GPU Kubernetes Consulting
Running GPU workloads on Kubernetes requires specialized expertise that goes far beyond standard container orchestration. We help teams design, deploy, and operate production GPU clusters on Kubernetes -- whether on EKS, GKE, AKS, or bare metal.
The Challenge of GPUs on Kubernetes
Kubernetes was designed for stateless web services, not GPU-intensive AI workloads. While the Kubernetes ecosystem has evolved to support GPUs, getting it right requires deep understanding of device plugins, resource scheduling, network configuration, and storage systems that most platform teams simply do not have. The result is GPU clusters running at 20-30% utilization, teams waiting days for GPU access, and training jobs failing due to misconfigured infrastructure.
The core challenges include GPU resource fragmentation where expensive accelerators sit idle while other teams queue for access, lack of proper scheduling that understands GPU topology and affinity, missing observability into GPU health and utilization, networking that does not support the RDMA requirements of distributed training, and storage systems that cannot feed data fast enough to keep GPUs busy. Solving these requires a purpose-built approach to Kubernetes GPU infrastructure.
What We Deliver
GPU Operator and Device Plugin Setup
The NVIDIA GPU Operator automates the management of GPU drivers, container runtime, device plugins, and monitoring components on Kubernetes. We deploy and configure the full GPU Operator stack including driver containers for consistent driver versions across nodes, the NVIDIA Container Toolkit for GPU access in containers, the Kubernetes Device Plugin for GPU resource advertising, DCGM Exporter for GPU metrics in Prometheus, and GPU Feature Discovery for topology-aware scheduling labels. We handle the edge cases that cause failures in production: driver version conflicts, containerd vs CRI-O runtime differences, secure boot compatibility, and upgrade strategies that avoid disrupting running workloads.
GPU Scheduling with KAI Scheduler
The default Kubernetes scheduler treats GPUs as simple integer resources with no understanding of topology, affinity, or fairness. For multi-tenant GPU clusters, you need a scheduler that understands GPU-specific requirements. We implement advanced GPU scheduling using KAI Scheduler or similar solutions that provide topology-aware placement ensuring GPUs within a job are on the same NVLink domain, fair-share scheduling across teams with guaranteed minimums and burst capacity, gang scheduling for distributed training jobs that require all-or-nothing allocation, preemption policies that allow high-priority jobs to reclaim resources from lower-priority workloads, and queue management with priority classes and resource quotas per namespace or team.
MIG and Fractional GPU Sharing
Not every workload needs a full GPU. Development, testing, inference serving, and data preprocessing can often run on a fraction of a GPU. We configure Multi-Instance GPU (MIG) on A100 and H100 GPUs to partition a single GPU into isolated instances with dedicated compute, memory, and memory bandwidth. We also set up time-slicing for GPUs that do not support MIG, enabling multiple workloads to share a GPU with configurable time quotas. The right partitioning strategy depends on your workload mix. We analyze your actual GPU usage patterns to recommend the optimal MIG profiles and sharing policies that maximize utilization without impacting performance-sensitive workloads.
EKS, GKE, and Bare-Metal GPU Clusters
Each deployment environment has its own set of challenges and best practices. On Amazon EKS, we configure GPU node groups with proper AMIs, EFA networking for distributed training, and integration with FSx for Lustre or S3 for training data. On Google GKE, we set up GPU node pools with the correct machine types, configure multi-networking for RDMA, and integrate with GCS and Filestore. For bare-metal deployments, we handle everything from the OS and driver installation through the full Kubernetes stack including Calico or Cilium networking, MetalLB for load balancing, and Rook-Ceph or similar for storage. Bare metal provides the best performance for GPU workloads, especially when combined with RDMA networking and GPUDirect storage.
Multi-Tenancy and Resource Management
Running multiple teams on a shared GPU cluster requires proper isolation and resource management. We implement namespace-based tenancy with GPU resource quotas, RBAC policies that restrict GPU access to authorized teams, network policies for workload isolation, priority classes that ensure production inference workloads are never preempted by development jobs, and cost allocation through GPU usage tracking and chargeback reporting. This enables organizations to consolidate GPU resources into a shared pool that delivers higher utilization and lower costs than dedicated per-team clusters.
Training Job Orchestration
Running distributed training jobs on Kubernetes requires orchestration beyond what standard Kubernetes controllers provide. We set up and configure the Kubeflow Training Operator for PyTorchJob, TFJob, and MPIJob resources, Volcano batch scheduler for gang scheduling and queue management, custom job controllers for organization-specific workflows, and integration with experiment tracking systems like MLflow and Weights & Biases. We ensure that distributed training jobs are properly configured with the correct number of workers, appropriate resource requests, RDMA-capable network interfaces, and shared storage volumes for checkpointing.
Technologies We Work With
Related Resources
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Whether you are building a new GPU cluster or optimizing an existing one, we can help you get Kubernetes GPU infrastructure right the first time.
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