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NVIDIA A100 vs H100 vs T4 im Vergleich: Welche GPU für welchen ML-Workload auf Kubernetes und wie ein AI-Startup damit 180.000 Euro jährlich spart.
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GPU-Workloads in Kubernetes mit HPA, KEDA und Custom Metrics effizient skalieren. Spot Instances für 60-70% Kostenersparnis bei ML-Workloads nutzen.