Easy-to-Understand Explanation of the Differences Between CPU and GPU: Usage-Based Comparison

Both the CPU and GPU are devices for performing calculations, but these two components are responsible for completely different areas.

If the differences between the CPU and GPU are not understood when selecting parts for a custom PC, it may become impossible to do what is intended.

For example, if a suitable GPU is not installed when high graphics performance is needed for gaming, games may not run smoothly. On the other hand, having a GPU can speed up video encoding for editing, so it is important to know the differences depending on the intended use.

This article also explains basic knowledge such as how to read CPU manufacturer and model numbers, performance benchmarks, and how to choose parts from the perspective of performance and compatibility.

≫ Related article: How to Choose a CPU for a Custom PC [Performance / Features / Compatibility]

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Differences Between CPU and GPU

This section explains the differences in roles, structure, and performance between CPUs and GPUs.

Differences in Roles

The CPU (Central Processing Unit) is the part responsible for all calculations inside the computer.

These calculations include not only simple arithmetic, but also logical operations, data transfer and management, instruction execution, and input/output control.

It has a small number of high-performance cores and excels at processing complex tasks sequentially.

On the other hand, the GPU (Graphics Processing Unit) is the part responsible for graphics processing such as image processing and video output.

It is necessary for displaying the results processed by programs on the display and for handling complex 3D graphics processing.

With many cores and excellent parallel processing capabilities, it is good at processing large amounts of data simultaneously.

Because of this, in addition to 3D graphics rendering, the GPU demonstrates its performance in tasks such as video encoding, AI machine learning, and scientific computing through GPGPU technology, which utilizes the GPU’s computing resources for non-graphics processing.

The GPU is not an essential part, but if it is not installed, the CPU will handle graphics processing.

However, the graphics performance of the CPU (CPU integrated GPU) is not very high, so it can only handle basic screen output, 2D games, and very light 3D games.

Therefore, a GPU is necessary for uses that require high graphics performance, such as 3D games or video editing, or for processing that utilizes GPGPU.

This article also explains how to read CPU and GPU model numbers and how to choose parts from the perspective of performance and compatibility.

≫ Related article: How to Choose a CPU for a Custom PC [Performance / Features / Compatibility]

≫ Related article: How to Choose a Graphics Card for a Custom PC [Performance / Features / Compatibility]

Differences in Structure and Mechanism

The differences in structure and mechanism between CPUs and GPUs are due to their design purposes.

CPUs are designed to efficiently handle general computing tasks, with a small number of high-performance cores, each capable of quickly processing complex tasks.

This allows them to flexibly handle a variety of tasks, such as operating system management and application execution.

On the other hand, GPUs are specialized for processing large amounts of data in parallel, with hundreds to thousands of small cores capable of performing many calculations simultaneously.

This is especially efficient for tasks that handle large amounts of data, such as image processing and machine learning.

For example, 3D graphics rendering and deep learning model training are typical examples.

In this way, CPUs and GPUs each play different roles by leveraging their respective strengths.

There is a video that visually explains the most important differences between CPUs and GPUs in an easy-to-understand way.

https://www.youtube.com/watch?v=-P28LKWTzrI

While the CPU processes tasks one by one, the GPU processes many tasks in parallel at once.

Recent CPUs have 10 to 30 cores in a multi-core configuration, so CPUs can also perform some parallel processing.

However, the number of GPU cores is 2,000 to 20,000, which is an order of magnitude higher, enabling massive parallel processing of large amounts of data with overwhelming computational power.

In this way, CPUs are good at single-threaded processing, while GPUs excel at specific tasks that require parallel processing.

Single-threaded processing means executing one task at a time in order, like cutting vegetables, then stir-frying, and finally seasoning when cooking.

In this method, the next task does not start until the previous one is finished.

On the other hand, parallel processing is a method of executing multiple tasks at the same time, which is especially effective on computers with multiple processors or cores.

If we compare parallel processing to cooking, it is like one person cutting vegetables while another person is stir-frying meat at the same time.

This can shorten the total work time.

Parallel processing is especially effective when handling large amounts of data or when calculations are complex.

For example, in scientific computing, image processing, or machine learning training, using parallel processing can greatly improve processing speed.

However, to perform parallel processing effectively, tasks must be properly divided and synchronized, which sometimes requires advanced programming skills.

Differences in Cores and Processing Speed

The number of cores and processing speed for CPUs and GPUs are summarized as follows.

The CPU and GPU have different characteristics, so they cannot be simply compared, but the large number of GPU cores gives an idea of its high computational power.

The CPU has a high clock speed, which determines processing speed, but a small number of cores.

In contrast, the GPU has less than half the clock speed of the CPU, but 200 to 600 times more cores.

This parallel processing capability is especially effective for tasks that require processing large amounts of data at once, such as image processing and machine learning.

In this way, the CPU and GPU have different roles and complement each other to maximize the performance of a computer.

Differences Between CPU and GPU by Use Case

Let’s look at the differences between CPUs and GPUs for various uses, from everyday tasks to specialized work.

Everyday Use

For everyday tasks such as web browsing, sending and receiving emails, and creating documents, CPU performance is the main factor.

These tasks mainly depend on single-threaded performance and overall processing speed, so the higher the CPU performance, the smoother the operation.

Especially, when working with multiple applications open at the same time, having more CPU cores and threads improves comfort.

On the other hand, since the GPU specializes in graphics processing, its performance is not as important for everyday use.

However, when playing videos or doing light image editing, the GPU does play a role, but for general use, the CPU is prioritized.

For example, when playing 4K videos or streaming high-quality video, the GPU’s hardware decoding function reduces the load and enables smooth playback.

However, these tasks can be handled by entry-level GPUs or integrated graphics, so for general use, the CPU is still more important.

Overall, for everyday use, CPU performance directly affects work efficiency, and the GPU often plays a supporting role.

Gaming

The CPU is responsible for game logic, AI behavior, physics calculations, and manages the overall flow of the game.

For example, controlling enemy character behavior, handling in-game events, and collision detection by the physics engine are mainly processed by the CPU.

In online games, the CPU also handles network communication and player synchronization, playing an important role in maintaining stable performance.

The GPU, on the other hand, specializes in graphics rendering and is necessary for displaying beautiful visuals and realistic 3D models smoothly.

Especially in the latest games, high-resolution graphics and complex shader effects require high graphics performance.

To achieve real-time lighting effects (ray tracing), high-resolution textures, and smooth frame rates, GPU processing power is directly linked to the gaming experience.

The importance of the CPU and GPU varies depending on the type of game.

For example, simulation games and MMORPGs require managing many characters and objects, so CPU performance greatly affects comfort.

On the other hand, FPS and racing games require high frame rates and smooth rendering, so GPU performance is more important.

To enjoy games comfortably, a system with a balanced CPU and GPU is necessary.

If only one is high-performance and the other is a bottleneck, sufficient performance cannot be achieved.

Therefore, it is important to combine the appropriate CPU and GPU according to the type of game and required specifications.

Image Editing

The CPU excels at general computing and plays an important role in the operation of image editing software that uses complex algorithms.

Specifically, basic functions such as file loading and saving, layer management, tool operation, and batch processing, as well as UI responsiveness, are mainly handled by the CPU.

Especially when handling high-resolution images or multiple layers, the CPU’s single-threaded and multi-core performance greatly affects work efficiency.

The GPU, on the other hand, excels at parallel processing and can quickly process large amounts of pixel data.

This speeds up graphics-related processing such as applying filters and rendering effects.

For example, advanced visual effects such as blur, sharpening, color grading, and 3D rendering are dramatically sped up by GPU acceleration.

Many image editing programs, such as Adobe Photoshop and Lightroom, use the GPU to provide smooth real-time previews and improve work efficiency.

In addition, in high-resolution display environments, operations such as zooming in and out or rotating the canvas are also smoothly handled by the GPU.

This reduces stress during work and creates a comfortable editing environment.

Therefore, when editing images, CPU performance affects the basic operation of the software, and GPU performance is directly linked to the speed and efficiency of image processing, so a balance between the two is important.

Especially for professional use, a combination of a high-performance CPU and GPU allows large projects to be handled comfortably.

It is also important to check how much the software used supports GPU acceleration for performance optimization.

Video Editing

The CPU is responsible for operating video editing software, applying effects, and encoding.

Especially since the ability to handle multiple tasks simultaneously is required, CPUs with many cores and high clock speeds are advantageous.

Specifically, the CPU plays an important role in the following tasks:

• Encoding/Decoding
  When exporting videos or converting to different formats, the CPU is mainly responsible.
  For compression formats such as H.264 and H.265 (HEVC), the CPU’s computing power directly affects performance.
• Multitasking
  For efficiently handling simultaneous tasks such as editing multiple clips, applying effects, and audio processing, multi-core performance is important.
• Overall Software Operation
  Editing on the timeline, applying transitions, and color correction are all affected by CPU performance.

The GPU, on the other hand, plays an important role in video preview and rendering.

Especially for real-time previews and applying advanced effects, the GPU’s parallel processing power is utilized.

Specifically, the GPU is active in the following processes:

• Real-Time Preview
  The GPU’s rendering performance is important for smooth playback even with high-resolution videos and multiple effects applied.
• GPU-Accelerated Effects
  There are many processes that are accelerated by the GPU, such as blur, color grading, and 3D effects.
  Especially video editing software like Adobe Premiere Pro and DaVinci Resolve greatly improve processing speed by utilizing the GPU.
• Hardware Encoding
  Recent GPUs are equipped with dedicated encoders such as NVIDIA’s NVENC and AMD’s VCE, enabling fast video exports.
  This reduces the load on the CPU and shortens encoding time.

Furthermore, for editing high-resolution videos such as 4K and 8K, GPU memory capacity (VRAM) is also important.

GPUs with large VRAM can efficiently handle large video files and multiple layers.

In video editing, a balance between CPU and GPU is important, and it is necessary to consider the performance of both rather than focusing on just one.

• Light Editing
  1080p, simple cut editing
  A high-clock CPU is sufficient.
• Moderate Editing
  4K editing, light effects
  A combination of a high-performance CPU and a mid-range or higher GPU is ideal.
• Professional Editing
  8K editing, many effects, 3D rendering
  A high-core CPU, high-end GPU, plenty of RAM, and fast storage are required.

Check if the video editing software used supports GPU acceleration and choose the optimal parts configuration for a comfortable editing environment.

Programming

The CPU is designed to efficiently perform general computing tasks, with high single-threaded performance and suitable for sequentially processing complex tasks.

For programs with complex control flow and many branches or conditions (e.g., database processing, web applications, system programming), high CPU clock speed and multi-core performance are important.

The CPU’s performance is especially utilized in the following situations:

• Logic Processing
  Implementing algorithms with many branches, complex conditions, and recursion
• Database Management
  Query processing, transaction management, integrity checks for large amounts of data
• Office Software Development
  Document processing, spreadsheet calculations, user interface control

The GPU, on the other hand, excels at processing large amounts of data in parallel, and is especially powerful in situations that require parallel computation, such as graphics processing and machine learning.

With thousands of cores, the GPU can process large amounts of data at high speed simultaneously.

Specifically, the GPU is active in the following fields:

• Graphics Processing
  3D rendering, real-time visual effects, image processing
• Machine Learning/AI
  Neural network training, large-scale matrix operations
• Scientific Computing
  Physics simulations, weather forecasting, genetic analysis
• Big Data Processing
  High-speed data analysis through parallel distributed processing

Depending on the programming application, which processor to prioritize will change.

• Database Management and Office Software Development
  For implementing complex data processing and business logic, CPU performance is important, and the need for parallel processing is relatively low.
• Web Development
  Server-side processing (backend) is CPU-dependent, but GPU may be used for 3D display or data visualization on the frontend.
• Game Development and 3D Rendering
  High-resolution graphics rendering and physics calculations require a GPU, but game logic and AI processing are also important roles for the CPU.
• Machine Learning and AI Development
  The GPU delivers overwhelming performance during the training phase, but the CPU may be sufficient for the inference phase.

To maximize programming efficiency, it is important to understand the roles of the CPU and GPU and choose appropriately.

Recently, parallel processing libraries that utilize the GPU (such as CUDA and OpenCL) have become widespread, and hybrid processing using both the CPU and GPU has become common.

Depending on the application, determining which processor contributes more to performance improvement leads to development efficiency and overall system optimization.

That said, when programming, it is common to want to try various fields, so it is a good idea to have a balanced combination of CPU and GPU.

AI (Artificial Intelligence)

The CPU is designed for general computing, excelling at processing complex logic, control flow, and single-task processing.

The CPU plays an important role in the following situations:

• AI Inference
  For lightweight models or when real-time response is needed (e.g., voice assistants, IoT devices), the CPU delivers sufficient performance.
  The CPU is good at low-latency processing and is energy efficient, making it suitable for AI inference on mobile devices and embedded systems.
• Model Preprocessing and Postprocessing
  The CPU can efficiently handle data preprocessing (cleaning, normalization) before inputting to AI models and postprocessing (analysis, display) of output results.
• Small-Scale Machine Learning
  For simple tasks that do not require complex parallel computation, such as linear regression or decision trees, the CPU alone is often sufficient.

The GPU, on the other hand, excels at processing large amounts of data in parallel, and demonstrates its performance in large-scale data processing tasks such as image processing and machine learning.

The GPU is active in the following situations:

• Deep Learning Training
  Neural network training requires massive matrix operations, and the GPU’s parallel processing power greatly speeds up training.
  Especially for training CNNs (convolutional neural networks) and RNNs (recurrent neural networks), the GPU is essential.
• Large-Scale Data Analysis
  For AI model training and inference using big data, the GPU’s memory bandwidth and parallel processing power are important for high-speed data processing.
• Real-Time Image and Video Analysis
  For processing large amounts of visual data in real time, such as autonomous driving or surveillance camera analysis, the GPU is indispensable.

Depending on the AI task, which processor to prioritize will change.

• AI Model Training
  The GPU is advantageous.
  It can process large amounts of data quickly and greatly reduce training time.
  For complex deep learning models, distributed training using multiple GPUs is also common.
  Examples: image recognition, natural language processing, large-scale speech recognition models
• AI Model Inference
  Both the CPU and GPU are used depending on the application.
  For small-scale, real-time inference, the CPU is suitable, while for batch processing or inference on large amounts of data, the GPU is better.
  Examples: face recognition on smartphones (CPU), large-scale data analysis in the cloud (GPU)

To utilize AI effectively, it is important to understand the characteristics of the CPU and GPU and make the optimal choice for the application.

The GPU is essential for AI training, and the CPU is often used efficiently for inference, but the optimal configuration depends on the environment and application requirements.

By utilizing both in a balanced way, AI performance can be maximized.

Summary: Basic Graphics Processing is Handled by the GPU, Everything Else by the CPU

This article explained the differences in roles, structure, and performance between CPUs and GPUs.

Here is a summary of the key points:

The GPU is not an essential part, but for uses that require high graphics performance, the integrated graphics function of the CPU is insufficient, so be sure to install a GPU as needed.

It is important not to focus performance on only the CPU or GPU, but to choose a balanced configuration according to the intended use.

This article also explains basic knowledge such as how to read CPU manufacturer and model numbers, performance benchmarks, and how to choose parts from the perspective of performance and compatibility.

≫ Related article: How to Choose a CPU for a Custom PC [Performance / Features / Compatibility]

あわせて読みたい

PC Parts Estimation & Compatibility Check Tool

Automatically checks each component you need when selecting parts for a custom-built PC—perfect for beginners.