Key Learning Points:

  • GPUs play an important role in image processing and AI training, excelling at handling many calculations simultaneously.
  • In deep learning, GPUs are essential for efficiently processing large volumes of data.
  • While powerful, GPUs are expensive and consume a lot of electricity, so they are often used through cloud services.

What Is a GPU? The Unsung Hero Behind AI’s Progress

The beautiful visuals you see on your computer or smartphone screen don’t just appear by magic. Behind the scenes, an incredible amount of fast-paced “calculation” is taking place. Whether it’s the realistic graphics in video games, CGI in movies, or the AI that generates text and images—these all rely on computing power.

At the heart of this power is a device called a “GPU.” You may not hear this term often, but today it’s become an essential part of supporting AI technologies.

How Does a GPU Work—and Why Is It So Well-Suited for AI?

GPU stands for “Graphics Processing Unit.” As the name suggests, it was originally developed to quickly render graphics for things like video games and editing software.

However, in recent years, its ability to perform “parallel processing” has drawn attention, making it widely used in AI training as well.

Parallel processing means being able to carry out many small calculations at the same time. Imagine trying to clean a large room—if one person does it alone, it takes time. But if 1,000 people each clean a small section at once, the job gets done much faster. That’s how a GPU works: using thousands of tiny calculation units to process massive amounts of data all at once.

This makes GPUs a perfect match for deep learning—a type of AI technology that requires repeatedly analyzing large datasets. Because deep learning involves performing countless calculations over and over again, GPUs are ideal thanks to their speed and efficiency.

Understanding GPU and AI Through Photo Searching

Let’s think about this with a more relatable example. Suppose someone asks you to find all the cat photos from a collection of 1,000 pictures. Doing it alone would take time—but if 1,000 people each check one photo, you’d finish almost instantly. In this analogy, those “1,000 pairs of eyes” represent what a GPU does.

And this task—finding cats—is similar to what AI does when recognizing or classifying images. These are exactly the kinds of jobs where GPUs shine.

That said, there are some things to keep in mind. Because they’re high-performance devices, GPUs tend to be expensive and consume a lot of electricity. Also, using them effectively often requires specialized knowledge and the right environment.

For these reasons, more and more people now use cloud-based GPU services instead of owning them directly. This is part of what lies behind terms like “cloud AI,” which we’ll explore further in another article.

The Future Role and Potential of GPUs

As AI continues to evolve rapidly, GPUs remain its silent but powerful foundation. We may not notice them directly in our daily lives—but thanks to their support behind the scenes—we enjoy convenient services and new experiences every day.

In future articles, we’ll also look into related technologies like “TPUs” and “inference optimization.” These topics can’t be fully understood without knowing how they relate to GPUs as well. While such terms might sound technical at first glance—they’re rooted in human creativity and trial-and-error efforts.

By unpacking each concept step by step together—you’ll likely have moments where everything suddenly clicks into place: “Ah! So that’s what it means.”

Next time, we’ll introduce TPUs—special chips designed specifically for deep learning—and take another step into understanding how the “brains” behind AI really work.

Glossary

GPU: Short for “Graphics Processing Unit.” Originally developed to speed up graphic-related tasks like gaming or video editing. Today it’s widely used for training artificial intelligence systems.

Parallel Processing: A method where many small calculations are carried out at the same time. Like cleaning with many people at once—it allows huge amounts of data to be processed quickly.

Deep Learning: A type of AI technology that learns patterns or features from large datasets through repeated analysis. It’s used in tasks like image recognition or generating human-like text.