PRIVATE GPU CLOUD
& AI FACTORY

Mirantis reduces GPU costs by using declarative templates, GPU bin-packing, and fractional GPU provisioning strategies that maximize utilization across workloads. The k0rdent FinOps integration allows operators to monitor GPU performance and cost in real-time, optimize resource allocation, and eliminate idle capacity while maintaining consistent service levels.

When it comes to AI infrastructure, enterprises often struggle with long time-to-value, lack of data sovereignty, GPU scarcity, and skill gaps. AI infrastructure also introduces aspects that require specialized tuning and automation such as high-performance networking, distributed storage, and GPU slicing.

Mirantis enforces compliance through policy-driven automation and a sovereign AI infrastructure model that ensures AI workloads adhere to regulatory compliance across regions. Zero Trust and confidential computing further guarantee isolation and security.

Mirantis delivers reliability through its declarative, template-based control plane and integrated observability stack. Its unified lifecycle management, multicloud support, and high-performance networking enable continuous optimization. This allows enterprises to scale confidently while driving innovation and protecting their AI investments.

Mirantis addresses GPU scarcity by managing AI environments through dynamic resource provisioning and compute resources orchestration. With fractional GPU provisioning and automated bin-packing, Mirantis maximizes utilization across training and inference workloads. This approach supports artificial intelligence scaling by reducing GPU idle time and maintaining performance isolation.

Yes. Through the k0rdent catalog, Mirantis integrates directly with popular MLOps frameworks such as ArgoCD, ClearML, and Kubeflow. This enables organizations to align their AI initiatives with existing development pipelines and ensure consistent model deployment, monitoring, and lifecycle management across all environments.

AI scaling laws describe how model performance improves predictably with increases in compute power, dataset size, and parameter count. Understanding these laws helps organizations balance training costs, resource allocation, and infrastructure scaling to achieve optimal accuracy and efficiency in large AI models.