Mirantis Automates AI Factory Deployments with k0rdent AI and NVIDIA Run:ai

Mirantis, delivering Kubernetes-native infrastructure for AI, today announced the company is enabling enterprises and neocloud providers to deploy production-ready AI platforms in minutes, not weeks which is achieved by automated deployment and lifecycle management of NVIDIA Run:ai through the Mirantis k0rdent AI platform. The integration closes the gap between GPU infrastructure provisioning and a fully operational AI factory.

As organizations race to build private AI factories, many discover that the journey from acquiring and provisioning AI infrastructure to deploying and operating it at factory scale represents a meaningful next phase of adoption. Provisioning GPU nodes is only the first step, teams must then integrate orchestration layers, sequence complex operator dependencies, configure networking and resource allocation, and validate all of the infrastructure before running a single training or inference job.

“Enterprises don’t struggle to purchase GPUs — they struggle to operationalize them,” said Richard Borenstein, senior vice president of business development, Mirantis. “By automating the deployment of NVIDIA Run:ai as part of a full-stack AI factory platform, k0rdent AI enables organizations to move from infrastructure delivery to workload execution in a fraction of the time, with repeatable, production-grade outcomes.”

“Enterprises and cloud providers are looking for ways to accelerate the path from infrastructure to production AI,” said Omri Geller, vice president and general manager, NVIDIA. “By integrating NVIDIA Run:ai with Mirantis k0rdent AI, customers can automate the deployment of AI factory environments, enabling faster time to value, improved GPU utilization, and more efficient scaling of AI workloads across multi-tenant environments.”

Mirantis k0rdent AI automates this layered assembly process — from bare metal through AI workload orchestration — including automated deployment of NVIDIA Run:ai, the AI workload and GPU scheduling layer in the NVIDIA AI Factory reference architecture.

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NVIDIA Run:ai in the AI Factory Stack

NVIDIA Run:ai provides the AI workload and GPU orchestration layer which is directly above infrastructure. It enables data scientists, ML engineers, and platform operators to submit training jobs, run inference workloads, and launch interactive notebooks via UI, CLI or API — without needing to manage Kubernetes clusters or underlying GPU configuration.

While powerful, inference control planes and GPU schedulers can be complex to deploy and operate, particularly across organizational boundaries between IT and data science teams. The Mirantis integration eliminates that complexity.

As a validated member of the NVIDIA AI Cloud Ready initiative, Mirantis built k0rdent AI to automate AI factory infrastructure layers from bare metal provisioning through platform services deployment. The platform automatically installs, configures, and sequences:

• Ingress and external DNS;
• cert-manager;
• NVIDIA GPU Operator;
• NVIDIA Network Operator;
• Dynamic Resource Allocation (DRA) Operator;
• MPI Operator;
• Training Operator;
• NVIDIA Run:ai platform templates.

k0rdent AI handles dependency sequencing, configuration validation, and infrastructure readiness checks — removing manual intervention and eliminating the tribal knowledge gained only through experience that is often required to operationalize AI infrastructure.

Now, what typically takes experienced engineers days or weeks can be completed in minutes. For neocloud providers, this enables on-demand AI factory provisioning — spinning up a full NVIDIA Run:ai environment when needed and tearing it down when idle – to maximize GPU utilization. For enterprises, it delivers consistent, repeatable AI factory deployments across teams, regions, and IT infrastructure.

Built for Regulated and Sovereign AI Environments

The integration supports air-gapped deployments, enabling fully-isolated AI factory environments with no external network dependencies. This ensures repeatable automation for regulated industries and government organizations operating in network-restricted infrastructure.

k0rdent AI also supports the latest rack-scale GPU architectures, including native support for NVIDIA Grace Blackwell NVL72 systems via NVIDIA NCX Infra Controller (NICo). NICo delivers zero-touch lifecycle automation for bare-metal systems that secures datacenter infrastructure at its foundation.

Certified for NVIDIA Run:ai Compatibility

The k0rdent AI and NVIDIA Run:ai integration has been tested and validated through NVIDIA Run:ai’s certification program. Mirantis executed more than 100 functional tests covering workload submission, scheduling behavior, multi-tenant operations, and platform lifecycle management, achieving partner-certified status. Mirantis k0rdent is listed among the officially certified NVIDIA Run:ai partner distributions.

Full Lifecycle AI Factory Automation

The current integration establishes the foundation for complete AI factory lifecycle management. Future roadmap enhancements include declarative upgrades, configuration drift management, and expanded day-two operations automation — all managed through k0rdent AI’s Kubernetes-native control plane.

As the AI factory model becomes the standard for enterprise private AI and neocloud GPU services, the Mirantis and NVIDIA Run:ai integration accelerates time to production while ensuring infrastructure remains automated, repeatable, and ready to scale.