Kubernetes GPU Cluster
GPU workload orchestration on Kubernetes. NVIDIA GPU Operator, Volcano scheduler and container-based HPC workload management.
Kubernetes GPU Cluster is the industry-standard solution for orchestrating AI and HPC workloads in a container-based, scalable and multi-tenant environment. Mevasis installs and configures Kubernetes clusters equipped with NVIDIA GPU Operator and Volcano scheduler end-to-end, from requirements analysis through to production deployment.
By managing GPUs, storage and networking through declarative manifests, Kubernetes gives your team reproducible environments and automated scaling that are hard to achieve with hand-configured clusters. GPU Operator and MIG also let you partition cards flexibly.
Key Features
NVIDIA GPU Operator
The NVIDIA GPU Operator fully automates driver, CUDA toolkit and container runtime integration on every node. This removes the manual, error-prone steps that slow down cluster bring-up. MIG partitioning support lets you slice large GPUs for smaller workloads.
Volcano Scheduler
The Volcano scheduler handles distributed HPC workloads with gang scheduling, preemption and queue management. Gang scheduling ensures all-or-nothing job launches, which is critical for tightly coupled MPI jobs. This delivers fair, efficient resource sharing in multi-tenant clusters.
Multi-Tenant Isolation
GPU quotas for different teams are safely partitioned using Namespace, RBAC and ResourceQuota objects. Each team sees only its own resources and cannot impact others. This makes a shared GPU cluster practical for many departments.
Full-Stack Monitoring
Provides real-time access to GPU temperature, power and utilization metrics via DCGM Exporter, Prometheus and Grafana. Custom dashboards show per-node and per-workload GPU health. Alerting catches thermal or memory issues before they break training jobs.
Frequently Asked Questions
A Kubernetes GPU Cluster should be chosen in environments where multiple teams share the same GPU infrastructure and workloads are container-based. It is ideal for scenarios where CI/CD pipeline integration is expected and centralized monitoring of resource utilization is desired.
Mevasis provides end-to-end service for Kubernetes GPU Cluster deployment, from hardware selection through software configuration. We deploy all components in the field — NVIDIA GPU Operator integration, Volcano scheduler setup, network configuration (InfiniBand or RoCE), multi-tenant namespace isolation and monitoring infrastructure (Prometheus, Grafana). Post-installation management, monitoring and update support are also provided.
Kubernetes GPU Cluster pricing varies by cluster size, GPU model and count, network infrastructure preferences and managed service scope. To receive an organization-specific quote, you can fill in the request form. After the Mevasis team completes a needs analysis, a detailed price proposal will be provided.
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