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Monetize GPU Infrastructure with Mirantis k0rdent AI

A GPU PaaS provides on-demand access and lifecycle management of GPU computing resources, so AI engineers can run their workloads without worrying about underlying hardware. Mirantis k0rdent AI’s built-in GPU PaaS operationalizes AI hardware the day it arrives. By abstracting the complexities of GPU management, teams can focus on building, training, and deploying AI models, accelerating AI initiatives while reducing operational overhead. 

Mirantis has 15 years of infrastructure expertise and can help you build AI infrastructure for your specific workloads, operate it 24x7 with guaranteed uptime SLAs, and transfer operations to your teams when you’re ready.


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NEOCLOUDS: LAUNCH AI SERVICES FASTER

Mirantis k0rdent AI compresses service development cycles, giving neoclouds first-mover advantage in emerging segments and enabling rapid GPU monetization. Maximize profit margins through intelligent resource allocation that prevents costly overprovisioning while serving more customers per GPU. Make it completely frictionless for customers to purchase valuable services on demand with a simple credit card transaction, drawing in higher margins.

Monetize GPU investments fast — Operationalize GPUs the same day hardware arrives, gaining a competitive advantage through faster service launches.

Optimize GPU economics through intelligent partitioning — Maximize ROI by allocating GPU slices across tenants, ensuring efficient utilization while maintaining strict isolation.

Monitor and rightsize GPU allocations — Track per-tenant GPU consumption, identify overprovisioning, and adjust quotas to match actual workload requirements.

Secure access across multiple teams — For customers in finance, healthcare, and government, enforce security, compliance, and auditability with hard multi-tenancy, RBAC, authentication, and built-in observability.

Enterprise AI: Ensure Speed and Governance

Mirantis k0rdent AI’s built-in GPU PaaS empowers enterprises to govern GPU resources at scale while maintaining governance, economic discipline, and centralized control.

Accelerate AI innovation — Operationalize GPUs the same day hardware arrives, making resources available for AI/ML workloads on Kubernetes without the typical integration and setup delays.

Govern GPU allocation through centralized control — Use the operator console to manage quotas, monitor per-tenant consumption, and enforce policies that prevent rampant overprovisioning.

Partition GPUs for maximum efficiency — Allocate GPU slices using NVIDIA MIG, vGPU, or software-based sharing to increase utilization across multiple teams while keeping performance isolation.

Focus on your core competency — Let your teams focus on strategic AI innovation by letting Mirantis manage the infrastructure 24x7 with the assurance of guaranteed availability SLAs.

Screenshot of a GPU configuration panel showing instance type, storage details, and deployed cluster services like NVIDIA GPU Operator and ArgoCD.

Easily provision GPU clusters complete with cloud native and AI integrations.

Flexible Infrastructure for AI Innovation

AI CLOUD
SOVEREIGN CLOUD
ENTERPRISE AI FACTORY

AI Clouds for Neoclouds

Mirantis k0rdent AI delivers GPU provisioning APIs and operator tools needed to rapidly commercialize AI infrastructure. The operator console enables platform architects to design GPU-backed service offerings with configurable slicing modes, set pricing and quotas, and monitor real-time utilization across all tenants.

Monetize GPU infrastructure rapidly — Integrate metering APIs with billing systems to automatically track usage, generate invoices, and process payments for GPU resources and services.

Maximize GPU utilization through intelligent partitioning — Allocate GPU slices across multiple tenants to avoid stranded capacity and improve hardware ROI.

Provide self-service GPU provisioning — Enable tenants to browse available GPU offerings, view transparent pricing, provision resources instantly, and track consumption through an integrated cloud portal.

Offer secure, multi-tenant infrastructure — Provide hard multi-tenancy with isolation at GPU, VM, and Kubernetes layers to meet compliance and security requirements.

Illustration of glowing digital clouds with circuit patterns, symbolizing cloud computing and data technology, on a dark background.Illustration of glowing digital clouds with circuit patterns, symbolizing cloud computing and data technology, on a dark background.

BLOG: A European Cloud Reckoning: Why Hybrid Sovereignty Demands New Thinking—And New Tools

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Sovereign Clouds for Neoclouds

Mirantis k0rdent AI enables neoclouds to rapidly launch compliant, sovereign GPU infrastructure with complete control over data residency and regulatory requirements.

Monetize infrastructure investments – Integrate metering, billing, and payment APIs seamlessly to track usage, generate invoices, and manage customer payments transparently.

Enforce data sovereignty — Use the operator console to define GPU provisioning policies that automatically enforce locality rules, ensuring workloads remain within regulatory boundaries.

Accelerate delivery of sovereign clouds – Deploy compliant multi-tenant GPU resources rapidly using declarative templates that standardize provisioning across regions.

Ensure strict tenant isolation – Enforce hard multi-tenancy across compute, storage, and networking layers, meeting regulatory requirements for data sovereignty and security compliance.

Illustration of glowing digital clouds with circuit patterns, symbolizing cloud computing and data technology, on a dark background.Illustration of glowing digital clouds with circuit patterns, symbolizing cloud computing and data technology, on a dark background.

BLOG: A European Cloud Reckoning: Why Hybrid Sovereignty Demands New Thinking—And New Tools

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Enterprise AI Factory

Mirantis k0rdent AI is the enterprise AI Factory platform that reduces time-to-market for new AI-powered products through unified infrastructure automation. Manage the complete GPU lifecycle—from bare metal discovery and driver deployment to advanced slicing with NVIDIA MIG and vGPU.

Provision GPUs with declarative automation — Deploy GPU resources using templates that standardize hardware discovery, driver installation, and slicing configuration, reducing setup time.

Govern GPU allocation through centralized control — Use the operator console to set quotas, monitor per-tenant consumption, and enforce policies that prevent overprovisioning and maximize hardware ROI.

Partition GPUs for efficient multi-tenancy — Allocate GPU slices using NVIDIA MIG, vGPU, or software-based sharing to safely run multiple teams on shared hardware while keeping performance isolation.

Rightsize resources with integrated observability — Track GPU usage patterns to identify when teams request more resources than needed, then adjust allocations to match actual workload requirements.

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EXECUTIVE BRIEF: Mirantis AI Factory Reference Architecture

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LET’S TALK

Contact us to learn how Mirantis can accelerate your cloud initiatives.

We see Mirantis as a strategic partner who can help us provide higher performance and greater success as we expand our cloud computing services internationally.

— Aurelio Forese, Head of Cloud, Netsons

image

We see Mirantis as a strategic partner who can help us provide higher performance and greater success as we expand our cloud computing services internationally.

— Aurelio Forese, Head of Cloud, Netsons

image

FAQ

Q:

What Is GPU Infrastructure?

A:

GPU infrastructure refers to the integrated hardware and software stack that enables high-performance computing for AI workloads, especially for training and inference. It includes GPUs, networking, storage, Kubernetes orchestration, virtualization, and management tools used for building AI infrastructure that can handle massive parallel processing

Q:

How Does a GPU Platform as a Service Simplify AI Infrastructure Management?

A:

A GPU Platform as a Service abstracts away the complexity of managing AI infrastructure by providing on-demand access to GPU resources. This allows teams to focus on artificial intelligence (AI) and AI training instead of provisioning, scaling, and maintaining GPU hardware. For example, Mirantis k0rdent AI’s GPU PaaS layer provides declarative automation, configuration reconciliation, and drift detection to remove operational complexity and help mitigate the AI skills gap in many organizations.

Q:

How Can Organizations Monetize Idle or Underutilized GPU Resources?

A:

Organizations can monetize GPU infrastructure that is idle or underutilized by using secure multi-tenancy with strict isolation and GPU utilization monitoring to securely share or lease every GPU slice to internal teams or external users. This approach helps maximize return on investment per GPU by maintaining maximum GPU utilization.

Q:

How Does Effective AI GPU Cloud Infrastructure Improve Performance and Cost Efficiency?

A:

Effective AI GPU cloud infrastructure improves performance through optimized GPU acceleration and high-speed data movement. At the same time,  building GPU clouds with secure multi-tenancy, GPU utilization monitoring, and rightsizing helps control costs by matching resources to actual workload demands. For organizations with highly fragmented GPU estates sprawling outside of core IT’s control, effective AI GPU cloud infrastructure also provides centralized control and governance to eliminate orphaned resources.

Q:

What Features Should I Look for in GPU Cloud Infrastructure Solutions?

A:

The right GPU cloud infrastructure features depend on your use case and organizational requirements. For most use cases, key features include automated GPU lifecycle management, secure multi-tenancy, unified infrastructure and services management, GPU resource monitoring and optimization, and cost tracking. For neoclouds and service providers building commercial AI clouds, other key features include declarative automation templates, rapid GPU operationalization, integrated billing and metering, and customizable self-service portals. For enterprises running regulated AI workloads, other key features include policy-driven compliance automation, support for airgapped deployments, centralized audit logging, flexibility to integrate existing architectures, and onboarding and implementation services.

Q:

Why Is GPU Infrastructure Essential for AI Workloads?

A:

GPU infrastructure is essential because AI workloads require an integrated hardware and software stack to operate at scale. Beyond GPU compute, organizations need Kubernetes orchestration, virtualization, networking, and governance layers to provision resources efficiently, maintain security and compliance, maximize GPU utilization, and demonstrate ROI on GPU investments.

Q:

What Are the Key Components of Modern GPU-Optimized Cloud Infrastructure?

A:

Modern GPU-optimized cloud infrastructure combines high-performance GPUs, fast networking, scalable storage, Kubernetes orchestration, virtualization, and governance layers designed for AI workloads. Increasingly, organizations also prioritize AI cloud sovereignty to ensure control over data storage, governance, and compliance and implementing this requires policy automation frameworks, secure software supply chain verification, and centralized audit logging systems.

Q:

What Is the Difference Between GPU PaaS and Infrastructure as a Service (IaaS)?

A:

Infrastructure as a Service (IaaS) provides IT infrastructure automation, such as for bare metal provisioning, virtualization, multi-cluster Kubernetes management, and hybrid multicloud deployments. IaaS delivers the foundational layer that automates compute, storage, and networking resources.

GPU PaaS builds on this foundation to deliver managed, on-demand access to GPU computing resources specifically optimized for AI workloads. GPU PaaS abstracts GPU lifecycle management, including hardware discovery, driver deployment, intelligent partitioning, and consumption monitoring.

Q:

How Does Mirantis k0rdent AI Ensure Multi-Tenancy, Security, and Compliance for GPU Clouds?

A:

Mirantis k0rdent AI supports secure multi-tenant GPU clouds through hard multi-tenancy with isolation across GPU, VM, and bare metal, and support for multi-tenant networking with strict VLAN isolation and policy enforcement. Other security and compliance features include software supply chain security with artifact scanning and signing and SBOM verification, policy-driven automation, configuration reconciliation, integrated audit logging and observability, data residency controls, and airgapped deployments. Additionally, Mirantis has specialized expertise and proven track record in deploying accredited, STIG- and FedRAMP-ready solutions.

Q:

How Should I Evaluate GPU Infrastructure Companies?

A:

Prioritize providers with proven expertise in both cloud infrastructure and AI workload requirements, demonstrated through customer deployments at scale and partnerships with NVIDIA and other vendors. Assess the platform's architectural approach—whether it supports flexible deployment models (bare metal, hybrid, multicloud) and provides the multi-tenancy and security controls your use case demands. If long-term flexibility is important to you, prioritize platforms built on open standards rather than proprietary technologies. Evaluate the breadth of their ecosystem integrations and ability to support your preferred AI/ML tools and frameworks. Finally, consider operational factors including observability capabilities, service level commitments, and whether their support model (self-managed, co-managed, or fully managed) aligns with your team's expertise and strategic priorities.

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