10ⁿ Tech designs, supplies, builds and commissions turnkey on-premises GPU clusters – your own AI supercomputer, in your own data center or colocation. Run AI training, fine-tuning and high-performance computing on hardware you own, with full data sovereignty and no recurring cloud-GPU bills. One accountable partner from the power and cooling design through to a validated, documented, day-2-ready cluster.
Running the AI software layer too? Pair this with our self-hosted AI platform – the cluster is the foundation; the platform runs your models on top.
Why build AI infrastructure on-premises
Your data and models never leave your building; production runs in-country, on hardware you control.
For sustained training and inference, owning the GPUs beats months of metered cloud-GPU rental – and there are no egress charges.
Local InfiniBand or 100-400Gb Ethernet and NVMe storage keep the GPUs fed, with no network bottleneck to a remote cloud.
Your hardware, your scheduling, your security perimeter and compliance posture – no shared tenancy.
What we build – the cluster stack
A high-density option: the Gooxi 8x RTX 5090 server
For maximum GPU memory and throughput per dollar, we supply and integrate the Gooxi 8x NVIDIA RTX 5090 server – eight RTX 5090 cards (256GB total GDDR7, PCIe 5.0) and dual AMD EPYC processors in a single 6U chassis. For inference, fine-tuning and many training workloads it delivers a large share of data-center-GPU performance at a fraction of the per-GPU cost of H100/H200.
The catch is integration – eight open-air GPUs in one server is an engineering problem, not plug-and-play. We handle it: redundant power sized for GPU spikes and 12V-2×6 cabling, chassis airflow and anti-recirculation baffling, and burn-in validation before handover. Gooxi is one of several GPU-server vendors we partner with; we match the platform to your workload and budget.
NVIDIA or AMD
We are vendor-neutral. NVIDIA H100/H200 give the broadest CUDA ecosystem and NVSwitch for tightly-coupled training; AMD Instinct MI300 (ROCm) competes hard on price-performance and memory capacity; and RTX-class builds such as the Gooxi 5090 server deliver the best cost per GPU for inference and many fine-tuning jobs. We pick the accelerator for your workload, not for a vendor.
Workloads it is built for
- LLM training and fine-tuning – tensor and pipeline parallelism on NVSwitch, or distributed across nodes over InfiniBand.
- Scientific and HPC computing – simulation, molecular dynamics, bioinformatics and research pipelines.
- High-throughput inference – serving your own models to the whole organisation at scale.
- Data preprocessing – high CPU, large memory and fast NVMe I/O to keep the GPUs busy.
How we deliver it
- Assessment – workloads, GPU sizing, and data-center or colocation constraints.
- Architecture and power/cooling design – rack layout, power per rack, airflow and the network fabric.
- Vendor-neutral procurement – GPU servers, storage and networking through our vendor relationships.
- Build and commission – in your data center or colocation, with verification and burn-in.
- Handover – as-built documentation, with optional day-2 managed support.
Why 10ⁿ Tech
We bring turnkey datacenter delivery across the GCC, deep vendor relationships (NVIDIA, AMD, Gooxi, xFusion, Kerno, ASUS, Supermicro, Dell, HPE), and the as-built rigor of our on-prem IT infrastructure practice. We solve the hard parts – high-density power and cooling, high-speed fabric, GPU integration and validation – so the cluster works on day two, not just on paper.
Frequently asked questions
What is self-hosted on-prem AI?
Running your AI and HPC workloads on a GPU cluster you own, in your own data center or colocation, instead of renting cloud GPUs. You get data sovereignty, predictable economics and full control of the hardware.
Is an on-prem GPU cluster cheaper than cloud GPUs?
For sustained, heavy use – continuous training or high-volume inference – owning the cluster typically beats months of metered cloud-GPU rental and avoids egress charges. For short, bursty experiments, cloud can be cheaper. We model both for your workload.
Which GPUs do you use?
NVIDIA H100 and H200 (PCIe and SXM/NVSwitch), AMD Instinct MI300, and cost-optimised RTX-class builds such as the Gooxi 8x RTX 5090 server – chosen for your workload and budget.
What is the Gooxi 8x RTX 5090 server?
A 6U server with eight NVIDIA RTX 5090 GPUs (256GB total GDDR7) and dual AMD EPYC processors. It offers very high GPU density at a low cost per GPU; we supply it and handle the integration – power, cooling and validation – that dense open-air GPUs require.
What is in a GPU cluster besides the GPUs?
CPU compute nodes, parallel NVMe storage, a high-speed InfiniBand or 100-400Gb Ethernet fabric, virtualization and scheduling, firewalls and switching, and the power and cooling design for high-density racks.
Can you build it in a colocation data center?
Yes. Most of our builds are in colocation facilities – we handle rack, power, cooling, networking, build and commissioning, and leave you a documented, running cluster.
Do you also run the AI software on it?
Yes. Our self-hosted AI platform – model serving, RAG, knowledge graph and explainable AI – runs on top of the cluster, so you get hardware and software from one partner.
Build your own AI supercomputer
If your AI roadmap needs serious, sovereign compute, we will design and deliver the cluster end to end. Talk to us or explore the full solutions portfolio.
