Individual skill
llm-inference-scaling
Auto-scale LLM inference clusters on Kubernetes using KEDA, custom GPU metrics, and horizontal pod autoscaling. Handle traffic spikes, implement queue-based scaling, and optimize cost with spot instances for AI workloads.
infrastructure/local-ai/llm-inference-scaling/SKILL.md
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# 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