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name: airunway-aks-setup description: "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", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller"." license: MIT metadata: author: Microsoft version: "1.1.2" argument-hint: "[skip-to-step N]"

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 (baseline troubleshooting), optional aks-gpu-inference (existing GPU/inference Day-2 incidents only)

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

GPU setup versus Day-2 incidents

Keep initial GPU node-pool, provider, controller, and model setup in this skill. For an incident affecting an existing GPU or inference deployment, load optional-gpu-day2.md before deciding whether to offer the focused optional add-on.

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
2	Controller Installation — CRD + controller deployment	step-2-controller.md
3	GPU Assessment — detect GPU models, flag dtype/attention constraints	step-3-gpu.md
4	Provider Setup — recommend and install inference provider	step-4-provider.md
5	First Deployment — pick a model, deploy, verify Ready	step-5-deploy.md
6	Summary — recap, smoke test, next 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.