LLMs Deep Dive

LLM Infra26 chapters

Make sense of LLM Infrastructure & Scaling

Follow a request from model execution to a reliable service. Build intuition for GPUs, numerical efficiency, scheduling, distributed inference, and operating cost, with an advanced training continuation.

Start readingPractice and coding

How a chapter works

Each chapter is a reading of about half an hour. The figures are drawn in the browser and most of them can be operated: drag a slider, append a token, and watch what the text described.

A chapter ends with practice questions and coding problems. The questions can be worked one at a time in study mode. The coding problems open in an editor, and an AI reviewer reads your solution and says what is wrong with it.

Select any passage while reading to ask for another explanation. Reading needs no account. Sign in to ask the tutor, to mark what you understood, and to keep your solutions.

197 practice questions17 coding problems

Contents

Running a model

GPU execution

Numerical and model efficiency

Serving across devices

Operating the service

Advanced training and experiments