AI Cloud Platform
A cloud purpose-built for AI workloads — GPUs with high-speed interconnects, data paths tuned for large datasets, and autoscaling inference endpoints that take models from training to production without rebuilding the stack. No retrofitting a web cloud to run ML.
Overview
General-purpose cloud was not built for AI workloads. Training runs need GPU density, high-throughput interconnects, and low-latency storage that standard instances cannot provide. AI Cloud Platform is purpose-built infrastructure for machine learning. It delivers NVIDIA GPU clusters with NVLink interconnects, high-performance parallel file systems, and containerised orchestration for training, fine-tuning, and inference deployment. Your ML team gets infrastructure that matches the performance of their models without managing GPU cluster operations.
Clevertek scopes every engagement to your environment — capacity, sites, compliance and support model — so you get a tailored plan rather than a fixed SKU. Pricing is quote-only, and our solutions architects will work through your requirements before any proposal.
Our approach
We provision and manage AI-optimised cloud infrastructure. That means NVIDIA GPU clusters (A100, H100, H200, B200), high-throughput storage (Lustre, GPUDirect, NVMe), and container orchestration (Kubernetes with GPU operator, Kubeflow, MLflow). Clusters are sized to your training and inference workloads, from single-node GPU instances to multi-node clusters with 100+ GPUs. We handle GPU driver management, container runtime configuration, job scheduling integration, and 24x7 monitoring of GPU utilisation, temperature, and memory.
Why work with us
NVIDIA GPU clusters — A100 to B200
Latest-generation NVIDIA GPUs with NVLink and NVSwitch interconnects — H100 and B200 for training, A100 for inference. Clusters sized to your workload, not pre-packaged.
High-throughput parallel storage
Lustre and GPUDirect storage with 100+ GB/s throughput — no I/O bottlenecks during training. Data pipeline and checkpoint storage designed for GPU utilisation.
Kubernetes with GPU operator
Containerised GPU orchestration with Kubernetes — Kubeflow for ML pipelines, MLflow for experiment tracking, and automated GPU scheduling for utilisation.
Training and inference on the same platform
Same infrastructure supports training runs and production inference deployments — no need to provision separate environments for development and production.
Multi-tenant GPU scheduling
Multiple teams share the cluster with namespace isolation, GPU quotas, and priority-based scheduling — no dedicated GPU per team, no idle capacity.
24x7 GPU cluster monitoring
GPU utilisation, memory, temperature, and interconnect health — monitored continuously with alerts for thermal throttling, ECC errors, and utilisation anomalies.
Key benefits
What this solution delivers for your business.
GPU infrastructure without cluster ops
Your ML team gets the GPU capacity they need without managing cluster operations — driver updates, interconnect cabling, cooling, and health monitoring are handled.
Faster training cycles
NVLink interconnects and GPUDirect storage eliminate I/O and communication bottlenecks — training runs complete faster, model iteration cycles shorten.
Cost-efficient GPU utilisation
Multi-tenant scheduling keeps GPUs utilised across teams. Idle GPU time on one team jobs is available to another team jobs — no dedicated, underutilised hardware.
Elastic scaling for training needs
Scale from a single GPU for experimentation to 100+ GPUs for training runs — capacity provisioned on demand, released when the job completes.
Production-ready inference deployment
Trained models deployed to production inference endpoints on the same platform — no model export/import, no inference infrastructure to manage separately.
What's included
Part of this managed service.
GPU compute clusters
NVIDIA GPU instances with NVLink interconnects — A100 (80GB), H100 (80GB SXM / 94GB HBM3), H200 (141GB HBM3e), B200.
- A100 / H100 / H200 / B200 GPUs
- NVLink and NVSwitch
- Multi-node scaling (2-100+ GPUs)
- GPU memory up to 141GB per GPU
High-performance storage
Parallel file system with GPUDirect support — Lustre, NVMe, and object storage tiered for training, checkpoint, and archive data.
- Lustre parallel file system
- GPUDirect storage access
- 100+ GB/s throughput
- Tiered storage (hot/warm/cold)
ML platform software
Kubernetes with GPU operator, Kubeflow, MLflow, Jupyter, and container runtime pre-configured.
- Kubernetes + GPU operator
- Kubeflow ML pipelines
- MLflow experiment tracking
- Jupyter and VS Code integration
Model serving and inference
Production inference endpoints with auto-scaling, A/B testing, and model versioning.
- GPU inference endpoints
- Auto-scaling based on load
- A/B testing and canary
- Model version management
Cluster monitoring and observability
GPU utilisation, memory, temperature, power, and interconnect metrics — with job-level tracking and cost allocation.
- Per-GPU utilisation and memory
- Temperature and power monitoring
- Job-level cost tracking
- Slurm/Univa integration
Where it helps
Real-world scenarios where this solution delivers measurable outcomes.
LLM training and fine-tuning
Train or fine-tune large language models on H100 or H200 clusters with 100+ GPUs — NVLink interconnects eliminate communication bottlenecks during distributed training.
Computer vision model development
Train vision models on A100 clusters with GPUDirect storage for high-throughput image data loading — from single-GPU experimentation to multi-node production training.
Production inference deployment
Deploy trained models to production inference endpoints with auto-scaling — GPU inference capacity that scales with request volume without over-provisioning.
Multi-team ML platform
Multiple data science teams sharing a common GPU cluster with namespace isolation, GPU quotas, and priority-based scheduling — no dedicated GPUs per team.
Questions buyers actually ask
What GPU models are available?
A100 (80GB PCIe and SXM), H100 (80GB SXM, 94GB HBM3), H200 (141GB HBM3e), and B200. Cluster size ranges from single-GPU instances to 100+ GPU multi-node configurations.
Is storage included or separate?
High-throughput parallel storage is included in the cluster design. Lustre or GPUDirect NVMe with 100+ GB/s throughput, sized to your training data volume and checkpoint requirements.
Can I run both training and inference on the same cluster?
Yes. The same Kubernetes and GPU-operator cluster supports training jobs and production inference. GPU quotas and priority scheduling ensure inference capacity is not starved by training jobs.
How is the cluster managed?
We manage GPU driver updates, container runtime configuration, Kubernetes upgrades, storage maintenance, and health monitoring. Your team accesses the cluster through standard interfaces — kubectl, Jupyter, MLflow.
Ready to scope a solution?
Talk to a Clevertek solutions architect about your requirements — no obligation.