Sovereign AI Infrastructure

Cloud-based LLM APIs are easy to start with, but often unsuitable for sensitive data, compliance, and cost planning. Private infrastructure gives full control over data, models, and operating costs. In Switzerland, data residency requirements for healthcare, finance, and government data are frequently a hard requirement. A dedicated platform also enables air-gapped operation and the freedom to swap or fine-tune models.

This depends heavily on the use case. For inference, modern NVIDIA GPUs (A10G, L4, H100 variants) with a small number of nodes are often sufficient – depending on model size and throughput. Training requires significantly more capacity and is often better started in the cloud. We assess your use case and recommend a realistic capacity plan – existing on-premise GPUs can often be integrated sensibly.

We integrate AI workloads into existing Kubernetes environments – no parallel structure. This covers GPU scheduling with the NVIDIA GPU Operator, namespace isolation, RBAC, and existing observability stacks. LLM serving with vLLM, Ollama, or KServe is embedded into the same GitOps processes as other workloads. The result is an operable platform, not a special project.

Data residency means that data never leaves the defined infrastructure – neither for processing nor telemetry. Concretely: models run on your own hardware, there are no connections to external model providers, and access logs are local and auditable. In Switzerland this typically means data centres in Switzerland or the EEA and conformity with the nDSG.