gke-inference

name: gke-inference description: >- Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for migrating existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-migration), GKE RAG with Cloud SQL/AlloyDB (use google-cloud-solution-rag-enterprise-search-gke-sqldb), or batch/HPC (use gke-batch-hpc). metadata: version: "1.0.1" category: Containers

GKE AI/ML Inference

Routing Note: For migrating existing AI workloads to GKE, open google-cloud-solution-guided-gke-ai-migration/SKILL.md. For GKE RAG with Cloud SQL or AlloyDB (pgvector), open google-cloud-solution-rag-enterprise-search-gke-sqldb/SKILL.md.

This reference covers deploying AI/ML inference workloads on GKE using Google's Inference Quickstart (GIQ) and best practices for LLM serving.

MCP Tools: apply_k8s_manifest, get_k8s_resource, get_k8s_logs, get_k8s_rollout_status, describe_k8s_resource, list_k8s_events. CLI-only: gcloud container ai profiles *

When to Use

• Deploy an AI model (Llama, Gemma, Mistral, etc.) to GKE
• Generate optimized Kubernetes manifests for inference
• Select GPU/TPU accelerators for model serving
• Configure autoscaling for LLM inference

Prerequisites

• A golden path GKE Autopilot cluster (GPU workloads are supported via ComputeClasses and NAP)
• gcloud CLI authenticated
• Sufficient GPU/TPU quota in the target region

Workflow

1. Discovery: Find Models and Hardware

# List all supported models
gcloud container ai profiles models list --quiet

# Find valid accelerator/server combinations for a model
gcloud container ai profiles list --model=<MODEL_NAME> --quiet

# Example: what can run Gemma 2 9B?
gcloud container ai profiles list --model=gemma-2-9b-it --quiet

2. Generate Manifest

gcloud container ai profiles manifests create \
  --model=<MODEL_NAME> \
  --model-server=<SERVER> \
  --accelerator-type=<ACCELERATOR> \
  --target-ntpot-milliseconds=<NTPOT> --quiet > inference.yaml

Parameters:

• --model: Model ID (e.g., gemma-2-9b-it, llama-3-8b)
• --model-server: Inference server (vllm, tgi, triton, tensorrt-llm)
• --accelerator-type: GPU/TPU type (nvidia-l4, nvidia-tesla-a100, nvidia-h100-80gb)
• --target-ntpot-milliseconds: Target Normalized Time Per Output Token (optional, for latency optimization)

Example:

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

3. Review and Deploy

# Review for placeholders (HF tokens, PVCs)
cat inference.yaml

# Deploy
kubectl apply -f inference.yaml

# Monitor
kubectl get pods -w
kubectl logs -f <POD_NAME>

Some models require Hugging Face tokens. Create a Kubernetes Secret and reference it in the manifest.

GPU ComputeClass for Inference

For Autopilot clusters, create a ComputeClass to target GPU nodes:

apiVersion: cloud.google.com/v1
kind: ComputeClass
metadata:
  name: l4-inference
spec:
  priorities:
  - machineFamily: g2
    gpu:
      type: nvidia-l4
      count: 1
    minCores: 4
    minMemoryGb: 16

Accelerator Selection Guide

Autoscaling LLM Inference

GPU-based autoscaling

Use custom metrics for GPU utilization:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: llm-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: llm-server
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metric:
        name: gpu_duty_cycle
      target:
        type: AverageValue
        averageValue: "80"

Best practices for inference autoscaling

1. Use DCGM metrics: Golden path enables DCGM monitoring for GPU utilization metrics
2. Set appropriate minReplicas: At least 1 for always-on serving; 0 for batch/on-demand
3. Tune scale-down delay: LLM model loading is slow; use longer stabilization windows
4. Consider queue depth: Scale on pending requests rather than pure GPU utilization for latency-sensitive workloads

Optimization Tips

• Quantization: Use quantized models (GPTQ, AWQ) to reduce GPU memory and increase throughput
• Batching: Configure model server batch size for throughput vs latency trade-off
• Tensor parallelism: Split large models across multiple GPUs within a node
• KV cache optimization: Tune --gpu-memory-utilization in vLLM for KV cache allocation

Troubleshooting