// AI / AI INFRASTRUCTURE
Production AI compute your team can actually deploy.
Clients want to train and run models on their own infrastructure. What follows demands deep expertise: sizing DGX clusters, choosing between InfiniBand and Spectrum-X, configuring Run.ai for GPU pooling. We design, deploy and hand over the full NVIDIA stack under your brand, so the project lands and the client's confidence holds.
Start an infrastructure conversationIdle GPUs burn budget your clients cannot afford to waste.
Around 44 percent of enterprises still assign workloads to GPUs by hand, leaving utilisation below 50 percent and money spent on silicon that sits idle. Behind every we want to run AI on our own infrastructure request is a project that needs real expertise: cluster sizing, the InfiniBand versus Spectrum-X decision, NVIDIA AI Enterprise integration, and GPU pooling across data science teams. A partner who cannot deliver this competently does not just lose the project; they lose the client's confidence in every AI initiative that follows.
What we build into the stack.
DGX & HGX System Design
NVIDIA DGX B200, DGX GB200 and DGX SuperPOD configurations sized to the client's real compute needs, not a vendor's catalogue. We architect, size and deploy the cluster, with HGX baseboard builds where existing OEM relationships call for it.
GPU Orchestration
Run.ai pools GPU resources across research and engineering teams so they stop sitting idle behind per-team reservations. Intelligent scheduling targets the roughly 40 percent cost reduction available when utilisation is managed properly.
NVIDIA AI Enterprise Stack
The full software suite configured and ready: NeMo for model development, Triton for inference serving, RAPIDS for data science. The cluster arrives as a working platform, not a rack of GPUs waiting on someone to wire up the software.
High-Performance Fabric
Quantum-X InfiniBand for training-intensive HPC, or Spectrum-X Ethernet for AI factories needing hyperscale performance on standard Ethernet, which delivers around 1.6 times the bandwidth density of traditional Ethernet. The network is chosen for the workload, not by default.
AI-Ready Storage
NetApp AI Data Engine and Pure Storage validated to keep Blackwell GPUs fed at full utilisation, so storage never becomes the bottleneck that starves an expensive cluster.
Edge AI Extension
The same architecture extended to NVIDIA Jetson deployments (Orin, Thor) on factory floors, retail sites and hospitals, with centralised model management through NVIDIA Mission Control. Inference happens where the data is generated.
The numbers behind a well-built cluster.
How the options compare.
What your client gets from a traditional reseller, a major integrator, or our team under your brand.
AI infrastructure, answered straight.
01 What is the difference between DGX and HGX?
DGX is NVIDIA's turnkey, pre-validated AI server platform, built and supported end to end by NVIDIA. HGX is the reference GPU baseboard design that OEMs such as Dell, HPE and Lenovo integrate into their own servers. DGX gives the fastest path to deployment with NVIDIA's own support; HGX gives flexibility to match an existing vendor relationship. We deploy both, and recommend based on the client's situation rather than a fixed preference.
02 How do we size a cluster when the client does not know their workload yet?
Every engagement opens with a sizing and readiness assessment. We evaluate planned workloads, data volumes, team size and budget, then deliver a validated architecture recommendation under your letterhead. It is a low-commitment entry point that builds the project pipeline without your team needing GPU expertise upfront.
03 InfiniBand or Spectrum-X, which should we recommend?
InfiniBand (Quantum-X) is the established choice for training-intensive HPC that needs the lowest possible inter-GPU latency. Spectrum-X Ethernet is the newer option for AI factories wanting hyperscale-style performance with standard Ethernet operational simplicity. We weigh the client's workload mix, existing network and operational team skills, then make the call on evidence rather than habit.
04 How does Run.ai cut GPU cost by 40 percent?
Run.ai pools GPU resources across teams with dynamic allocation. Instead of each team reserving dedicated GPUs that sit idle 50 to 85 percent of the time, it schedules workloads across a shared pool by priority and availability. It is the same principle as CPU virtualisation, applied to GPUs, and it is where most of the waste in a cluster hides.
05 What does the client run after handover?
We hand over a documented, production-ready platform with runbooks, validated configuration and the operational knowledge to run it. NVIDIA AI Enterprise includes NVIDIA's own 24/7 support tier for the software stack, so the vendor backstop is there from day one. If the client wants the cluster managed on an ongoing basis, that is your managed service under your brand, and we can backstop deeper engineering on a project basis when a piece of work calls for it.
06 Can you deploy Edge AI alongside the data centre cluster?
Yes. The Edge AI service extends the same architecture to NVIDIA Jetson deployments (Orin, Thor) at factory floors, retail sites and hospitals, with inference at the point of data generation and centralised model management through NVIDIA Mission Control. The edge and the core are deployed as one estate, managed from a single console.
Tell us what your clients need.
A tri-cloud migration. A 200-site SD-WAN rollout. A security architecture before NIS2 hits. An AI system your client is asking about next quarter. We scope it, staff it, and deliver it under your brand. One conversation tells us if we are the right team.