Virtualization for AI Workloads: Building Open Source GPU‑Optimized Infrastructure

As enterprise AI matures, infrastructure patterns are shifting. Teams that started with dedicated GPU servers are now building shared platforms that must support multiple workloads, enforce governance and quickly scale without overwhelming operations.

Virtualization for AI workloads can provide a practical path forward. When built on Kubernetes and open source foundations, this approach brings GPU-backed workloads under the same management, policy and observability patterns that many platform teams already use.

Virtualization for AI workloads: key takeaways

• To succeed, enterprise AI requires clear governance, repeatable provisioning and operations that can keep pace with demand.
• Standardization can serve as a business enabler in that it helps speed delivery, strengthen control, reduce infrastructure debt and preserve optionality.
• Without standardization, AI growth can fragment your platform, increase manual coordination and create new bottlenecks.
• While bare metal offers strong raw performance, cloud native virtualization may be the better choice for enterprises that need shared platforms with consistent controls.
• SUSE supports AI platformization by helping teams standardize management and governance across mixed Kubernetes estates and GPU-backed workloads.

Understanding virtualization and AI workloads

Virtualization abstracts physical hardware so that multiple workloads can share the same server. Despite sharing a server, each workload runs in an isolated environment with its own allocated compute, memory and storage. This model has powered enterprise data centers for decades, enabling consolidation, isolation, and resource efficiency at scale.

When applied to AI, virtualization introduces new opportunities and friction points. For example, the computational intensity and GPU dependency of modern AI workloads result in much different demands than those of traditional applications.

Unique challenges of AI workloads

It’s likely that AI workloads have already stressed your conventional infrastructure patterns. Training large models demands sustained GPU compute, sometimes for days or weeks at a time. Inference workloads are often bursty; they can be idle for hours before a sudden flood of requests. Both patterns create challenges for capacity planning and static resource allocation.

Data gravity adds another constraint, as models benefit from being close to the massive datasets that they process. Moving terabytes across networks slows iteration and inflates egress costs. In addition, storage must deliver high throughput while supporting the checkpoint-and-resume patterns common in distributed training.

GPU scarcity compounds these pressures. Without clear policies, early adopters may monopolize accelerators while newer projects wait indefinitely. Platform teams may end up brokering access manually, which quickly proves unsustainable. And if different teams require different versions of CUDA, PyTorch or TensorFlow, the maintenance burden will multiply further.

Bare metal vs virtualization for AI workloads

Bare metal refers to running workloads directly on physical hardware without a hypervisor or virtualization layer. For AI, this approach provides maximized GPU performance, in part because of removing virtualization overhead. Bare metal often dominates early AI deployments because pilot teams prioritize raw speed. If a single data scientist owns a single GPU server, the tradeoffs are manageable.

Scale can have a significant impact on this equation. Multiple teams sharing physical servers without virtualization will undoubtedly face isolation challenges. A single runaway job can starve neighboring workloads of resources. And without a hypervisor enforcing boundaries, security isolation weakens. In addition, GPU sharing becomes impractical without software-defined partitioning. Overall, many teams find that the infrastructure that accelerated their pilots can become an obstacle to enterprise adoption.

Virtualization, applied thoughtfully, can restore isolation and policy enforcement. While it requires a small performance compromise, virtualization can provide vital operational flexibility for AI workloads at scale. 

Why cloud native virtualization could be a game changer for AI

Cloud native virtualization can address multiple AI infrastructure challenges by unifying VM and container management under Kubernetes. Rather than maintaining separate operational stacks, it allows you to manage both with the same APIs, scheduling logic and policy frameworks.

In the context of AI, this unification specifically helps to reduce the sprawl that accumulates when each project builds its own stack. With cloud native virtualization, you gain the benefits of namespace isolation, role-based access and resource quotas without having to duplicate governance mechanisms. GPU resources can be managed through policy-driven, Kubernetes-aligned patterns rather than manual allocation. The platform can standardize how environments are provisioned when a new model needs hardware. And when a job completes, resources can return to the shared pool.

Even if you built machine learning infrastructure before Kubernetes became dominant, or if your entire software stack has been previously validated for compliance, the case for cloud native virtualization holds. It applies Kubernetes patterns to workloads that cannot yet be containerized. By running those VMs on Kubernetes, you allow them to benefit from the same automation, observability and lifecycle management as containerized services. This consistency often translates to faster provisioning, clearer cost attribution and reduced day-two toil.

How SUSE supports you in running efficient AI workloads

Successfully moving from concept to implementation requires technology built for scale and a long future. SUSE offers solutions that work together to deliver the unification, GPU optimization and flexibility that sustainable AI infrastructure requires.

SUSE Rancher Prime, SUSE Virtualization and SUSE AI

SUSE Rancher Prime is a hybrid IT platform for managing Kubernetes clusters across data centers, clouds and edge locations. It provides a unified control plane with RBAC and policy management, and it easily integrates with observability tooling.

SUSE Virtualization extends Rancher Prime with cloud native virtualization built on Kubernetes. It allows VMs and containers to run side by side, with the same authentication, authorization, and policy frameworks. As a result, you can provision, configure and monitor virtual machines through the same interface that you use for containerized workloads.

SUSE AI, which is purpose-built for AI workloads, further extends SUSE Rancher Prime. It simplifies the deployment and management of generative AI infrastructure while integrating with the broader Rancher ecosystem, which provides observability and governance. 

Together, these solutions help enterprises to build and sustain scalable, governable AI infrastructure.

NVIDIA vGPU support

GPU resources are expensive and can be difficult to optimize. Underutilization wastes budget, while oversubscription blocks progress. SUSE Virtualization supports NVIDIA vGPU technology, enabling multiple virtual machines to share a single physical GPU while still preserving isolation.

With vGPU, platform teams can partition and allocate GPU resources with defined profiles and controls. As a result, training jobs might receive dedicated slices during intensive compute phases while inference services efficiently share the remaining capacity. Each tenant operates within defined boundaries, reducing the risk that one workload will monopolize hardware or degrade neighbors’ performance.

SUSE provides documented guidance for NVIDIA vGPU configuration, so GPU virtualization becomes an operational reality rather than a research project.

All built on an open source foundation

The platform decisions you make today often have a direct impact on your future options. Open technologies can help reduce the risk of brittle dead ends. When the next model architecture emerges, open foundations help ensure that your infrastructure can adapt. 

SUSE intentionally builds its virtualization and AI solutions on open source foundations, enabling valuable flexibility at a time when AI infrastructure continues to rapidly evolve. For example, SUSE Rancher Prime supports any CNCF-certified Kubernetes distribution, giving enterprises choice across deployments.

Real-world success stories of virtualization for AI workloads

Several organizations have already partnered with SUSE to improve visibility, operational efficiency and platform sustainability. By proactively planning for complex workloads, platform consolidation and other growth-based challenges, these groups achieved long-term operating advantages.

FIS Group: simplifying management of complex AI workloads and more

FIS Group faced the challenge of managing AI services while maintaining visibility into resource consumption across teams. Without unified tooling, an unsustainable amount of manual effort was required for tracking token usage, model performance and GPU utilization.

By deploying SUSE’s integrated stack, FIS Group gained the ability to monitor these metrics from a single platform. This visibility enabled better cost accountability, enabling platform teams to more easily identify optimization opportunities. 

While FIS Group prioritized AI-related workload management, the integrated tooling has also reduced operational burdens across the company’s multiple environments.

Switch: sustainably scaling AI innovation

Switch operates some of North America’s largest data centers, and it runs them entirely on renewable energy. As AI workloads surged, Switch needed infrastructure that could meet the growing demand without compromising its sustainability commitments or operational reliability.

Switch also had some other aspirations for its IT. The organization’s prior architecture collected telemetry data only once per hour, which proved insufficient for managing power-intensive AI workloads. Deployments took days, and replication across data centers required separate contracts, interfaces and monitoring tools for storage, networking and compute.

After deploying SUSE Rancher Prime and SUSE Linux Micro, Switch reduced deployment times by up to 98 percent. Telemetry collection increased 360-fold, from hourly to every 10 seconds. In addition, the organization scaled its AI workloads without a corresponding increase in energy consumption.

Swiss National Supercomputing Center: simplifying resource management

The Swiss National Supercomputing Centre (CSCS) operates high-performance computing infrastructure that supports leading scientific research. Two platform engineers support 80-90 engineers on the Kubernetes platform; this ratio would be unfathomable without significant operational efficiency.

SUSE Rancher Prime helped CSCS unify its management of more than 50 Kubernetes clusters, which span 20 VLANs. In addition, SUSE Virtualization helped CSCS unify VM and container management in a hyperconverged stack alongside approximately 300 VMs and 200 bare metal servers. As a result, time spent on infrastructure management has dropped by approximately 80 percent. Application deployments have also accelerated by approximately 70 percent. 

Achieve efficient AI workloads in virtual environments with SUSE

While it’s not always obvious, many enterprises leading in the AI arena are treating infrastructure as an operating model rather than a collection of technology purchases. They are designing for day two — for the upgrades, audits and team changes that inevitably follow deployment. They have prioritized observability because they know that visibility enables effective governance. And they continually choose portable foundations because they know that today’s requirements will evolve.

SUSE shares these commitments. Virtualization for AI workloads, built on Kubernetes and open source, can help you achieve the standardization you need without sacrificing valuable flexibility. 

Ready to move from AI experimentation to platformization? Explore SUSE AI and learn how we can support your AI ambitions.

FAQs about virtualization for AI workloads

What is virtualization for AI workloads?

Virtualization for AI workloads means running AI applications in virtual machines. Some teams manage those virtualized workloads through Kubernetes to improve isolation, sharing and operational consistency, specifically in contrast to bare metal.

What are the benefits of virtualization for AI workloads?

Key benefits of virtualization for AI workloads include improved GPU utilization through shared hardware, operational flexibility for rapid provisioning, unified management via Kubernetes and improved utilization.

What are the common challenges in implementing virtualization for AI workloads?

Common challenges in implementing virtualization for AI workloads include ensuring adequate GPU performance in virtual environments, managing data-intensive workloads, balancing resource sharing with isolation and orchestrating distributed training.

How can SUSE help you with virtualization for AI workloads?

SUSE provides integrated solutions that can help with virtualization for AI workloads. SUSE Virtualization supports GPU-optimized VM management, SUSE Rancher Prime enables Kubernetes orchestration and SUSE AI can help streamline your genAI deployment.