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PRIVATE GPU CLOUD & AI FACTORY

Accelerate the delivery of AI projects with open, scalable AI infrastructure. Deploy faster, cut costs, stay compliant, and retain control over your data, models, and operational strategy.

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SCALABLE AI INFRASTRUCTURE TO BUILD AND RUN APPLICATIONS ANYWHERE

Organizations racing to operationalize AI systems face steep challenges: GPU scarcity, high costs, fragmented infrastructure, and risks to data security and compliance. Relying on hyperscalers or proprietary stacks can introduce even more complexity and long-term lock-in.

Mirantis helps you build scalable AI infrastructure using open, composable stacks that span data center, cloud, and edge. Gain the freedom to scale model development and inference anywhere—faster, more securely, and at lower cost. Optimize GPU usage, ensure compliance across geographies, maintain data, model, and access sovereignty, and accelerate time-to-value.

Accelerate AI While Reducing Cost and Risk

Deploy secure, scalable AI infrastructure faster. Improve efficiency in real time, cut GPU spend, and stay compliant, without sacrificing control or agility.

Outcomes:

Deploy AI in days with reusable templates

Run apps predictably with hard multitenancy

Cut GPU costs with smart bin-packing and scaling

Stay compliant with built-in policy automation

Inference Anywhere: Reliable AI Operations Across Any Environment

Inference Anywhere is a production-grade platform for deploying and operating AI and machine learning models across cloud, datacenter, and edge with low latency, high security, and full control.

Features/Key Capabilities:

Provision GPU infra with cost-aware orchestration

Enable secure multi-tenant model operations

Accelerate training with turnkey MLOps pipelines

Scale inference with smart routing and controls

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AI Scaling that Keeps Infrastructure in Sync with Business Growth

Enterprises need to scale AI infrastructure in ways that support rapid innovation while maintaining compliance and operational resilience as workloads expand. Mirantis enables organizations to optimize AI infrastructure evolution by delivering composable, policy-driven, and GPU-optimized infrastructure that scales securely.

Faster Time-to-Value: Deploy AI and ML environments in days using reusable, declarative templates that automate provisioning and configuration

Stronger Compliance: Uphold regional and industry regulations through built-in policy automation and data sovereignty controls

Greater Flexibility: Seamless scale across clouds, data centers, and edge environments with a hybrid and composable architecture

Enhanced Security: Enforce zero-trust principles and hard multi-tenancy to safeguard data, models, and workloads at every layer

Reliable AI Operations: Ensure consistent performance and uptime with unified observability, FinOps, and lifecycle management across all AI clusters

DATASHEET

From Metal-to-Model™ — Simplify AI Infrastructure

k0rdent AI enables enterprises and service providers to accelerate AI adoption with trusted, composable, and sovereign infrastructure.

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PRODUCT

Mirantis k0rdent AI 

Mirantis k0rdent AI delivers scalable, secure AI inference across cloud, datacenter, and edge. 

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REFERENCE ARCHITECTURE

Power the Next Generation of AI with Industry-Standard AI Factories

Deliver Sovereign, GPU-Powered AI Clouds at Scale.

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LET’S TALK

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

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

Frequently Asked Questions About Scaling AI Infrastructure

Why Is It Critical for Enterprises to Adopt Flexible AI Infrastructure?

A flexible AI infrastructure allows enterprises to deploy and manage AI applications and machine learning (ML) workloads seamlessly across on-prem, cloud, and edge environments. Open, composable, Kubernetes-based architecture lets organizations scale flexibly, uphold data sovereignty, and avoid vendor lock-in.

How Does Mirantis Help Reduce GPU Costs While Scaling AI Infrastructure?

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.

What Are the Biggest Challenges Enterprises Face with Scalable AI Infrastructure?

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.

How Does Mirantis Ensure Compliance and Data Sovereignty in AI Operations?

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.

Can Mirantis AI Factory Run Both Training and Inference Workloads to Accelerate AI Delivery?

Yes. Mirantis AI Factory supports the full lifecycle of AI and machine learning (ML) workloads, including model training, fine-tuning, and inference. Using Inference Anywhere and GPU-optimized pipelines, teams can operationalize models faster while ensuring data consistency, reliability, and performance across distributed environments.

What Makes Mirantis One of the Most Reliable, Scalable AI Infrastructure Services?

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.

How Does Mirantis Handle GPU Scarcity and Optimize AI Infrastructure for Scalability?

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.

Can Mirantis Integrate with Existing MLOps Tools to Support Reliable AI Operations?

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.

What Are AI Scaling Laws?

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.

How Does Test-Time Scaling Improve the Efficiency of AI Models?

Test-time scaling improves model efficiency by adjusting computation dynamically during inference, using methods like selective activation or adaptive batching to reduce latency and energy consumption. This ensures faster, more cost-efficient inference while maintaining model accuracy.

PRIVATE GPU CLOUD & AI FACTORY

SCALABLE AI INFRASTRUCTURE TO BUILD AND RUN APPLICATIONS ANYWHERE