AI Infrastructure - GPU clusters engineered for production
From single-node fine-tuning rigs to multi-rack H100/H200 clusters with InfiniBand fabrics, we build AI infrastructure that trains, serves, and scales in production.
Production AI needs production infrastructure
Notebook experiments do not translate to reliable inference under load. Our AI infrastructure practice covers compute, storage, network fabric, MLOps tooling, and the security controls needed to serve models to real users.
The full AI infrastructure stack
GPU Compute Clusters
NVIDIA H100, H200, L40S, and A100 nodes with NVLink and NVSwitch topologies.
High-Speed Fabric
InfiniBand HDR/NDR or 400G RoCE for low-latency distributed training.
Parallel Storage
WEKA, VAST, or Ceph tiers sized for checkpointing and inference throughput.
MLOps Platform
Kubeflow, MLflow, Ray, and NVIDIA NIM microservices, deployed and managed.
Model Serving
Triton, vLLM, and TGI stacks with auto-scaling and A/B evaluation.
AI Security
Model isolation, prompt-injection defence, data-loss prevention, and audit logging.
Frequently asked questions
Both. We deliver turn-key GPU pods inside your data centre, at partner colocation, or on sovereign cloud - with a single operating model across sites.
Yes. We deploy NVIDIA MIG and time-slicing on Kubernetes so multiple teams share H100/A100 GPUs without noisy-neighbour interference.
H100/H200 nodes typically land in 8-14 weeks depending on quantity. We design the rest of the stack in parallel so you commission on delivery day one.
Plan your AI infrastructure roadmap
Book a design workshop and leave with a sized cluster, network topology, and phased rollout plan.