The convergence of Kubernetes and GPU computing has fundamentally transformed how organizations deploy and scale artificial intelligence, machine learning, and data science workloads. As GPU-accelerated applications become increasingly mainstream, understanding why Kubernetes excels at orchestrating GPU resources is essential for modern infrastructure teams.
The Evolution of GPU Support in Kubernetes
Kubernetes revolutionized container orchestration when it introduced native support for GPUs as extended resources. This capability transforms GPUs from isolated hardware components into schedulable, manageable resources that integrate seamlessly with the Kubernetes resource model. Unlike traditional CPU and memory resources that Kubernetes manages natively, GPUs require specialized device plugins that act as bridges between the Kubernetes API and the underlying hardware.
The device plugin framework enables Kubernetes to discover, advertise, and allocate GPUs to containerized workloads without requiring changes to the core Kubernetes codebase. This extensibility is fundamental to why Kubernetes runs better on GPUs compared to traditional orchestration approaches—it treats GPUs as first-class resources while maintaining the flexibility to support evolving hardware capabilities.
NVIDIA’s Pivotal Role in GPU-Aware Kubernetes
NVIDIA has emerged as the driving force behind GPU acceleration in Kubernetes environments. The company’s comprehensive software stack addresses the unique challenges of running GPU workloads in containerized environments, from driver management to resource scheduling and monitoring.
The NVIDIA Device Plugin Architecture
At the heart of NVIDIA’s Kubernetes integration is the device plugin, which implements the Kubernetes device plugin API to expose NVIDIA GPUs to the cluster. When deployed as a DaemonSet across GPU nodes, the device plugin performs several critical functions:
apiVersion: apps/v1 kind: DaemonSet metadata: name: nvidia-device-plugin-daemonset namespace: kube-system spec: selector: matchLabels: name: nvidia-device-plugin-ds template: metadata: labels: name: nvidia-device-plugin-ds spec: tolerations: - key: nvidia.com/gpu operator: Exists effect: NoSchedule containers: - image: nvcr.io/nvidia/k8s-device-plugin:v0.14.0 name: nvidia-device-plugin-ctr securityContext: allowPrivilegeEscalation: false capabilities: drop: ["ALL"] volumeMounts: - name: device-plugin mountPath: /var/lib/kubelet/device-plugins volumes: - name: device-plugin hostPath: path: /var/lib/kubelet/device-pluginsThis configuration demonstrates how the device plugin integrates with the kubelet on each node, automatically discovering GPUs and registering them as allocatable resources. The plugin continuously monitors GPU health and updates the node’s capacity information, enabling the Kubernetes scheduler to make informed placement decisions.
Key Features That Make Kubernetes GPU-Aware
GPU Resource Specification and Heterogeneous Clusters
One of the most powerful features of Kubernetes on NVIDIA GPUs is the ability to specify detailed GPU attributes in pod specifications. This capability is crucial for heterogeneous clusters where different nodes may contain various GPU models with different capabilities.
apiVersion: v1 kind: Pod metadata: name: gpu-training-workload spec: containers: - name: training-container image: nvcr.io/nvidia/pytorch:23.10-py3 resources: limits: nvidia.com/gpu: 2 nodeSelector: nvidia.com/gpu.product: NVIDIA-A100-SXM4-40GB nvidia.com/gpu.memory: "40960" tolerations: - key: nvidia.com/gpu operator: Exists effect: NoScheduleThis specification requests two A100 GPUs with 40GB memory, demonstrating how Kubernetes enables precise hardware targeting. The scheduler evaluates these requirements against available node resources, ensuring workloads land on nodes with appropriate GPU capabilities. This granular control prevents common issues like deploying memory-intensive models on GPUs with insufficient VRAM.
Integrated GPU Monitoring with NVIDIA DCGM
NVIDIA Data Center GPU Manager (DCGM) provides the telemetry foundation for GPU-aware Kubernetes clusters. DCGM collects comprehensive metrics including GPU utilization, memory usage, temperature, power consumption, and error rates. When integrated with Prometheus and Grafana, DCGM creates a complete observability stack for GPU resources.
Deploy DCGM as a DaemonSet to collect metrics across all GPU nodes:
apiVersion: apps/v1 kind: DaemonSet metadata: name: dcgm-exporter namespace: gpu-monitoring spec: selector: matchLabels: app: dcgm-exporter template: metadata: labels: app: dcgm-exporter spec: containers: - name: dcgm-exporter image: nvcr.io/nvidia/k8s/dcgm-exporter:3.1.7-3.1.4-ubuntu22.04 ports: - name: metrics containerPort: 9400 env: - name: DCGM_EXPORTER_LISTEN value: ":9400" - name: DCGM_EXPORTER_KUBERNETES value: "true" securityContext: runAsNonRoot: false runAsUser: 0 capabilities: add: - SYS_ADMIN volumeMounts: - name: pod-gpu-resources readOnly: true mountPath: /var/lib/kubelet/pod-resources volumes: - name: pod-gpu-resources hostPath: path: /var/lib/kubelet/pod-resourcesDCGM metrics expose critical insights that inform optimization decisions. For example, if GPU utilization remains consistently below 70% while memory usage approaches capacity, you might benefit from batch size adjustments or model optimization rather than additional GPU resources.
Multi-Runtime Support: Docker, containerd, and CRI-O
Kubernetes on NVIDIA GPUs supports multiple container runtimes, providing flexibility for different deployment scenarios. While Docker pioneered containerization, containerd and CRI-O offer streamlined alternatives that align more closely with Kubernetes’ Container Runtime Interface (CRI).
The NVIDIA Container Toolkit integrates with all major container runtimes, ensuring consistent GPU access regardless of the underlying runtime choice. This compatibility is achieved through the NVIDIA Container Runtime hook, which modifies container configurations to inject GPU devices and drivers:
# /etc/nvidia-container-runtime/config.toml [nvidia-container-runtime] debug = "/var/log/nvidia-container-runtime.log" [nvidia-container-cli] debug = "/var/log/nvidia-container-cli.log" environment = [] ldconfig = "/sbin/ldconfig" load-kmods = true no-cgroups = false user = "root:video"This configuration ensures that containers receive proper GPU access regardless of whether you’re using Docker, containerd, or CRI-O as your runtime.
Official Support on NVIDIA DGX Systems
NVIDIA DGX systems represent purpose-built infrastructure for AI workloads, and their official support for Kubernetes eliminates deployment uncertainty. DGX systems come pre-validated with Kubernetes configurations, GPU Operator installations, and optimized networking setups.
A DGX A100 system contains eight A100 GPUs interconnected with NVLink and NVSwitch, providing 600GB/s of GPU-to-GPU bandwidth. When running Kubernetes on DGX, this high-speed interconnect enables efficient distributed training and multi-GPU inference workloads. The pre-configured software stack includes CUDA drivers, NCCL libraries for collective communication, and validated container images from NVIDIA’s NGC registry.
The NVIDIA EGX Stack: Cloud-Native AI at Scale
The NVIDIA EGX stack extends Kubernetes’ GPU capabilities with a comprehensive software platform designed for edge-to-cloud AI deployments. EGX combines the NVIDIA GPU Operator, network operator, and security components into a cohesive stack that simplifies GPU cluster management.
GPU Operator: Automated GPU Software Management
The GPU Operator represents a paradigm shift in how GPU software is managed on Kubernetes. Rather than manually installing drivers, CUDA toolkits, and container runtimes on each node, the GPU Operator deploys these components as Kubernetes resources:
apiVersion: nvidia.com/v1 kind: ClusterPolicy metadata: name: cluster-policy spec: operator: defaultRuntime: containerd driver: enabled: true version: "525.85.12" toolkit: enabled: true version: v1.13.1-ubuntu20.04 devicePlugin: enabled: true version: v0.14.0 dcgm: enabled: true dcgmExporter: enabled: true serviceMonitor: enabled: true gfd: enabled: true migManager: enabled: true nodeStatusExporter: enabled: trueThis ClusterPolicy defines the desired state for GPU software across the entire cluster. The GPU Operator reconciles this state by deploying appropriate DaemonSets and managing component lifecycles. When you need to upgrade drivers, simply update the ClusterPolicy specification—the operator handles rolling updates while minimizing workload disruptions.
Rapid Container Deployment with EGX
EGX enables organizations to deploy AI containers in minutes rather than days. The stack includes pre-configured Helm charts, validated container images, and automated deployment workflows. For example, deploying a complete inference serving stack:
# Add NVIDIA Helm repository helm repo add nvidia https://helm.ngc.nvidia.com/nvidia helm repo update # Install GPU Operator helm install gpu-operator nvidia/gpu-operator \ --namespace gpu-operator \ --create-namespace \ --wait # Deploy Triton Inference Server helm install triton nvidia/triton-inference-server \ --namespace triton \ --create-namespace \ --set image.pullPolicy=IfNotPresent \ --set replicas=3This workflow bootstraps a production-ready inference environment in minutes, demonstrating how EGX streamlines GPU-accelerated application deployment.
NVIDIA Triton: Abstracting Hardware from Software
While Kubernetes excels at resource management and orchestration, it doesn’t solve the complete problem of making GPUs easy to use for AI applications. This is where NVIDIA Triton Inference Server becomes essential.
The Kubernetes and Triton Symbiosis
Triton provides a standardized inference serving layer that abstracts model deployment complexities from hardware specifics. When running in Kubernetes, the architecture creates clear separation of concerns: Kubernetes orchestrates cluster-level resources while Triton manages node-level GPU optimization.
apiVersion: apps/v1 kind: Deployment metadata: name: triton-inference-server spec: replicas: 3 selector: matchLabels: app: triton-server template: metadata: labels: app: triton-server spec: containers: - name: triton image: nvcr.io/nvidia/tritonserver:23.10-py3 args: - tritonserver - --model-repository=s3://my-models/repository - --strict-model-config=false - --log-verbose=1 resources: limits: nvidia.com/gpu: 1 ports: - containerPort: 8000 name: http - containerPort: 8001 name: grpc - containerPort: 8002 name: metrics livenessProbe: httpGet: path: /v2/health/live port: http initialDelaySeconds: 30 periodSeconds: 10 readinessProbe: httpGet: path: /v2/health/ready port: http initialDelaySeconds: 30 periodSeconds: 10This deployment configuration showcases how Triton integrates with Kubernetes patterns. The liveness and readiness probes ensure that only healthy Triton instances receive traffic, while the metrics endpoint exposes inference performance data to Prometheus.
Multi-Framework Support and Dynamic Batching
Triton supports TensorFlow, PyTorch, ONNX, TensorRT, and custom backends, enabling teams to deploy models regardless of training framework. This flexibility is critical in heterogeneous environments where different teams use different ML frameworks.
Dynamic batching is one of Triton’s most powerful features for GPU utilization. By automatically combining individual inference requests into batches, Triton maximizes GPU throughput:
{ "name": "resnet50", "platform": "tensorrt_plan", "max_batch_size": 32, "input": [ { "name": "input", "data_type": "TYPE_FP32", "dims": [3, 224, 224] } ], "output": [ { "name": "output", "data_type": "TYPE_FP32", "dims": [1000] } ], "dynamic_batching": { "preferred_batch_size": [8, 16, 32], "max_queue_delay_microseconds": 500 } }This configuration allows Triton to wait up to 500 microseconds to accumulate requests into preferred batch sizes, significantly improving GPU utilization without excessive latency impact.
Multi-Instance GPU: The Hardware Evolution for Kubernetes
Understanding MIG Architecture
Multi-Instance GPU (MIG) represents a fundamental shift in GPU architecture that aligns perfectly with Kubernetes’ resource model. Available on NVIDIA A100, A30, and H100 GPUs, MIG partitions a single physical GPU into up to seven independent instances, each with dedicated memory and compute resources.
MIG addresses a critical limitation in traditional GPU allocation: the all-or-nothing resource model. Prior to MIG, a Kubernetes pod requesting a GPU received exclusive access to the entire device, even if the workload only utilized a fraction of available compute. This led to significant underutilization in inference and development scenarios.
MIG Profiles and Kubernetes Integration
MIG instances are defined by profiles that specify compute and memory allocations. The A100 supports several standard profiles:
- 1g.5gb: 1/7 of compute, 5GB memory
- 2g.10gb: 2/7 of compute, 10GB memory
- 3g.20gb: 3/7 of compute, 20GB memory
- 4g.20gb: 4/7 of compute, 20GB memory
- 7g.40gb: Full GPU, 40GB memory
Configure MIG through the GPU Operator’s ClusterPolicy:
apiVersion: nvidia.com/v1 kind: ClusterPolicy metadata: name: cluster-policy spec: migManager: enabled: true config: name: default-mig-parted-config --- apiVersion: v1 kind: ConfigMap metadata: name: default-mig-parted-config namespace: gpu-operator data: config.yaml: | version: v1 mig-configs: all-1g.5gb: - devices: all mig-enabled: true mig-devices: "1g.5gb": 7 mixed-profile: - devices: [0,1,2,3] mig-enabled: true mig-devices: "1g.5gb": 7 - devices: [4,5,6,7] mig-enabled: falseThis configuration creates a mixed-mode cluster where some GPUs are partitioned into seven 1g.5gb instances while others remain as full GPUs for large training workloads.
MIG Resource Allocation in Pods
When MIG is configured, the device plugin exposes MIG instances as separate resource types. Pods request specific MIG profiles rather than full GPUs:
apiVersion: v1 kind: Pod metadata: name: inference-pod-small spec: containers: - name: inference image: inference-server:latest resources: limits: nvidia.com/mig-1g.5gb: 1 --- apiVersion: v1 kind: Pod metadata: name: inference-pod-medium spec: containers: - name: inference image: inference-server:latest resources: limits: nvidia.com/mig-3g.20gb: 1This granular resource allocation enables optimal GPU utilization. A single A100 can now serve seven small inference workloads or a combination of workloads with different resource requirements, dramatically improving resource efficiency compared to full-GPU allocation.
The Impact on Cluster Economics
MIG fundamentally changes the economics of GPU clusters. Consider a Kubernetes cluster with eight DGX A100 systems, each containing eight GPUs. Without MIG, this cluster supports 64 concurrent GPU workloads. With MIG configured for 1g.5gb instances, the same hardware supports 448 concurrent workloads.
For inference serving at scale, this represents a 7x improvement in resource efficiency. Organizations can serve more models, handle higher request volumes, and reduce infrastructure costs simultaneously. The linear scaling that MIG enables transforms how teams think about GPU resource allocation.
The Democratization of GPU-Accelerated Computing
As AI transitions from experimental to production-scale deployments, the combination of Kubernetes and modern GPU technologies is democratizing access to accelerated computing. The “GPU-accelerated as a fast button” vision is becoming reality—developers can leverage GPU acceleration without deep expertise in GPU programming or hardware management.
The Role of Abstraction Layers
Kubernetes provides the orchestration substrate while technologies like Triton and MIG handle the complexity of GPU utilization. This layered approach enables several important capabilities:
Workload Portability: Applications can move between different GPU types and cloud providers without code changes. Kubernetes abstracts the underlying hardware while Triton handles model-specific optimizations.
Resource Efficiency: MIG and dynamic batching ensure that expensive GPU resources are fully utilized. Kubernetes’ scheduling algorithms optimize placement while monitoring tools identify optimization opportunities.
Operational Simplicity: The GPU Operator automates software lifecycle management. Developers focus on application logic rather than driver versions, CUDA compatibility, or container runtime configurations.
Real-World Deployment Patterns
Modern GPU-accelerated applications on Kubernetes follow several common patterns:
Inference Serving: Deploy Triton with horizontal pod autoscaling based on request queue depth. Use MIG instances for cost-effective serving of multiple models on shared hardware.
apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: triton-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: triton-inference-server minReplicas: 3 maxReplicas: 20 metrics: - type: Pods pods: metric: name: nv_inference_queue_duration_us target: type: AverageValue averageValue: "50000"Training Workflows: Use full GPUs or large MIG instances for training. Leverage Jobs and CronJobs for batch training workflows with automatic cleanup.
Development Environments: Provide developers with on-demand GPU access through namespace quotas and MIG instances. Enable experimentation without monopolizing full GPUs.
Looking Forward: The GPU-Accelerated Future
The convergence of Kubernetes and GPU computing continues to evolve rapidly. Emerging trends include:
Multi-Cloud GPU Orchestration: Kubernetes enables consistent GPU workload deployment across AWS, GCP, Azure, and on-premises infrastructure. Organizations can optimize for cost, performance, or data locality without rewriting applications.
Edge AI with GPUs: The EGX stack extends GPU-accelerated computing to edge locations. Kubernetes orchestrates distributed AI workloads from cloud to edge, enabling real-time inference at data sources.
Confidential Computing: NVIDIA’s Confidential Computing technology combined with Kubernetes secrets management enables secure AI processing for sensitive data.
Sustainable Computing: GPU monitoring and right-sizing tools help organizations minimize power consumption and carbon footprint. Kubernetes’ scheduling capabilities can optimize for energy efficiency alongside performance.
Conclusion: Why Kubernetes and GPUs Belong Together
Kubernetes runs better on GPUs because it provides the orchestration, scheduling, and resource management foundation that GPU workloads require at scale. NVIDIA’s comprehensive software stack—from the device plugin and GPU Operator to Triton and MIG—builds on this foundation to create a complete platform for GPU-accelerated computing.
The combination enables organizations to:
- Deploy GPU workloads with the same patterns and tools as CPU workloads
- Optimize resource utilization through fine-grained allocation and sharing
- Monitor and troubleshoot GPU resources using familiar Kubernetes tools
- Scale GPU infrastructure from single nodes to multi-cloud deployments
As AI becomes increasingly central to business operations, the Kubernetes + GPU platform provides the scalability, reliability, and efficiency required for production AI deployments. The technologies discussed here—from device plugins to MIG—represent an evolving ecosystem that continues to make GPU-accelerated computing more accessible and efficient.
