Services
AI Infrastructure
Build and operate the infrastructure for training, deploying and monitoring AI models.
01 — Scope
Deploy and operate AI workloads.
When we can help
- GPU capacity is costly, difficult to share and unevenly used.
- Manual deployments make it difficult to track which model version is in use.
- There is no single view of latency, cost or quality across AI workloads.
- Your team needs to support AI workloads without a tested operating process.
What we do
- GPU infrastructure in the cloud and on-premises, including scheduling and sharing
- Kubernetes for AI: training, inference and batch workloads
- Model serving and inference platforms, including open-weight models
- MLOps: training pipelines, registries, deployment and rollback
- Monitoring of model response times, running costs, quality and drift
- Capacity planning and cost control for AI workloads
02 — Delivery
What your team can expect
- A clear view of GPU capacity, utilisation and running costs
- Versioned model deployments with a tested rollback process
- Operational documentation and ownership agreed with your platform team
Platforms and tools
Chosen around your environment and requirements.
- NVIDIA GPUs and operators
- Kubernetes
- Ray
- vLLM
- Triton
- KServe
- MLflow
- Weights & Biases
- AWS, Azure and Google Cloud AI platforms
- Scaleway GPU instances
- Terraform
Talk to Vio Engine
Tell us what you need to change, build or support.