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Welcome to a practical deep dive on serving large language models at scale. If you’re exploring what it takes to serve a large language model (LLM) to thousands of concurrent users, this lesson will walk you through the real operational challenges and the architecture patterns that solve them. Veteran Kubernetes operators will recognize part of the playbook — package the app in a container, load balance requests, add replicas, and scale — but LLMs introduce new constraints that make the usual approach break in surprising ways. This course shows exactly where and why that happens, and how to recover. Hi — I’m Chris Short, Principal Trainer at KodeKloud. Whether you’re on a platform team, an SRE, or simply curious about the infrastructure behind modern chatbots, this material takes you from a single GPU to a shared model-serving fleet with demos, commands, and measurable results. The material is split into two complementary halves:
  • Story-driven concepts: No terminals or clusters. We build ideas step-by-step, starting with a single user asking a chatbot a question. Each new idea is introduced as the solution to a limitation discovered in the previous step, so the resulting architecture emerges naturally.
  • Hands-on demos: Real commands executed against a real cluster. Every architectural claim from the story is verified with live metrics, commands, and observable behavior.
A presentation slide titled "What One Server Already Does" showing six icon tiles with labels like "Holds the model," "Reads your prompt," "Writes the answer," "Saves that work," "Keeps it between turns," and "Shares it with everyone." A small circular video inset of a presenter appears in the lower-right corner.
What you’ll learn
  • How far the familiar Kubernetes pattern (containerize → load balance → add replicas) gets you when serving LLMs — and exactly where it fails.
  • The failure modes you’ll see in practice: request drops, head-of-line blocking, GPU memory contention, and throughput collapse — plus how those failures show up in real metrics.
  • A component-by-component architecture for model serving on Kubernetes, with responsibilities for routing, placement, and worker lifecycles.
  • How to validate routing and placement decisions using commands and dashboards rather than slides.
  • A safe rollout strategy to update model servers without dropping in-flight requests — turning a demonstration into an operational pattern.
  • How to read the dashboards and benchmarks that tell you whether the system is healthy and where to tune for cost, latency, and throughput.
A presentation slide titled "What the Knobs Move" showing a row of control dials, icons for model/hardware/traffic, and two vertical meters labeled Speed and Cost. A small video thumbnail of a person speaking appears in the bottom-right.
You can follow the demos and reproduce the results without owning a cluster — the lesson provides an environment and the exact commands you’ll run, so you can focus on understanding architecture, behaviors, and observable signals.
Community matters. At KodeKloud we learn together: ask questions, share what you build, and use community feedback to turn examples into production-ready designs. Get comfortable — we’re going to disassemble the system behind the chatbots you use every day, examine the constraints, and assemble a scalable, observable model-serving architecture you can run in production.

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