Check Dynamo Operator Health
To troubleshoot NVIDIA Dynamo on Amazon EKS, begin by verifying the operator’s deployment status through the EKS control plane and Kubernetes API. Check pod health, logs, and resource utilization to confirm the Dynamo operator is running without errors. Use kubectl commands to inspect the operator’s state and validate that it's properly connected to the EKS cluster and configured with correct service accounts and RBAC policies. Monitor events in the Kubernetes cluster for any failed reconciliations or misconfigurations. Ensure the operator has access to the required NVIDIA NIMs and AWS services like S3 or IAM roles for secure inference. If issues persist, validate network policies, security groups, and IAM permissions that allow Dynamo to communicate with AWS and EKS components.
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Additionally, cross-reference logs from the NVIDIA Dynamo and EKS components to identify any misconfigurations or timeouts. Confirm that the underlying nodes have the necessary GPU drivers and NVIDIA container runtime. Use AWS CloudWatch and EKS logs to track inference performance and error patterns. If the operator fails to initialize, ensure that the correct version of the Dynamo operator is deployed and matches the EKS version. Regular monitoring and alerting on operator health can prevent service degradation and ensure consistent AI inference performance.
Validate EKS GPU Node Pools
When troubleshooting NVIDIA Dynamo on Amazon EKS, begin by confirming that GPU node pools are healthy, schedulable, and running compatible NVIDIA drivers, CUDA toolkits, and container runtime versions. Inspect pod events, node conditions, and Dynamo component logs to identify scheduling failures, image pull errors, or GPU resource mismatches. Validate that requests and limits align with available GPU memory, and check that device plugins expose accelerators correctly. Review EKS networking, security groups, and service endpoints so Dynamo workers can communicate with model storage and inference gateways without latency spikes or connection resets.
Next, correlate application metrics with cluster telemetry using CloudWatch, Prometheus, or NVIDIA DCGM to distinguish model-level bottlenecks from infrastructure issues. Examine autoscaling behavior, queue depth, and token throughput to see whether Dynamo routing or batching is underperforming. Test a minimal inference workload to isolate configuration changes, then compare successful and failed runs. If errors persist, inspect IAM permissions, persistent volume mounts, and node taints, while rolling back recent manifest or driver updates until stable operation returns.
Inspect Inference Endpoint Logs
When troubleshooting NVIDIA Dynamo on Amazon EKS, inspect inference endpoint logs from pods, containers, and ingress controllers to identify request failures, model loading errors, GPU memory pressure, or latency spikes. Correlate logs with Kubernetes events, node conditions, and Dynamo router metrics to distinguish application issues from cluster capacity or networking problems. Check pod restarts, image pull errors, readiness probe failures, and resource limits, then verify Dynamo components, NIM services, and autoscaling policies are healthy. Review CloudWatch or Prometheus dashboards for throughput, token latency, queue depth, and GPU utilization, comparing recent deployments or configuration changes against baseline behavior.
Next, validate the EKS networking path, including service endpoints, load balancer health checks, security groups, and DNS resolution, because Dynamo inference traffic often fails before reaching the model. Inspect node labels, taints, and GPU driver compatibility, then confirm Dynamo workers can access model artifacts and required AWS services. If errors persist, reproduce one request with verbose logging, capture traces, and escalate with timestamps, request IDs, and affected endpoints. This structured approach keeps troubleshooting focused, reduces downtime, and helps teams restore AI inference.
Test Autoscaling and Load Balancing
To troubleshoot NVIDIA Dynamo on Amazon EKS, begin by checking pod health, events, and logs across the Dynamo controller, worker, and router components. Use kubectl describe pod and kubectl logs to identify image pull failures, GPU scheduling issues, readiness probe failures, or permission problems. Verify that nodes have the required NVIDIA drivers, device plugin, and GPU capacity, and confirm that Dynamo's configuration points to valid model storage, endpoints, and autoscaling policies. Inspect metrics for latency, queue depth, and token throughput to distinguish application bottlenecks from cluster resource constraints.
Next, correlate Dynamo logs with EKS control plane and node-level signals, including CloudWatch, Prometheus, or NVIDIA DCGM metrics. Check ingress and service routing, TLS settings, and load balancer health to ensure requests reach healthy workers. Review recent deployment changes, Helm values, and scaling thresholds, then roll back if needed. Test with a small synthetic workload to isolate failures, and validate recovery by watching pod restarts, autoscaling events, and end-to-end inference latency before returning production traffic.
Review Cost and Latency Metrics
When troubleshooting NVIDIA Dynamo on Amazon EKS, begin by checking pod status, events, and logs across the control plane and worker nodes, because scheduling failures or image pull errors often surface there first. Verify that GPU node groups have available capacity, correct instance types, and healthy drivers, then inspect Dynamo service endpoints, ingress rules, and environment variables for mismatched model paths or credentials. Compare observed latency with baseline metrics, and isolate whether delays come from queueing, model loading, or downstream dependencies.
Next, use CloudWatch, Prometheus, and NVIDIA tooling to correlate cost spikes with scaling behavior, cold starts, and inefficient batching. Review autoscaling policies, resource requests, and limits to ensure Dynamo pods are not throttled or evicted. If inference errors persist, test a minimal deployment, validate networking between EKS and AWS services, and roll back recent changes while preserving logs for root-cause analysis.
Dynamo EKS Troubleshooting Comparison
| Symptom | Troubleshooting Action | EKS/NVIDIA Tool |
|---|---|---|
| Pod pending | Check node capacity and GPU availability | kubectl describe pod |
| Inference latency | Inspect Dynamo metrics and HPA scaling | Prometheus/Grafana |
| GPU errors | Review driver and device plugin logs | nvidia-smi/kubectl logs |
| Network failures | Validate service endpoints and ingress | kubectl get svc |