AI Infrastructure
Compute and serving capacity for AI models, sized for demand and run inside your control.
Why It Matters
AI projects stall when the hardware arrives before the plan, and teams buy accelerators without knowing which models they will serve.
Then the cluster sits idle, runs out of memory at peak or breaks when a model update changes how it serves. Power and cooling limits in the building can end the plan after the equipment is ordered.
What We Do
Private GPU Clusters
Accelerators on your premises or in a private cloud, sized for the models you serve and grown only when demand does.
Hybrid AI Inference
Each request runs where it is cheapest and safest, inside your estate or in the cloud, under rules you set.
Model Serving
One serving layer loads, answers and scales each model, with limits on memory and demand and dashboards your team can read.
What Changes for You
Capacity That Fits the Work
The cluster is sized for the models you will serve and the demand you expect, so you buy or rent only what the workload needs.
Models That Answer Reliably
Serving software loads each model, batches requests and scales with demand, keeping response time inside the target you set.
A Cluster You Can See
Use, memory, temperature and failures are measured in one place, so your team sees a problem before users do.
How the Work Runs
We size the work before we choose the hardware. Each stage produces a document your team can check, and no capacity is ordered until the demand and the quality bar are agreed.
Size the Workload
We list the models, their memory needs and the demand each must meet, and you receive a sizing report with every assumption written down. You give us the quality bar and the demand figures the design must meet, and you approve the sizing before any hardware is chosen.
Design the Cluster
We agree the accelerators, networking, storage, cooling and serving software, and the result is a cluster design your team can review. You decide what must stay inside your estate and what may use cloud capacity, and you approve the design before installation.
Build and Connect
The cluster is installed and connected to your network and identity, and your team supplies the addresses and access rules first. We test the power and cooling envelope with the facilities team before the first model is loaded.
Test the Models
We load the models, measure response time and run a load test at your expected peak. You receive a load test report with the figures behind it. You approve the test workload and the pass mark before the test starts, and memory headroom is checked at peak.
Hand Over
You receive the runbooks, dashboards, a capacity plan and the upgrade steps, and we train the people who own each task. A model update is rehearsed on a copy of the serving stack before it reaches users. Your team can check response time at peak, utilisation and the cost per thousand requests from the same dashboards.
Platforms and Technologies
We work with NVIDIA graphics processing units (GPUs), on your premises or in Amazon Web Services (AWS) and Google Cloud. Red Hat Enterprise Linux and OpenShift provide the operating system and container platform. Kubernetes schedules the work, and vLLM serves models to applications. Open models such as DeepSeek and Qwen run on the same serving layer.
- AI and machine learning
- vLLM
- DeepSeek
- Qwen
- Cloud
- NVIDIA GPUs
- Amazon Web Services
- Google Cloud
- Red Hat Enterprise Linux
- DevOps and observability
- Red Hat OpenShift
- Kubernetes
Naming a product does not mean a partnership with its vendor.
NVIDIA
We size and build GPU estates on NVIDIA for training and inference, then prove the stack under a real workload.
AWS
We set up AWS accounts and guardrails, move workloads with its migration tools and keep cost and access visible.
Google Cloud
We build data, analytics and AI systems on Google Cloud, with BigQuery, Vertex AI and GKE under one control model.
Red Hat
We build OpenShift platforms on Red Hat, move applications into containers and keep operations planned.
Industries
Media and Telecommunications
Consumer platforms, streaming, connected devices and ad-tech that stay fast at peak audience, with AI for content and personalisation.
Public Sector
Citizen portals, case management, payments and integrations with government registries, designed to recognised accessibility guidelines.
Software and Technology Companies
Software companies and technology teams: multi-tenant products, platforms and the AI features and billing that keep them growing.
Questions
What buyers ask before they commit to AI compute.
Should we buy GPUs or rent cloud capacity?
Own capacity suits steady demand and data that must stay inside your estate. Rented capacity suits short experiments and peaks. We compare both before you commit.
How do you size a cluster?
From the models, their memory needs and the peak demand you expect. We add headroom for failures and write down every assumption, so you can check the sizing before you commit.
Which models can the cluster serve?
Open models such as Llama, Mistral and Qwen run well on owned capacity. We test each model's quality and speed before it goes into service.
Who owns the cluster and the models?
You own the hardware, the configurations and the runbooks. You also own the models and the data you use with them.
What does an AI infrastructure engagement cover?
Sizing, design, installation, model serving, testing and handover. We can also run the cluster under a managed agreement. Model building and application development belong to your AI teams or our AI practice.
Tell Us What You Need to Run, Build or Secure.
Describe the service, the systems and the pressure you are under. The right team takes it from there.