GPU infrastructure
GPU infrastructure is the foundation that makes serious AI work at scale, training, fine-tuning, inference, and deployment across environments where latency, cost, and reliability all matter. We help teams move from local experiments to production-grade compute: selecting the right hardware or cloud setup, designing deployment pipelines, and connecting models to the applications that depend on them.
The problem we solve is familiar: AI projects stall when compute is an afterthought. Teams hit memory limits, unpredictable cloud bills, slow iteration cycles, or deployment gaps between a working notebook and a system users can actually rely on. We bring hands-on infrastructure experience so your models run where they need to, efficiently and maintainably.
This direction is for AI-native startups, enterprise teams building internal AI products, and organisations that need dedicated inference capacity or hybrid cloud/on-premise setups. Through NVIDIA Inception and our work with the CEE Partners network on DGX Cloud Lepton, we have direct exposure to global GPU cloud platforms, agentic AI workflows, and the ecosystem around NVIDIA's compute stack.
Whether you need help architecting a deployment, optimising inference costs, or connecting to managed GPU cloud services, we approach infrastructure as part of the product, not a separate concern left to the last sprint.
Problems we solve
- Models stuck on laptops or ad-hoc cloud instances
- Unpredictable inference costs and performance
- No clear path from notebook to production serving
- Missing monitoring and autoscaling for AI workloads
Who this is for
- Teams deploying LLM or vision models to production
- Startups needing GPU cloud architecture advice
- Enterprises evaluating hybrid or dedicated inference
- Product groups connecting AI features to existing apps
What we deliver
- GPU cloud architecture and provider evaluation
- Model deployment pipelines (inference and batch)
- Containerised AI workloads and orchestration
- Cost and performance optimisation for inference
- Hybrid and multi-cloud compute strategies
- Integration with application layers and APIs
- Technical advisory for AI infrastructure roadmaps
Technologies & stack
- NVIDIA GPUs, CUDA, and DGX Cloud Lepton
- Docker, Kubernetes, and container orchestration
- PyTorch, ONNX, and model serving frameworks
- AWS, GCP, and specialised GPU cloud providers
- Monitoring, autoscaling, and CI/CD for ML workloads
How we work
- 01Assess workloads, latency targets, and budget constraints
- 02Propose architecture and a pilot deployment path
- 03Implement serving, monitoring, and cost controls
- 04Hand over runbooks and iterate with your team
Selected reference
NVIDIA Inception programme member. Hands-on contribution to DGX Cloud Lepton via the CEE Partners network, a GPU cloud platform connecting developers to global compute for inference, training, and AI deployment. Participation in GTC Paris 2025 with workshops on Agentic AI.
Frequently asked questions
Do you sell GPUs or only advise?
We help you architect and deploy. Hardware or cloud spend sits with your chosen providers. We focus on making that spend useful.
Can you help with on-premise as well as cloud?
Yes. Hybrid and on-premise options are in scope when latency, data residency, or cost require them.
Is this separate from your AI product work?
It is related but secondary to our core software and AI application delivery. We bring infrastructure when compute is blocking the product.
What is NVIDIA Inception in practice for clients?
It means we stay close to NVIDIA's ecosystem and programmes such as DGX Cloud Lepton work through CEE Partners. Clients get informed architecture choices, not a badge on a slide.
Related services
Need help with AI compute?
Share your current setup and goals. We can advise on architecture, deployment, or how to connect your models to reliable GPU infrastructure.
Get in touch