llm-inference-scaling

LLM Inference Scaling

Scale LLM inference horizontally on Kubernetes with GPU-aware autoscaling, request queuing, and cost-efficient spot instance strategies.

When to Use This Skill

Use this skill when:

• LLM API traffic is unpredictable and you need to scale up/down automatically
• Managing a fleet of vLLM or TGI inference pods on Kubernetes
• Reducing inference costs with spot/preemptible GPU instances
• Implementing queue-based autoscaling for batch inference jobs
• Building a multi-model serving platform that shares GPU resources

Prerequisites

• Kubernetes cluster with GPU nodes (NVIDIA operator installed)
• KEDA (Kubernetes Event-Driven Autoscaler) installed
• Prometheus with GPU metrics (dcgm-exporter or gpu-operator)
• Helm 3+ for chart deployments

GPU Node Setup

# Install NVIDIA GPU Operator (handles drivers, container toolkit, DCGM)
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update

helm install gpu-operator nvidia/gpu-operator \
  --namespace gpu-operator \
  --create-namespace \
  --set driver.enabled=true \
  --set dcgm.enabled=true \
  --set devicePlugin.enabled=true

# Verify GPU nodes are recognized
kubectl get nodes -l nvidia.com/gpu.present=true
kubectl describe node <gpu-node> | grep nvidia

vLLM Deployment with GPU Resources

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-llama-8b
  labels:
    app: vllm
    model: llama-3.1-8b
spec:
  replicas: 1
  selector:
    matchLabels:
      app: vllm
      model: llama-3.1-8b
  template:
    metadata:
      labels:
        app: vllm
        model: llama-3.1-8b
    spec:
      nodeSelector:
        nvidia.com/gpu.present: "true"
      tolerations:
      - key: nvidia.com/gpu
        operator: Exists
        effect: NoSchedule
      containers:
      - name: vllm
        image: vllm/vllm-openai:latest
        args:
        - "--model"
        - "meta-llama/Llama-3.1-8B-Instruct"
        - "--tensor-parallel-size"
        - "1"
        - "--gpu-memory-utilization"
        - "0.90"
        - "--max-num-seqs"
        - "128"
        resources:
          requests:
            nvidia.com/gpu: "1"
            memory: "20Gi"
            cpu: "4"
          limits:
            nvidia.com/gpu: "1"
            memory: "24Gi"
            cpu: "8"
        ports:
        - containerPort: 8000
        readinessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 60
          periodSeconds: 10
        env:
        - name: HUGGING_FACE_HUB_TOKEN
          valueFrom:
            secretKeyRef:
              name: hf-token
              key: token