A Buyer’s Guide to Kubernetes GPU Platforms

How to Evaluate GPU Infrastructure for AI, ML, and Platform Teams

Introduction

GPUs have become one of the most critical and constrained resources in modern infrastructure. As organizations race to adopt AI and machine learning, they quickly discover that the challenge is no longer access to GPUs, but how to operate them at scale.

Most enterprises now support multiple AI and ML teams, different frameworks and runtimes, and a mix of training, inference, experimentation, and CI workloads. Kubernetes has become the standard control plane for these workloads. However, running GPUs efficiently, securely, and flexibly in Kubernetes introduces a new set of platform challenges, especially when multiple teams need to share the same GPU fleet.

This buyer’s guide is designed to help platform and infrastructure leaders evaluate Kubernetes GPU platforms, understand modern architectural patterns, and avoid approaches that lead to GPU sprawl, cluster sprawl, and rising operational cost.

The GPU-Driven Enterprise

GPUs are fundamentally different from traditional compute resources:

• They are expensive and scarce
• They are often underutilized
• They are difficult to share safely
• They are tightly coupled to drivers, runtimes, and Kubernetes components

As AI adoption grows, organizations often respond by creating dedicated GPU clusters per team or per workload. Separate clusters are provisioned for training and inference. Temporary clusters are created for experimentation. Over time, these clusters become long-lived infrastructure that is expensive to operate and difficult to change.

This approach fragments GPU capacity, increases operational overhead, and limits flexibility. Platform teams are left managing an expanding fleet of clusters, each with its own lifecycle, security posture, and tooling.

The core problem is not GPU scheduling. It is how GPU infrastructure is exposed to teams.

What Are Modern GPU Workloads?

Modern GPU workloads in Kubernetes are diverse and fast-moving. They include:

• Model training, batch, distributed, long-running
• Model inference, low-latency, high availability
• Interactive development, notebooks, experiments
• CI and automated testing
• Internal AI platforms serving multiple teams

These workloads differ dramatically in lifecycle, performance profile, and isolation requirements. A platform designed around a single workload type or a static cluster model will quickly become a bottleneck.

Modern GPU platforms must assume workloads are ephemeral, teams move quickly, requirements change frequently, and sharing GPU infrastructure is unavoidable.

Where Many Kubernetes GPU Platforms Fall Short

Many Kubernetes GPU platforms are designed to solve important problems around cluster management, governance, and policy. However, some architectural patterns limit their ability to support multi-tenant GPU workloads at organizational scale.

Cluster-Centric Isolation Models

Platforms that treat the Kubernetes cluster as the primary unit of isolation force organizations to provision a new cluster for every team or workload. While this provides isolation, it leads directly to cluster sprawl, duplicated tooling, fragmented security controls, and inefficient GPU utilization across the organization.

GPU Sharing Without Strong Control Plane Isolation

Some platforms attempt to enable GPU sharing while still relying on a single shared Kubernetes control plane. In these environments, teams share not only infrastructure but also core Kubernetes components, increasing blast radius and making it difficult to safely support different workloads, configurations, or upgrade cycles.

Namespace-Only Multi-Tenancy for GPU Workloads

Namespaces provide logical separation, but they do not offer strong isolation for GPU workloads. Relying on namespaces alone places heavy dependence on policy, quotas, and correct configuration. For GPU workloads, where drivers, runtimes, and performance characteristics matter, this model introduces unnecessary risk.

Shared Node Tenancy for Virtual Clusters

Some platforms offer virtual clusters that run on shared worker nodes using a shared networking and storage stack. While this shared-nodes model may be acceptable for general-purpose workloads, it is not recommended for GPU workloads. GPUs are sensitive to driver versions, runtime configuration, and resource contention, and shared-node tenancy significantly increases blast radius and operational risk.

No Support for Node-Level Isolation Models

Modern GPU platforms must support node-level isolation options, such as private or dynamically assigned nodes per tenant. Without node isolation, organizations are forced to choose between unsafe sharing and inefficient over-provisioning. Platforms that cannot isolate networking and storage at the node level struggle to safely support diverse GPU workloads.

Policy-Heavy Safety Models

Platforms that rely primarily on quotas, admission policies, and manual guardrails to enforce safety tend to scale poorly. As the number of teams grows, platform teams become bottlenecks, responsible for preventing misconfiguration rather than benefiting from architectural isolation.

GPU Utilization Optimized Per Cluster, Not Per Organization

Legacy architectural patterns often optimize GPU utilization within individual clusters while failing to optimize usage across the broader platform. This leads to stranded capacity, where GPUs sit idle in one cluster while demand exists elsewhere.

The Anatomy of a Modern Kubernetes GPU Platform

A modern Kubernetes GPU platform is not defined by a single feature, but by architectural principles that determine how safely GPUs can be shared and how well the platform scales over time.

1. Isolation and Tenancy by Design

Modern GPU platforms must provide strong isolation by architecture, not by convention. Each team should be able to operate in its own Kubernetes control plane without requiring a dedicated physical cluster. Isolation must extend beyond namespaces to include control planes, platform components, and GPU workloads.

Node-level tenancy models are essential for GPU workloads. While shared nodes may work for general-purpose workloads, GPUs often require private or dynamically assigned nodes to isolate drivers, networking, and storage safely.

2. Safe and Efficient GPU Sharing

A modern platform should enable high GPU utilization without increasing blast radius. GPU sharing must be supported by strong isolation mechanisms that prevent one team’s workloads from impacting others.

Visibility into GPU usage, combined with the ability to pause or reclaim idle capacity, is essential to avoiding stranded resources and unnecessary cost.

3. Operational Scalability

As AI adoption grows, the platform must scale operationally. New GPU environments should be provisioned quickly and consistently, and environments should be ephemeral by default.

Platform teams should not be burdened with managing an ever-growing number of long-lived clusters or intervening manually for routine operations.

4. Team Autonomy with Platform Guardrails

AI and ML teams need autonomy to configure and evolve their environments. Platform teams need guardrails that ensure stability, security, and consistency.

A modern platform enables self-service access to GPU resources while enforcing standards through architecture rather than manual review or policy alone.

5. Deployment Flexibility and Longevity

GPU platforms must behave consistently across cloud, on-premises, and hybrid environments. The same isolation, tenancy models, and operational experience should be available everywhere.

This consistency ensures the platform can adapt as GPU strategies and infrastructure evolve.

Buyer Evaluation Checklist: Key Questions to Ask

The following questions help validate whether a Kubernetes GPU platform truly aligns with the principles of a modern GPU platform.

1. Isolation and Tenancy by Design

• Can each team run its own Kubernetes control plane without a dedicated cluster?
• Is isolation enforced by architecture rather than namespaces and policy?
• Does the platform support node-level isolation for GPU workloads?
• Can GPU workloads run on private or dynamically assigned nodes?
• Are networking and storage isolated appropriately?

2. Safe and Efficient GPU Sharing

• Can GPUs be shared across teams without increasing blast radius?
• How does the platform prevent interference between GPU workloads?
• Is GPU utilization optimized across the organization rather than per cluster?
• Can idle GPU capacity be identified and reclaimed?

3. Operational Scalability

• How long does it take to provision a new GPU-enabled environment?
• Can environments be created on demand without platform team intervention?
• Are environments ephemeral by default?
• How many physical clusters must be managed as adoption grows?

4. Team Autonomy with Platform Guardrails

• How much autonomy do teams have to configure and operate their environments?
• Can teams upgrade and experiment without impacting others?
• How are standards enforced without constant platform involvement?

5. Deployment Flexibility and Longevity

• Does the platform behave consistently across cloud, on-premises, and hybrid environments?
• Are the same tenancy and isolation models available everywhere?
• Can the platform evolve as GPU strategies change?
• Platform Approaches: A High-Level Comparison

Broadly, Kubernetes GPU platforms fall into two categories. Many platforms focus on managing clusters and enforcing policy, treating the cluster as the primary unit of isolation. As teams scale, this approach leads to cluster sprawl and growing operational overhead.

An alternative approach is to virtualize Kubernetes itself. By decoupling Kubernetes control planes from the underlying infrastructure, multiple isolated Kubernetes environments can share GPU-enabled nodes while maintaining strong isolation. This architectural difference has significant implications for cost, security, and scalability.

Some platforms can approximate these outcomes through policy and operational effort, but architectural support is often the difference between early success and long-term scalability.

Capabilities Comparison

Why vCluster for GPU Platforms

vCluster is designed around the idea that Kubernetes itself should be the unit of tenancy. By virtualizing Kubernetes control planes, vCluster allows teams to run fully isolated Kubernetes environments on shared GPU infrastructure.

For GPU workloads, vCluster supports private and dynamically assigned node models, ensuring proper isolation at the node, networking, and storage layers. This enables organizations to safely share expensive GPU resources while maintaining flexibility across teams and workloads.

Platform teams retain control over the underlying infrastructure, while teams gain the autonomy they need to move quickly. The result is higher GPU utilization, fewer physical clusters, and a platform that scales with AI adoption.

vCluster is not a GPU scheduler or a traditional cluster management platform. It is a foundational building block for organizations building scalable, multi-tenant GPU platforms on Kubernetes.

Conclusion

Selecting a Kubernetes GPU platform is a long-term architectural decision that directly impacts cost efficiency, security, and developer velocity.

Platforms designed around cluster sprawl, shared-node GPU tenancy, or policy-heavy isolation models struggle to scale as AI adoption grows. In contrast, platforms that provide strong isolation, flexible node-level tenancy, and efficient resource sharing enable organizations to maximize the value of their GPU investments.