From Bare Metal to GPU Virtualization
and Open-Source Private Cloud
The evolution of the virtualization market has brought a key architectural question back to the center of decision-making: how to build infrastructure capable of absorbing new workloads — AI, simulation, and 3D — while maintaining control over costs, sovereignty, and long-term technology strategy.
In many environments, the answer can no longer be “virtualize everything” nor “keep everything on bare metal.” Organizations must make intelligent trade-offs based on workload characteristics.
Virtualization maximizes resource utilization, isolates environments, and simplifies application deployment and scalability, whereas bare metal delivers optimal raw performance but offers less flexibility and more complex operations.
GPUs: The Turning Point of Modern Infrastructure
AI has transformed GPUs into critical resources. While CPUs can be easily shared, GPUs represent a significant investment and must be used to their full potential. Without fine-grained governance, organizations quickly face:
- GPU underutilization
- Rigid project-based reservations,
- Accelerate deployments
- An inability to prioritize workloads
The objective therefore becomes turning GPUs into shared, on-demand resources.
NVIDIA vGPU: Pooling and Governing GPU Access
NVIDIA vGPU enables the partitioning of a physical GPU into multiple profiles, allowing several virtual machines to share the same card. This makes it possible to:
• increase overall GPU utilization,
• isolate environments,
• secure access to GPU resources,
• offer an internal “GPU as a Service” model.
Historically, VMware provided a widely adopted framework for these use cases. Today, however, organizations are seeking architectures that reduce dependency on a single vendor while maintaining high levels of performance and automation.
Depending on their priorities — performance, control, scalability, or cost — organizations can adopt different infrastructure models.
Possible Infrastructure Models
1) Proprietary alternatives
Hyper-V or Nutanix can address certain constraints, but vendor dependency and total cost of ownership remain key concerns.
2) Public cloud
Useful for burst capacity, proofs of concept, or specific use cases. However, the medium-term cost of GPU usage in the cloud, combined with sovereignty requirements, often makes it necessary to retain a strong on-premises foundation.
3) Industrialized open-source private cloud
This is the most sustainable approach when the goal is to build a controlled, scalable platform aligned with AI workloads.
Why Open Source Is a Strategic Choice (When Properly Operated)
An open-source stack is not just “an alternative hypervisor.” Its value lies in a modular architecture, for example:
• KVM for the compute layer
• OpenStack for cloud orchestration (multi-tenancy, quotas, APIs, automation)
• Ceph for distributed storage
• OVN for virtual networking
• Observability tools such as Prometheus and Grafana
This approach decouples responsibilities, enables evolution without full redesign, and avoids vendor lock-in.
Canonical: Industrializing Open Source for AI and GPUs
The challenge is no longer finding open-source components, but making them enterprise-ready: integration, updates, support, and automation. Canonical addresses this need through Ubuntu and a coherent portfolio.
Approaches from Canonical for Different Use Cases:
Bare metal orchestration for maximum-performance workloads like heavy AI training.
Fast, isolated environments for Linux workloads, testing, and industrialization.
Simplified private cloud for mid-size clusters (3 to 50 nodes).
Full-featured, multi-tenant private cloud designed for GPU pooling and AI industrialization.
Kubernetes: The Application Orchestration Layer
For many AI workloads, Kubernetes is essential – it automates the deployment, scaling, and management of applications such as model training jobs, data processing tasks, and real-time inference services. Canonical offers Canonical Kubernetes, which integrates with open-source infrastructure (OpenStack or bare metal). The most common enterprise model combines:OpenStack (infrastructure) + Kubernetes (AI workloads).
Conclusion
Organizations must rethink virtualization as an architectural decision: balancing bare metal and virtualization, controlling vendor lock-in, ensuring sovereignty, and enabling the industrialization of NVIDIA GPU–accelerated workloads. Along this journey, managed open source – a service that combines open technologies with enterprise grade support, security maintenance, and lifecycle management, delivered by Canonical – provides a practical and reliable foundation for building modern, resilient, and scalable infrastructure.
Provides a practical and reliable foundation for infrastructure
Let’s assess your context and build the right platform together, from bare metal to GPU Virtualization and Open-Source Private Cloud.
