Remove cross-tenant workloads from GPU worker nodes. vMetal supplies machines, and vCluster assigns dedicated Private Nodes while operators manage remaining network and storage contention.
Shared GPU worker nodes expose tenants to competition for compute, memory, network, and storage resources.
Namespace boundaries do not stop workloads on the same worker node from competing for GPU, CPU, memory, network, or storage resources.
A separate Kubernetes management stack for every tenant increases infrastructure and operational work.
GPU drivers allocate devices and partitions; tenant boundaries assign workers. Time-slicing does not guarantee performance isolation, and fabric contention needs separate controls.
Private Nodes remove cross-tenant workload placement from assigned GPU workers. vMetal provisions capacity and vCluster separates tenant control planes; GPU allocation, network bandwidth and storage contention need their own controls. vCluster Labs serves 50+ GPU clouds & Fortune 500s.
Address noisy neighbor GPU Kubernetes issues with dedicated Private Nodes, separate tenant control planes, and optional runtime and network isolation.
Private Nodes dedicate GPU worker nodes to one tenant, removing other tenants’ workloads from those workers. Tenant workloads can still contend internally, and network fabrics or storage may remain common; configure those performance boundaries separately.
vNode uses Linux user namespaces and seccomp to restrict workload privileges and system calls. This strengthens runtime boundaries alongside Private Nodes; it does not allocate GPU capacity or eliminate contention within a tenant’s workers.
Each tenant receives its own virtualized API server and RBAC boundary on the control plane cluster.
Tenant-owned CNI configuration and supported Netris integrations provide separate network controls for cross-tenant worker contention. Ethernet segmentation can use VLANs, VRFs and ACLs; InfiniBand isolation uses fabric partitions rather than Ethernet constructs.
Private Nodes define worker ownership, while runtime policies restrict workload privileges. GPU drivers allocate devices within each tenant; network and storage policies manage remaining contention. Treat these as complementary boundaries rather than one isolation or performance guarantee.
This isn’t a side project. Behind every vCluster deployment is 5+ years of deep K8s engineering, security hardening, and battle-tested infrastructure work at massive scale.
Talk to our team about your stack
Deploy vCluster on your infra in minutes
Go live with a hyperscaler-grade tenant experience in days
Noisy neighbor problems arise when workloads compete for GPU execution, memory, CPU, network or storage capacity. Kubernetes namespaces alone do not dedicate workers. Private Nodes remove cross-tenant workload placement from assigned workers, but workloads within one tenant can still contend, and network or storage infrastructure may remain common. Device allocation and fabric performance need their own policies.
Private Nodes are worker nodes joined to one tenant cluster, with that tenant's own CNI and storage configuration. They can be physical machines or supported VMs. Use dedicated bare metal when the service requires exclusive physical servers. vMetal can provision the underlying machines, while platform policies define which capacity a tenant may request and how it is released.
vCluster gives each tenant an independent Kubernetes API, data store and RBAC scope. Private Nodes dedicate worker capacity to that tenant, including its own CNI and storage configuration. vMetal orchestrates the machines underneath. This separates tenant administration and worker placement; shared infrastructure, application permissions and network policies still require an explicit security design.
vNode uses Linux user namespaces and seccomp to restrict workload privileges and system calls, helping prevent container breakout. It uses the underlying Linux kernel rather than a separate guest kernel. Add it as runtime hardening alongside dedicated Private Nodes, patching and least-privilege policies. Validate kernel, container runtime, GPU access and workload compatibility; it is not a replacement for GPU allocation or application authorization.
vCluster Labs software powers 100K+ GPUs and 1M+ CPUs, serving 50+ GPU clouds & Fortune 500s combined. In production, Lintasarta operates 170+ tenant clusters, while Boost Run completed its managed Kubernetes launch in under 45 days from the decision. These named deployments provide scale and launch examples for teams evaluating the platform for their own infrastructure.
For customer-controlled production GPU workloads, start with Private Nodes and an independent tenant control plane. Use dedicated bare metal when exclusive physical servers are required. Add runtime hardening and fabric segmentation according to the threat model, and configure GPU allocation within each tenant. Untrusted workloads also require patching, access controls and tested monitoring and recovery procedures.
See how Private Nodes address noisy neighbor GPU Kubernetes contention for production tenants.
