Phase 0: How Computers Actually Work · ~30 minutes · Python · uv

CPU vs. GPU, and Why It'll Matter Later

A GPU isn't a faster CPU. It's a different shape of computation entirely.

Hiring signal: Can explain GPU parallelism as a shape-of-workload question, not a raw-speed question — the exact distinction that makes later CUDA/training content make sense instead of feeling like memorized trivia

What you will learn

Explain the structural difference between a CPU (few powerful sequential cores) and a GPU (thousands of simple parallel cores)
Identify whether a given task is parallelizable (GPU-friendly) or inherently sequential (CPU-friendly)
Explain in one sentence why matrix multiplication specifically benefits from a GPU
Measure a real timing difference between a loop-based and a vectorized approach to the same problem

Introduction

Type: Learn Languages: Python Prerequisites: Lesson 01 (What a Computer Actually Does) Time: ~30 minutes

Learning Objectives

Explain the structural difference between a CPU (few powerful sequential cores) and a GPU (thousands of simple parallel cores)
Identify whether a given task is parallelizable or inherently sequential
Explain in one sentence why matrix multiplication specifically benefits from a GPU
Demonstrate, with real code, why order matters for some tasks and not others

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, Few Powerful Workers vs. Thousands of Simple Ones, What Makes a Task Parallelizable, Proving It With Code, Not Just Trust, What You're Building, Key Terms, Exercises — plus a hands-on lab, quiz, and project artifact.

Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.