LLM Serving Mastery

Build the systems intuition and production evidence needed to operate open-weight LLMs at scale.

LLM Serving Mastery is an executable curriculum for engineers who already build LLM applications and want to own the infrastructure underneath them. You will reason from GPU memory traffic, transformer cost, and workload shape before reaching for serving frameworks.

The curriculum is organized as ten skills. Each substantial topic combines a long-form technical article, runnable code, a measurement you produce, and a lab with an explicit success criterion.

Start with Foundations View the learning path

What you will build

You will progress from hardware and inference fundamentals to serving, compression, data engineering, post-training, Kubernetes platforms, observability, and governance. The goal is not familiarity with a list of tools. It is the ability to predict what will bind, measure whether you were right, and defend a production decision with evidence.

Stage	Capability
Foundations	Derive memory, bandwidth, cache, and transformer costs from first principles
Inference and optimization	Take an open model to a latency, throughput, and cost target
Model and data lifecycle	Build, adapt, evaluate, and package models without treating them as black boxes
Production platform	Operate multi-tenant inference with scheduling, observability, security, and governance

How to use the curriculum

Read the theory article first, then run the linked artifact and reproduce its evidence. Complete the lab only after you can predict the result. Every performance claim should end in a chart or machine-readable result produced on your own hardware.

The source repository contains all scripts, configurations, and committed evidence. Use the GitHub link in the header to clone it or inspect the runnable implementation beside each topic.