What you'll learn
Curriculum
8 modules · 25 lessonsGPU Fundamentals for K8s Engineers
Understand how GPUs differ from CPUs, the NVIDIA driver stack, and GPU memory, the foundation for everything else.
Device Plugin vs GPU Operator
Two approaches to GPU management on Kubernetes. Learn when to use each and how to migrate between them.
MIG Partitioning in Production
Partition expensive GPUs into isolated slices for multi-tenant workloads. Profiles, configuration, and production gotchas.
Scheduling & Resource Management
Dedicated GPU node pools, taints, tolerations, and priority classes for GPU workloads.
LLM Serving with vLLM
Deploy vLLM on Kubernetes end-to-end: model loading, memory tuning, and autoscaling with HPA.
Multi-Model Serving & Routing
Use LiteLLM as a gateway for routing requests across multiple models with fallback strategies.
Monitoring, Debugging & War Stories
DCGM + Prometheus + Grafana for GPU monitoring, OOM debugging, and real production incidents.
Cost Optimization & Capacity Planning
Spot vs on-demand GPU nodes, right-sizing for inference vs training, and budgeting frameworks.
About the Author
Sharon Sahadevan
AI Infrastructure Engineer
Building production GPU clusters on Kubernetes. H100s, large-scale model serving, and end-to-end ML infrastructure across Azure and AWS.
10+ years designing cloud-native platforms with deep expertise in Kubernetes orchestration, GitOps (Argo CD), Terraform, and MLOps pipelines for LLM deployment.
Author of KubeNatives, a weekly newsletter read by 3,000+ DevOps and ML engineers for production insights on K8s internals, GPU scheduling, and model-serving patterns.
Engineer Reviews
I have been following Sharon for a very long time on LinkedIn, learning from his deep production experience in Kubernetes, cloud-native infrastructure, and AI/ML platforms.
When he launched DevOpsBeast, I saw it as an opportunity to tap into his real-world production knowledge, especially around GPU infrastructure in Kubernetes, which was still relatively new to me.
Going through the course helped me connect many of the dots around the errors and challenges I faced while setting up GPU clusters and managing workloads in my current role.
I highly recommend DevOpsBeast to anyone looking for deep practical experience and not just theory.