Intermediate to Advanced|8 hours|15 lessons

GPU Cost Optimization on Kubernetes

GPUs are the most expensive line item in any cluster running ML workloads. Most teams overspend by 40-70% on the wrong GPU types, idle capacity, and missed autoscaling. This course is the operational playbook for cutting GPU spend in half without breaking production: right-sizing GPU types, MIG partitioning, GPU-aware autoscaling, spot and reserved capacity strategies, and cost attribution that makes engineers actually care.

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Text-based, no videos

5 modules, 15 lessons

Lifetime access

What you'll learn

The true cost of a GPU — compute, storage, network, idle overhead — and why most cost-per-request calculations are wrong

The three forms of GPU waste (wrong type, no autoscaling, non-GPU pods on GPU nodes) and how to measure each in your cluster

The 80/20 of GPU optimization — which fixes deliver 80% of the savings and the order to apply them

Matching GPU type to workload: T4 vs A10G vs A100 vs H100 with real benchmarks for common workloads

MIG partitioning math — when 1× A100 with MIG saves money over 4× T4, and when it does not

Quantization for cost: INT8 and INT4 trade-offs that let smaller GPUs handle the same workload

GPU-aware HPA: why CPU-based scaling is useless and which metrics actually correlate with capacity

Karpenter for GPU node pools — spot/on-demand mix that hits 60-80% savings without breaking SLOs

Scale-to-zero with KEDA for low-traffic models, plus cold-start mitigation

Spot instances for training: the checkpointing and recovery playbook that delivers 60-70% savings

Reserved instances and Savings Plans — when commitments make sense and how much to commit

Hybrid capacity strategies that production ML platforms actually use

Per-workload cost attribution with Kubecost, OpenCost, and custom dashboards

The quarterly GPU audit — a systematic process for ongoing optimization (real case study: $180K → $67K monthly)

Curriculum

5 modules · 15 lessons

Understanding GPU Costs

Where the money actually goes. The true cost of a GPU, the three forms of waste, and the 80/20 of optimization.

3 lessons

The True Cost of a GPU30 minFREE The Three Forms of Waste30 minFREE The 80/20 of GPU Optimization30 minFREE

Right-Sizing GPU Workloads

The decision tree for picking the right GPU per workload, the MIG math that often beats smaller GPUs, and quantization for cost.

3 lessons

Matching GPU Type to Workload30 min MIG Partitioning for Cost Savings30 min Quantization for Cost30 min

Autoscaling for GPU Workloads

The autoscaling primitives that actually work for GPUs: custom metrics for HPA, Karpenter for GPU node pools, KEDA for scale-to-zero.

3 lessons

HPA That Actually Works for GPUs30 min Cluster Autoscaling for GPU Nodes30 min Scale-to-Zero30 min

Spot and Reserved Capacity

The spot training playbook, the math behind Reserved Instances and Savings Plans, and the hybrid strategies production teams actually use.

3 lessons

Spot Instances for Training30 min Reserved Instances and Savings Plans30 min Hybrid Strategies30 min

Cost Tracking and Attribution

The labels, tools, and rituals that turn GPU spend from a black box into per-workload accountability — and the audit that makes optimization continuous.

3 lessons

Per-Workload Cost Attribution30 min Building a Cost Optimization Culture30 min The Quarterly GPU Audit30 min

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.

Ready to master GPU infrastructure?

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