Isolating Workloads in a Multi-Tenant GPU Cluster

In the first part of this series, you explored the progression from basic GPU clusters to production-ready AI factories and how governance and repeatability become essential as demands grow. Today, you'll dive into the practical side of that governance, isolating workloads so that multiple teams and tenants can safely share your GPU clusters.

Cluster sharing and multitenancy are necessary to maximize utilization and keep costs under control. However, without proper isolation, you can get data leaks within your workloads, performance degradation in the infrastructure, and compliance violations for your organization.

What Does Isolation Mean in a GPU Cluster?

Within your shared cluster, you need to be able to execute workloads from multiple tenants or teams without any interference. This interference can take several forms: Insufficient safeguards might allow unrestricted data access between workloads, one task can overload the cluster, causing performance issues in other jobs, and resources can be held up by a single tenant.

To prevent these issues, you need to consider isolation across three key dimensions:

• Control-plane isolation: This allows you to determine which users or teams can schedule workloads or request GPUs.

• Data-plane isolation: This ensures workloads do not directly interact with or compromise each other—for example, preventing one container from accessing another container's GPU memory or user space.

• Resource isolation: This enforces fair and predictable allocation of finite resources, preventing a heavy workload from monopolizing devices and starving other teams' jobs.

For your multitenant GPU environment, implementing isolation across these dimensions gives each tenant a scoped sandbox for their tasks. This is essential for stable operations, especially when working with sensitive models, data sets, and proprietary algorithms.

Security Risks Without Proper Isolation

Performance and governance benefits are not the only reasons to implement isolation within your multitenant environments; without strong isolation, several security risks emerge:

• GPU-level security: Outside of specialized features like Multi-Instance GPUS (MIGs), GPUs generally lack tenant-level memory isolation. As such, they are particularly vulnerable to side-channel attacks and memory leakage as multiple processes may share the same GPU hardware.

• Container-breakout risks: Container breakouts are another vulnerability to watch out for. Misconfigured or permissive containers can result in malicious or buggy workloads escaping their boundaries and compromising GPU resources.

• Lateral movement: Your workloads will often communicate over the same network fabric. This can be used as an exploit, with compromised workloads reaching across and spreading to other tenants' services and critical infrastructure.

• Namespace overreach: Your tenants should be tightly scoped and safe from making cluster-wide changes. However, improperly configured access opens the door for accidental changes or malicious action, both of which can compromise the stability and security of your cluster infrastructure.

Isolation Strategies Across the Stack

As you can see, the risks associated with overly permissive isolation in your multitenant environments are pretty significant. So let's explore some effective isolation strategies that will help mitigate those risks at the orchestration, runtime, and hardware levels, maintaining performance while preventing interference.

Kubernetes RBAC and Namespaces

Role Based Access Control (RBAC) and namespaces are a great start for enabling fine-grained security and access control in your multitenant environments. With RBAC, you can define which users have access to specific cluster resources and set logical boundaries between teams or tenants with namespaces.

These features work well together. For example, you can create `dev` and `datascience` namespaces and developer-specific roles (*eg* `gpu-user` or `admin`) under these namespaces. Workloads and resources within a namespace are generally access-restricted outside of that namespace. Any user or group of users that need access to namespace-scoped resources will be granted the RoleBinding for their needs. This process provides a clear governance model for how teams create, use, and manage resources within the cluster.

For your GPU clusters, you can restrict GPU access to certain teams or individual users and ensure GPU workloads stay scoped to their own namespace environment. You can use tools such as OPA Gatekeeper and Kyverno to set RBAC policies at scale within your organization, ensuring rules are efficiently created and automatically enforced.

Pod Security Standards / PodSecurityAdmission

The default Kubernetes environment prioritizes flexibility and ease of use. Workload objects, such as deployments and jobs, can create pods and pod containers that have root access and privileges across the cluster. This is not ideal for your multitenant environment and should be managed by setting Pod Security Standards.

You can use the Pod Security Admission Controller (replacing the deprecated Pod Security Policies in v1.25+ Kubernetes environments) to enforce Pod Security Standards (Privileged, Baseline, and Restricted) at the namespace level. This enables you to utilize your namespace governance model to restrict pod access from host systems. For your multitenant GPU cluster, you want to reduce the blast radius of any compromises and ensure the host infrastructure is protected.

Start by limiting `Privileged` container access as it provides users with allow-by-default privileges within the host system. Set up your workload processes to run in containers as a nonroot user to ensure container-level isolation. Disable the ability to make writes to the root file system, preventing tampering with binaries inside the pod. Also, enabling seccomp profiles restricts the system calls available to a workload, cutting down the attack surface.

Network Policies

By default, your pods are allowed to communicate collectively, with unencrypted network traffic across the cluster. In multitenant environments, you should restrict this behavior using network policies to manage pod-to-pod traffic using namespace labels and IP address ranges.

For your multitenant GPU cluster, it's recommended that you set up a policy that restricts all traffic, then explicitly set up other policies to enable the necessary connections. For example, with your teams' workloads in scoped namespaces, you can implement a policy only allowing traffic within a namespace, ensuring one team's pods do not interact with another. Inbound and outbound connections should be equally considered for isolation, with IP blocks restricting communication to known IP ranges. Egress restrictions would help secure sensitive data, models, or proprietary results.

Third-party network policy providers, such as Calico and Cilium, also offer a host of security and traffic-control features to more efficiently enforce your policies at scale. For example, ingress and egress gateways can help you isolate workloads at the network layer, restricting inbound and outbound connections.

vCluster for Control Plane Isolation

vCluster gives your teams or tenants a virtual control plane to deploy and manage workloads and resources as if they had their own dedicated virtual Kubernetes cluster. Regular shared clusters apply objects such as CustomResourceDefinitions (CRDs) and webhooks globally, leaving you open to multitenant conflicts. In contrast, each virtual cluster has its own isolated control plane, API server, and CRDs. This is particularly important in GPU-heavy environments where custom operators, such as the NVIDIA GPU Operator, are regularly deployed with their attached CRDs.

The platform uses your cluster infrastructure and integrates with underlying Kubernetes security and resource management features. This ensures you can set namespace-level quotas, GPU-allocation policies, and even employ node-level selection for your virtual clusters. There is a wide range of tenancy models suitable for your GPU use cases, such as dedicated and private nodes. With these options, you can get a flexible assignment of dedicated GPU nodes using the Kubernetes node selector or fully isolated virtual clusters backed by private GPU nodes. All approaches provide your ML developer teams with strong isolation for their models and training workflows within your clusters.

Resource Quotas and Limits

Kubernetes resource quotas and limits let you manage resource usage across CPU, memory, and extended resources, including the GPU. By setting up a quota for each namespace, you can specify the number of GPUs that can be requested in a namespace.

Declaratively assign GPUs to namespaces using resource quotas, and set resource limits at the pod/container level to enforce the maximum GPU consumption for individual workloads. This prevents one team or tenant from monopolizing your cluster's GPU pool and ensures fair access across groups. With an object count quota, you can also limit the number of pods, jobs, or compute resources launched concurrently. This restricts tenants from flooding the cluster with GPU-bound workloads, which cause scheduling backlogs and degraded performance for others.

MIG and Time-Slicing for Hardware Isolation

MIG enables a single physical GPU to be securely partitioned into multiple isolated instances for optimal GPU utilization. Each instance gets its own dedicated compute and memory resources at the hardware level, preventing memory snooping or cross-workload interference.

Time-slicing your GPUs divides GPU access into time intervals and schedules workloads in turns, allowing for intermittent usage. However, this method offers a softer isolation with no memory or fault-isolation assurances, so tenant workloads can affect one another through memory contention or delayed scheduling.

By combining these methods, you can balance performance with cost-efficiency. MIG ensures hard isolation between tenants and teams, while time slicing your MIG partitions enables fine-grained sharing among workloads within the same team or tenant.

Operational Best Practices

You have explored the different isolation levels and available strategies to implement multitenancy in your GPU clusters. Now, let's run through some best practices to help you avoid common performance, security, and compliance pitfalls that can occur within your production environments.

• No privileged pods: Privileged pods can bypass many isolation controls and directly interact with host-level resources. Privilege should be granted only when necessary for tightly scoped use cases, with clear audit trails and monitoring.

• Hardened images: Compromised images are a very common vulnerability in containerized systems. Always base your workloads on minimal, well-maintained images with a proven security profile. You can integrate image scanning tools (such as Trivy) into your infrastructure to detect vulnerabilities early.

• Observability: You need visibility into your cluster operations, especially around GPU usage in the cluster. NVIDIA's DCGM (Data Center GPU Manager) and Prometheus are good options for GPU health monitoring and system alerts.

• Policy enforcement: Ensuring optimal multitenant operations across your Kubernetes cluster involves a host of policies (security, networking, etc.). Third-party options, like OPA Gatekeeper and Kyverno, can help you enforce these rules across your cluster's resources without relying on manual processes.

• Dedicated nodes: You don't need general-purpose workloads clogging up your GPU schedules in your multitenant GPU cluster. Use Kubernetes taints and tolerations to enforce dedicated node pools for your GPU workloads, reducing the risk of interference and optimizing GPU utilization.

Conclusion

Sharing GPU access across your teams is often the smart move for getting the most out of your hardware. But shared GPU environments come with their own headaches around performance and security.

This article walked through practical ways to tackle these challenges by properly isolating workloads in your multitenant GPU cluster. When you combine this with the AI-factory groundwork we covered in [part one](URL), you've got everything you need to turn basic GPU clusters into secure, scalable platforms that actually work in production while keeping costs in check.

That wraps up this two-part series on building production-ready AI infrastructure and managing GPU resources at scale.

Ready to roll out stable, secure GPU operations across your organization? Check out vCluster for solutions that help you squeeze every bit of value from your GPUs while also guaranteeing tenant isolation.