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The $4,350/Month GPU Waste Problem: How Kubernetes Architecture Creates Massive Cost Inefficiency
Your H100 costs $5,000 per month, but you're only using it at 13% capacity—wasting $4,350 monthly per GPU. Analysis of 4,000+ Kubernetes clusters reveals 60-70% of GPU budgets burn on idle resources because Kubernetes treats GPUs as atomic, non-shareable resources. Discover why this architectural decision creates massive waste, and the five-layer optimization framework (MIG, time-slicing, VPA, Spot, regional arbitrage) that recovers 75-93% of lost capacity in 90 days.
🔗 Full episode page: https://platformengineeringplaybook.com/podcasts/00034-kubernetes-gpu-cost-waste-finops
📝 See a mistake or have insights to add? This podcast is community-driven - open a PR on GitHub!
Keywords: kubernetes gpu, gpu cost optimization, multi-instance gpu, kubernetes finops, gpu utilization, spot instances, vertical pod autoscaler, aws eks cost allocation, nvidia mig, gpu time-slicing
Summary:
• Analysis of 4,000+ K8s clusters shows 13% average GPU utilization because Kubernetes treats GPUs as atomic resources—when a pod requests nvidia.com/gpu:1, it locks the entire GPU even when using only 5% capacity, leaving the remaining 95% completely unusable by other workloads
• Platform teams compound the waste with round-number overprovisioning (memory: 16GB when P99 usage is 4.2GB) without Vertical Pod Autoscaler data, and miss 2-5x regional cost differences plus 70-90% Spot instance savings by anchoring on AWS us-east-1 on-demand pricing
• Multi-Instance GPU (MIG) enables up to 7 isolated instances per A100 with hardware partitioning—real SaaS example: 50 dedicated A100s ($23,760/month) → 8 A100s with 7×1g.10gb MIG instances ($3,802/month) = 84% cost reduction with maintained security isolation
• Five-layer solution framework: Kubernetes resource configuration (GPU limits, node taints preventing 30% non-GPU pod waste), MIG for production inference, time-slicing for development (75% savings per developer), AWS EKS Split Cost Allocation (pod-level GPU tracking since Sept 2025), and model optimization (quantization achieving 4-8x compression)
• 90-day implementation playbook: Days 1-30 foundation (DCGM Exporter, node taints, VPA in recommendation mode, cost tracking), Days 31-60 optimization (right-sizing from VPA data, MIG for production, time-slicing for dev), Days 61-90 advanced (regional arbitrage, Spot pilot, model quantization)—target outcome is 13-30% baseline → 60-85% utilization with $780K annual savings for 20-GPU clusters
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By vibesreYour H100 costs $5,000 per month, but you're only using it at 13% capacity—wasting $4,350 monthly per GPU. Analysis of 4,000+ Kubernetes clusters reveals 60-70% of GPU budgets burn on idle resources because Kubernetes treats GPUs as atomic, non-shareable resources. Discover why this architectural decision creates massive waste, and the five-layer optimization framework (MIG, time-slicing, VPA, Spot, regional arbitrage) that recovers 75-93% of lost capacity in 90 days.
🔗 Full episode page: https://platformengineeringplaybook.com/podcasts/00034-kubernetes-gpu-cost-waste-finops
📝 See a mistake or have insights to add? This podcast is community-driven - open a PR on GitHub!
Keywords: kubernetes gpu, gpu cost optimization, multi-instance gpu, kubernetes finops, gpu utilization, spot instances, vertical pod autoscaler, aws eks cost allocation, nvidia mig, gpu time-slicing
Summary:
• Analysis of 4,000+ K8s clusters shows 13% average GPU utilization because Kubernetes treats GPUs as atomic resources—when a pod requests nvidia.com/gpu:1, it locks the entire GPU even when using only 5% capacity, leaving the remaining 95% completely unusable by other workloads
• Platform teams compound the waste with round-number overprovisioning (memory: 16GB when P99 usage is 4.2GB) without Vertical Pod Autoscaler data, and miss 2-5x regional cost differences plus 70-90% Spot instance savings by anchoring on AWS us-east-1 on-demand pricing
• Multi-Instance GPU (MIG) enables up to 7 isolated instances per A100 with hardware partitioning—real SaaS example: 50 dedicated A100s ($23,760/month) → 8 A100s with 7×1g.10gb MIG instances ($3,802/month) = 84% cost reduction with maintained security isolation
• Five-layer solution framework: Kubernetes resource configuration (GPU limits, node taints preventing 30% non-GPU pod waste), MIG for production inference, time-slicing for development (75% savings per developer), AWS EKS Split Cost Allocation (pod-level GPU tracking since Sept 2025), and model optimization (quantization achieving 4-8x compression)
• 90-day implementation playbook: Days 1-30 foundation (DCGM Exporter, node taints, VPA in recommendation mode, cost tracking), Days 31-60 optimization (right-sizing from VPA data, MIG for production, time-slicing for dev), Days 61-90 advanced (regional arbitrage, Spot pilot, model quantization)—target outcome is 13-30% baseline → 60-85% utilization with $780K annual savings for 20-GPU clusters