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How Do CPU, GPU and Memory Chips Differ?

  • Basic
  • Article
  • Ages 12+
  • 3 min read
Three distinct CPU, graphics board and RAM components arranged separately on a light grey studio table, top-down view.

In an application, the CPU executes instructions and coordinates work, the GPU suits many similar parallel operations, and memory holds instructions and data. Their main difference is their job rather than package size.

Contents

What you will learn

  • Distinguish computation from data storage.
  • Explain why a CPU and GPU can cooperate.
  • Follow a simple image-processing example.

Before you start

Read the linked prerequisite first. This article explains principles and does not require assembling or opening equipment.

How do CPUs and GPUs divide work?

A CPU executes commands needed by programs and the operating system. It handles varied work, including serial tasks whose next step depends on the previous result. A GPU has many smaller, more specialized cores suited to dividing work into similar parallel pieces.

A GPU therefore does not automatically replace a CPU. The useful comparison asks whether the work can be divided and which steps depend on earlier results.

Serial rows, parallel cells and storage represent different roles.
  • Left — CPU: instruction processing
  • Centre — GPU: parallel work
  • Right — memory: data storage
Working one step after another and splitting work into parts that run together are two different ways to organise a job.

What does memory do?

In this CPU model, instruction memory supplies instructions while data memory supplies or receives data. A register holds operands or results inside the processor. Holding a value and computing with it are different responsibilities.

More memory does not itself change an operation. When reading a computer diagram, ask where the data lives, who requests it and who uses the result.

Cooperation rather than substitution

Consider an illustrative image editor: the CPU reads a user request, similar operations across many pixels could be assigned to a GPU, and image data must be held for processing. This is a proposed division of work, not a description of every image editor.

Some CPUs include a GPU on the same chip. Sharing a package does not erase the distinct roles of serial processing, parallel processing and storage.

A worked example

Take a photo and the request “brighten the whole image” in a hypothetical photo editor. The CPU receives the user’s request and coordinates the work. Brightening each pixel is nearly the same operation, and one pixel does not wait for another’s result, so the work can be split across the many small cores of a GPU. The image data sits in memory; registers hold the values being computed inside the processor.

Now switch to a different job: adding up a list of numbers one by one, where each step needs the previous total. That does not split into parallel pieces easily, so it is the kind of work a CPU handles serially. The two examples show that one machine can use both styles, and the useful question is whether the job can be divided. If memory is deciding how to brighten the image in your diagram, revisit that role.

Frequently asked questions

Are GPUs only for images?

No. Parallel processing is also used for workloads such as AI.

Does an integrated GPU remove the CPU?

No. Integration places components with different responsibilities together.

Summary

  • CPUs execute instructions and handle varied work.
  • GPUs suit work that can be divided into parallel pieces.
  • Memory holds instructions and data for processing.

Terms in this article

English and Vietnamese terms, with the meanings used here. Hover, tap or Tab to a term with a dotted underline for an explanation as you read.

registerthanh ghi
Storage inside a processor for operands and results.

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Sources

These sources are written for older readers. If they feel hard, read them with an adult.