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Outcome School · Inference and serving18 min read

by Amit Shekhar · 11 May 2026

Continuous Batching in LLMs

Continuous Batching, a technique that lets LLM servers handle many more users at the same time by keeping the GPU busy at every single step of generation.

3,494 words#llm#ai#machine-learning4 recall cards

Continuous Batching in LLMs
Read on Outcome School ↗then come back to lock it in
Before you read, guess

Why is batching important for GPU efficiency?

Ten seconds, a guess, then read — a wrong guess still makes the answer stick.

What this article covers

  1. The Big Picture
  2. Quick Recap: How an LLM Generates Tokens
  3. Why Batching Matters for LLMs
  4. The Old Way: Static Batching
  5. The Problem with Static Batching
  6. What is Continuous Batching?
  7. The Ride-Share Analogy
  8. How Continuous Batching Works Step by Step
  9. A Numeric Example
  10. Real Numbers and Speedup
  11. Benefits of Continuous Batching
  12. A Few Important Notes

The article lives on outcomeschool.com. Read it there, then come back: the tutor in the margin has read it and will answer questions, and the questions below check what stayed.

Before you go

In one sentence, what was this chapter about?

From memory, without scrolling up. Writing it is what makes it yours; the grade is only to show you what you had.

How sure?