Free Lesson
How LLM Serving Works: An Introduction to vLLM and SGLang
45 min
Oct 21, 2026 10:00 PM
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What you'll learn
Why one word costs 16 GB of reading
See what an H100 actually does when a model writes, and why serving is limited by memory rather than maths.
Where a simple server wastes the GPU
Learn how reserved memory and batches that wait for their slowest request leave most of a GPU idle, with the numbers.
The two ideas behind vLLM and SGLang
Understand PagedAttention and continuous batching, then watch one model on one GPU serve fifty users at once.
Why this topic matters
Every AI product pays for every token it generates, and over a model's life serving costs far more than training. vLLM and SGLang are the open-source engines most companies use to serve models like Llama, Qwen and DeepSeek. You will see why a GPU needs a serving engine, where a simple server loses most of its memory and time, and the two ideas that fix it. The first of three free lessons before our vLLM and SGLang bootcamp.
