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