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/airunway-aks-setup

@d0c21c0 official

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".

Use this Skill: https://skilld.dev/gh/microsoft/github-copilot-for-azure/airunway-aks-setup

This session only. Nothing lands on disk.

referencesmodel-sizing.md

≈470 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Model Sizing & Starter Recommendations

VRAM Sizing Guide

Estimate how much VRAM a model requires. Values are weights only — add 20–30% overhead for KV cache and activations during inference.

Parameters float16 / bfloat16 int8 int4 / GGUF Q4
1B ~2 GB ~1 GB ~0.5 GB
3B ~6 GB ~3 GB ~1.5 GB
7–8B ~14–16 GB ~7–8 GB ~3.5–4 GB
13B ~26 GB ~13 GB ~6.5 GB
34B ~68 GB ~34 GB ~17 GB
70B ~140 GB ~70 GB ~35 GB

Example: 4× A100 80 GB = 320 GB total. Llama-3.1-70B at bfloat16 ≈ 168 GB with overhead — fits across 4 GPUs with tensor parallelism.

Starter Model Recommendations

Cluster Capacity Model Provider Notes
CPU-only google/gemma-3-1b-it-qat-q8_0-gguf KAITO (llama.cpp) GGUF Q8; runs on CPU
1× T4 (16 GB) microsoft/Phi-3-mini-4k-instruct KAITO (vLLM) ~8 GB float16; fits with headroom
1× A10G/L4 (24 GB) meta-llama/Llama-3.1-8B-Instruct KAITO (vLLM) ~16 GB bfloat16; gated — needs HF token
1× A100 40 GB microsoft/Phi-3-medium-128k-instruct KAITO (vLLM) ~28 GB float16; non-gated; MIT license
1× A100 80 GB / H100 meta-llama/Llama-3.1-8B-Instruct KAITO (vLLM) Oversized; upgrade to 70B if more GPUs available
4× A100 80 GB meta-llama/Llama-3.1-70B-Instruct KAITO (vLLM, TP) ~168 GB; tensor parallelism; gated

Gated Models

These models are gated on HuggingFace and require an access token:

  • meta-llama/Llama-3.1-8B-Instruct
  • meta-llama/Llama-3.1-70B-Instruct

Non-gated alternatives (no token required):

  • microsoft/Phi-3-mini-4k-instruct (MIT license)
  • microsoft/Phi-3-medium-128k-instruct (MIT license)
  • google/gemma-3-1b-it-qat-q8_0-gguf (Gemma license)

Source: SKILL.md on GitHub

No alerts5mo3 checks · Risk SAFE
  • Gen Agent Trust Hub5mo

    This skill facilitates the setup of AI Runway on Azure Kubernetes Service (AKS) using standard administrative tools. It incorporates security best practices, particularly regarding the handling of sensitive HuggingFace tokens during deployment. No security considerations were identified.

  • Socket5mo

    No alerts

  • Snyk5mo

    Risk: LOW · No issues

Signed by skilld at d0c21c0. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 5 months ago
metadata
{
  "author": "Microsoft",
  "version": "0.0.0-placeholder"
}
argument-hint
[skip-to-step N]
  • kubernetes
  • aks
  • azure
  • llm
  • model-serving
  • gpu
  • inference
  • kaito
  • vllm
  • mlops

README badge

README badge for microsoft/github-copilot-for-azure/airunway-aks-setup

Installs and configures AI Runway on an existing AKS cluster, walking through controller setup, GPU assessment, inference provider selection, and model deployment. Targets users deploying large language models or other GPU-accelerated workloads to Kubernetes via KAITO, Dynamo, or KubeRay.

Generated from the current SKILL.md.

Do I need an existing AKS cluster to use this skill?
Yes. This skill assumes you already have an AKS cluster. If you don't, the skill will hand off to the azure-kubernetes skill to provision one first, then return here.
What inference providers does this skill support?
The skill covers KAITO, Dynamo, and KubeRay as inference provider options. Step 4 recommends and installs the appropriate provider for your setup.
Does this skill work with CPU-only clusters?
Yes, CPU-only inference is acceptable, though the skill includes GPU assessment and provider setup primarily designed for GPU workloads. A bare cluster without GPU resources is still supported.
What happens if I already have part of AI Runway set up?
You can use the `skip-to-step N` argument to resume from a specific phase. The skill will report the current state and skip already-completed steps.
What are the cost implications of using this skill?
GPU node pools incur significant charges — A100-80GB can cost $3–5+/hr. The skill will confirm you understand these costs before provisioning GPU resources.

Generated from the current SKILL.md. These answers refresh after source changes.