How to Deploy a GPU Cluster on AKS for Running vLLM at Scale

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A technical guide details how to set up an Azure Kubernetes Service (AKS) cluster with a GPU node pool to serve the vLLM inference engine, using Standard_NV36ads_A10_v5 nodes equipped with NVIDIA A10 GPUs. The setup separates system workloads onto a cheap CPU node pool while placing GPU nodes on a scale-to-zero pool, meaning GPU costs are only incurred when inference workloads are active. Observability is handled through a kube-prometheus-stack comprising Prometheus and Grafana, alongside the NVIDIA DCGM exporter for GPU-specific metrics. Azure GPU quota must be requested in advance through the portal, as new subscriptions default to zero allowance for GPU VM families due to cost and abuse concerns. The guide is part of a broader series covering GPU selection, autoscaler tuning, and vLLM deployment on AKS.

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