What is a GPU? How is it Different From the CPU?

Although most people are aware of what a CPU is, a GPU’s function and importance may not be as well understood.

The goal of this article is to clarify the idea of GPUs by describing their function, as well as how they vary from CPUs in terms of processing power.

What is a GPU and CPU?

A GPU, or graphics processing unit, is a specialised electronic circuit made to quickly access memory and change it in order to speed up the production of images and graphics for display. It is generally used for tasks related to image processing, video playback, and 2D and 3D graphics rendering. A GPU is specially optimised for parallel processing activities linked to graphics, unlike a CPU, which concentrates on general-purpose processing tasks.

Contrarily, the central part of a computer system, known as the CPU, is in charge of processing commands and carrying out calculations. It functions as the computer’s brain and manages a variety of duties, such as managing memory, operating input/output devices, and carrying out intricate computations. While CPUs and GPUs are both crucial parts of modern computers, their architectures are different and were created with certain tasks in mind. CPUs are better at sequential processing, while GPUs are better at parallel computing.

Also Read: What are the differences between Intel and AMD Motherboards?

Definition of GPU: its purpose and function

A GPU (graphics processing unit) focuses on handling complex mathematical computations needed for rendering graphics-intensive applications such as video games, virtual reality simulations, and data visualisation. In contrast, a CPU (central processing unit), is responsible for executing general-purpose instructions and managing various tasks.

A GPU’s main job is to handle massive amounts of data at once by breaking them up into smaller pieces and spreading them among thousands of cores. These cores operate in parallel to complete complicated calculations at a breakneck speed. Smoother frame rates, more lifelike visual effects, and overall better performance are all benefits of offloading graphical processing chores from the CPU to the GPU in graphics-intensive programmes.

In addition, GPUs have been specifically designed to handle the repetitive calculations required for rendering graphics using methods like matrix multiplication and shader programmes. Because of their specialisation in computing efficiency, GPUs are very useful tools for a variety of compute-intensive jobs that call for a lot of processing capacity, including scientific simulations, machine learning algorithms, cryptocurrency mining operations, and others.

Definition of CPU: its purpose and function

A CPU’s main function is data processing, which it accomplishes through a variety of processes, including arithmetic computations, logical comparisons, and data transportation. It retrieves commands from memory, decodes them into microoperations, and then executes these microoperations in its control unit. The CPU is also in charge of controlling data input and output between peripherals and memory.

The CPU has a wider variety of capabilities beyond only processing graphics, in contrast to a GPU (Graphics Processing Unit), which is specialised in producing graphics and images efficiently for gaming and other graphics-intensive applications. While both CPUs and GPUs have distinct designs that are optimised for certain tasks, CPUs are built to efficiently handle general-purpose computing activities.

Also Read: Unlocking the Future of Computing: What Makes Ryzen CPUs Special?

Differences between GPU and CPU

The architecture of GPUs and CPUs is one important distinction. A CPU normally has 2 to 64 cores, although each core is capable of simultaneously processing numerous instructions. A modern GPU, on the other hand, can have thousands or even hundreds of tiny cores that are excellent at parallel processing. Because of their ability to process enormous volumes of data concurrently, GPUs are perfect for computationally demanding activities like machine learning algorithms.

The memory architecture of GPUs and CPUs is another difference. GPUs have bigger global memory regions shared by all cores, but CPUs often have many layers (L1, L2, L3) of cache memory near to the cores for quick access to frequently used data. This makes it easier for various threads running concurrently on the GPU to exchange information quickly.

Overall, GPUs and CPUs differ greatly in terms of architecture and purpose, although having certain practical similarities. GPUs are vital tools for boosting computationally heavy operations and graphics-intensive applications that require high-speed data processing capabilities beyond what conventional CPUs can deliver.

Various Types of GPUs

There are several different types of GPUs available in the market, each designed for specific purposes. Here are some commonly known types:

1. Integrated Graphics Processing Unit (iGPU): This type of GPU is integrated into the computer’s motherboard or CPU. It is generally less powerful than dedicated GPUs but sufficient for everyday tasks like web browsing, video playback, and basic gaming.

2. Dedicated Graphics Processing Unit (dGPU): These GPUs are separate cards that can be added to a computer’s motherboard. They are more powerful than integrated graphics and are designed for demanding tasks like gaming, video editing, and graphics-intensive applications.

3. Gaming GPUs: These GPUs are designed exclusively for gaming, providing great performance and cutting-edge features that are geared towards delivering immersive gaming experiences. The AMD Radeon RX series and the NVIDIA GeForce RTX series are two examples.

4. Workstation/Professional GPUs: These GPUs are designed for professional workloads such as 3D modelling, animation, CAD/CAM, scientific simulations, and video editing. They offer superior precision, stability, and support of specialized software libraries required in professional applications. Examples include the NVIDIA Quadro series and the AMD Radeon Pro series.

5. Mobile GPUs: These GPUs are specifically designed to meet the power consumption constraints of mobile devices such as smartphones and tablets while still providing decent graphics performance.

6. AI/Deep Learning Accelerators: With the rise in artificial intelligence and deep learning applications, specialized GPUs have been developed to handle complex calculations required by these workloads efficiently. Examples include NVIDIA Tesla GPU accelerators and Google TPU (Tensor Processing Units).

7. External Graphics Card: Also known as eGPUs or external GPU enclosures, these allow users to connect a dedicated GPU externally to their laptops or computers via Thunderbolt or USB ports. They provide increased graphics processing capabilities to systems with limited internal GPU power.

It’s important to note that within each type of GPU mentioned above, there are further variations based on different models, architectures, and performance levels.

Importance of GPUs in computing

It is impossible to exaggerate the significance of GPUs in computing. GPUs have evolved into essential tools for researchers and developers alike with the rise of advanced technologies like artificial intelligence (AI) and machine learning (ML), which demand enormous quantities of data processing capacity. Additionally, the need for realistic graphics in video games has compelled GPU makers to keep enhancing the performance of their products. As a result, for gamers who wish to enjoy visually appealing games with smooth framerates, having a strong GPU has become essential.

Overall, GPUs have revolutionised modern computing by enabling quicker data processing across numerous industries thanks to their distinctive architecture specialised for parallel computing workloads and their capacity to execute complicated computations efficiently.


In conclusion, GPUs excel in their capacity to handle highly parallel workloads effectively, while CPUs remain crucial for overall system operation and carrying out complicated tasks with the utmost precision and variety. Training procedures can be greatly expedited by utilising their enormous computational capacity and specialised architecture designed for parallel processing activities like graphics rendering or machine learning algorithms. Users can decide what they need from their computers and how best to use resources when they are aware of the differences between these two components.

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Raj Maurya

Raj Maurya is the founder of Digital Gyan. He is a technical content writer on Fiverr and When not working, he plays Valorant.

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