Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "

AI Runway AKS Setup This skill walks users from a bare Kubernetes cluster to a running AI model deployment. Follow each step in sequence unless the user provides skip to step N to resume from a specific phase. Cost awareness: GPU node pools incur significant compute charges (A100 80GB can cost $3–5+/hr). Confirm the user understands cost implications before provisioning GPU resources. Prerequisites This skill assumes an AKS cluster already exists. If the user does not have a cluster, hand off to the azure kubernetes skill first to provision one (with a GPU node pool unless CPU only inference is acceptable), then return here. Quick Reference Property Value Best for End to end AI Runway onboarding on AKS CLI tools kubectl , make , curl MCP tools None Related skills azure kubernetes (cluster setup), azure diagnostics (troubleshooting) When to Use This Skill Use this skill when the user wants to: Set up AI Runway on an existing AKS cluster from scratch Install the AI Runway controller and CRDs Assess GPU hardware compatibility for model deployment Choose and install an inference provider (KAITO, Dynamo, KubeRay) Deploy their first AI model to AKS via AI Runway Resume a partially complete AI Runway setup from a specific step MCP Tools This skill uses no MCP tools. All cluster operations are performed directly via kubectl and make . Rules 1. Execute steps in sequence — load the reference for each step as you reach it 2. Report cluster state at each step: ✓ healthy, ✗ missing/failed 3. Ask for user confirmation before any install or deployment action 4. If a step is already complete, report status and skip to the next step 5. If the user provides skip to step N , start at step N; assume prior steps are complete Steps Step Reference 1 Cluster Verification — context check, node inventory, GPU detection [step 1 verify.md](references/steps/step 1 verify.md) 2 Controller Installation — CRD + controller deployment [step 2 controller.md](references/steps/step 2 controller.md) 3 GPU Assessment — detect GPU models, flag dtype/attention constraints [step 3 gpu.md](references/steps/step 3 gpu.md) 4 Provider Setup — recommend and install inference provider [step 4 provider.md](references/steps/step 4 provider.md) 5 First Deployment — pick a model, deploy, verify Ready [step 5 deploy.md](references/steps/step 5 deploy.md) 6 Summary — recap, smoke test, next steps [step 6 summary.md](references/steps/step 6 summary.md) Error Handling Error / Symptom Likely Cause Remediation No kubeconfig context Not connected to a cluster Run az aks get credentials or equivalent Controller in CrashLoopBackOff Config or RBAC issue kubectl logs n airunway system l control plane=controller manager previous Provider not ready Image pull or RBAC issue kubectl logs <pod name n <namespace for the provider pod ModelDeployment stuck in Pending GPU scheduling failure or provider not ready kubectl describe modeldeployment <name n <namespace events bfloat16 errors at inference T4 or V100 lacks bfloat16 support Add dtype float16 to serving args For full error handling and rollback procedures, see [troubleshooting.md](references/troubleshooting.md).

airunway-aks-setup