Solutions · GPU Hosting & MLOps
Serve and fine-tune models on infrastructure you control
Kubernetes GPU platforms for inference and fine-tuning, without the idle bill.
The problem
Where teams get stuck
GPUs are the most expensive line on the cloud bill and often the least utilised. Models are deployed by hand, latency is unpredictable and nobody can say what a thousand tokens actually costs.
Reference architecture
The building blocks
- Compute
- GPU node pools on EKS / GKE / AKS, provisioned on demand with Karpenter
- Drivers
- NVIDIA GPU Operator, MIG / time-slicing where it fits
- Serving
- vLLM and Triton Inference Server behind KServe
- Training
- Ray clusters for distributed fine-tuning and batch inference
- Registry
- MLflow model registry, artefacts in object storage
- Delivery
- GitOps promotion of model versions with canary rollouts
- Observability
- DCGM exporter, latency and token metrics, cost per model
How we deliver
A typical engagement
Weeks 1–2
Workload profiling: model sizes, traffic shape, latency targets and data residency.
Weeks 3–6
GPU platform built as code; first model served with autoscaling and dashboards.
Weeks 7–8
Fine-tuning pipeline on Ray; model promotion through GitOps.
Ongoing
Cost and utilisation reviews; right-sizing and scheduling policies.
What changes
Targets we agree up front
- Target: GPU utilisation above 60% during business hours
- Cost per 1,000 tokens, tracked per model
- p95 inference latency against an agreed SLO
- Model release by pull request, not by SSH
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