gke-inference-quickstart

name	gke-inference-quickstart
description	Deploy optimized AI/ML inference workloads on GKE using Google's Inference Quickstart (GIQ). Covers model discovery, manifest generation, and deployment using native MCP tools and CLI.

GKE Inference Quickstart (GIQ)

Purpose

This skill guides the deployment of AI/ML inference workloads on GKE using GIQ. It leverages gcloud container ai profiles manifests create to create optimized Kubernetes manifests based on Google's best practices and benchmarks.

When to Use

• Goal: Deploy an AI model (e.g., Llama, Gemma, Mistral) to GKE.
• Goal: Generate a Kubernetes manifest for inference.
• Context: User asks about "GIQ", "Inference Quickstart", or "AI benchmarks" on GKE.

Prerequisites

• A GKE cluster (preferably with GPU/TPU node pools, though GIQ can help identify requirements).
• gcloud CLI installed and authenticated (for discovery commands).

Workflow

1. Discovery: Find Models and Hardware

Before generating a manifest, you often need to pick a valid combination of Model, Model Server, and Accelerator.

List all supported models:

gcloud container ai profiles models list

Find valid accelerators and servers for a specific model:

# Replace <MODEL_NAME> with a model from the list above (e.g., 'gemma-2-9b-it')
gcloud container ai profiles list --model=<MODEL_NAME>

View benchmarks/profiles (optional): To see costs and latency targets:

gcloud container ai profiles list --model=<MODEL_NAME>

2. Generate Manifest

Use the gcloud container ai profiles manifests create command. This ensures you are using the latest supported flags and options directly from the CLI.

Parameters:

• --model: The model ID (e.g., gemma-2-9b-it).
• --model-server: The inference server (e.g., vllm, tgi, triton, tensorrt-llm).
• --accelerator-type: The accelerator type (e.g., nvidia-l4, nvidia-tesla-a100).
• --target-ntpot-milliseconds: (Optional) Target Normalized Time Per Output Token in ms.

Example Command:

gcloud container ai profiles manifests create \
  --model=gemma-2-9b-it \
  --model-server=vllm \
  --accelerator-type=nvidia-l4 \
  --target-ntpot-milliseconds=50 > inference-workload.yaml

3. Review and Deploy

1. Save: The example command above saves output to inference-workload.yaml. Ensure you have this file.
2. Review: Check for any placeholders or specific requirements (like PVCs or secrets).
   • Note: Some models require Hugging Face tokens. Ensure query instructions for secrets are followed.
3. Deploy:

   kubectl apply -f inference-workload.yaml
   

Troubleshooting

• Invalid Combination: If the manifest creation fails with an invalid combination error, re-run the discovery commands in Step 1 to verify the tuple (model, server, accelerator).
• Quota Issues: Ensure the target region has sufficient quota for the requested accelerator (e.g., NVIDIA_L4_GPUS).

Reference

• Docs: GKE Inference Quickstart Documentation