A guide for DevOps engineers on orchestrating LLMs availability and scaling using Kubernetes.

Key Sections:
1. **Prerequisites:** GPU Operator setup, Nvidia Container Toolkit.
2. **Serving Options:** KServe vs Ray Serve vs simple Deployment.
3. **Resource Management:** Requests/Limits for GPU, dealing with bin-packing.
4. **Scaling:** HPA based on custom metrics (queue depth).
5. **Example:** Full Helm chart walkthrough for a vLLM service.

**Internal Linking Strategy:** Link to Pillar. Link to 'Ollama vs vLLM'.

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A comprehensive DevOps guide for deploying local LLMs on Kubernetes using vLLM. Covers GPU node preparation with the NVIDIA GPU Operator, comparison of serving frameworks (plain Deployment vs Ray Serve vs KServe), GPU resource management including bin-packing strategies with MIG/MPS, and custom-metric autoscaling via HPA targeting vLLM queue depth. Includes a full Helm chart walkthrough with deployment, service, and HPA templates for serving Mistral-7B-Instruct, plus verification steps and an implementation checklist.

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Prerequisites: Preparing Your Cluster for GPU Workloads

Choosing a Serving Framework: KServe vs. Ray Serve vs. Simple Deployment

Resource Management: GPU Requests, Limits, and Bin-Packing

Autoscaling Inference: HPA on Custom Metrics

Full Walkthrough: Helm Chart for a vLLM Service