVIDIA data-centre GPUs in the H100, H200 and Blackwell (B200) class, plus L40S-class cards for inference and graphics work. Availability changes, so we confirm current stock on the sizing call.
Where is the hardware located?
In UAE data centres, so data can stay in the country. If you need capacity elsewhere in the GCC, we can arrange it.
Do you offer hourly pricing?
Yes. On-demand nodes are billed by the GPU-hour. For runs longer than a few weeks, a reserved term is almost always cheaper and guarantees the capacity.
Can we bring our own containers and tools?
Yes. You get root on bare metal, or a namespace on managed Kubernetes. We support the common stacks out of the box.
Is our data used for anything else?
No. Your nodes and storage are dedicated to you, encrypted at rest, and wiped to a documented standard when the term ends.
What if we do not know how much compute we need?
That is normal. Start on demand for a week, measure, then reserve what the numbers say. We will help you read them.
Start with a conversation, not a proposal.
Thirty minutes with someone who has built this before. If we're not the right fit, we'll say so and point you to who is.