CPU vs. GPU: Understanding the Key Differences

While a CPU is the brains of a computer, GPU is its soul. For decades, CPU remained the most researched computer component. The silicon chip went through multiple iterations, exponentially increasing its capability. It was only in the last decade that GPU broke out of the shadows and ignited a worldwide AI boom.

As GPU took a central stage in modern super-computing, it became widely employed to speed up tasks from networking to gaming and encryption to AI. Today, both CPU and GPU are considered essential factors for a computing task. That’s why the best CPU and GPU combos are driving advances in gaming machines, professional workstations, smaller desktop PCs, and the latest generations of laptops.
In this article, we are taking a look at their key differences.

In this article, we are taking a look at their key differences.

What is a CPU?

A CPU (Central Processing Unit) is the computer’s core processing component. It defines a computing device but works alongside other hardware. The processing chip sits in a specific socket on the motherboard. A regular CPU is separate from the memory as it cannot store information. It merely processes all the information inside the memory. A CPU is built by placing hundreds of millions of microscopic transistors into a single chip.

The advancement of CPU technology today deals with making these transistors smaller and improving the CPU speed. In fact, according to Moore’s law, the number of transistors on a chip effectively doubles every two years. Modern devices, such as mobile phones and tablets, utilize a special System on Chip (SoC) that packages CPU with graphics and memory components. Therefore, they can do more than a CPU’s standard functions.

What is a GPU?

A GPU (Graphics Processing Unit) is a specialized CPU designed to manipulate memory and accelerate the performance of a computer for several tasks. It has a much higher number of ALUs than the CPU. Thus, breaking down complex problems into thousands of separate tasks and solving them simultaneously. As for the architecture, a GPU’s internal memory is a point-to-point connection, while accelerating memory throughput and the amount of data it can process.

A GPU uses thousands of cores with instruction sets optimized for floating-point and arithmetic calculations. This makes a GPU much faster with linear algebra and similar jobs requiring a higher degree of parallelism. Therefore, GPUs are considered the core component responsible for graphics. The rendering of shapes, textures, and lighting has to be completed at once to keep the images moving across the display.

GPU vs. CPU: A Look at their Differences

As you must have noticed by the discussion above, there is a considerable difference between the two components and how they work. Let’s take their differences in detail so that it’s easy for you to decide whether you need them both for your setup or not.


Although a GPU has more cores than a CPU, they are less powerful in sheer clock speed. Normally, a GPU’s clock speed ranges from 500 to 800 MHz with denser cores on a single chip. Conversely, CPUs today can go as fast as 3.5 to 4 GHz. GPUs are also less versatile as they have limited instruction sets. You can go with 24 to 48 superfast CPU cores in a server environment, but adding just 4 to 8 GPUs can offer 40,000 additional cores. This way, the sheer number of GPU cores and massive parallelism they bring to the table can make up for less powerful, less versatile, and less smart cores.


The GPU RAM is dedicated memory. It is a much wider interface with short paths and a P2P connection. That’s why it runs a much higher clock speed than a CPU memory. GPU memory can deliver up to several hundred GB per second to the GPU. The CPU RAM is a system memory. It is mostly 2 DIMM wide and has a multi-drop bus. Therefore, it needs more power to drive even when it’s running at lower clock speeds. CPU memory delivers in the mid-tens of GB per second. However, several latest CPUs use wider interfaces to deliver up to 100 GB of data per second. As for the internal design, both of these kinds of memory are very similar to each other.

Instruction Sets

A GPU can work with a much bigger and complex instruction set. CPU, on the other hand, has a limited instruction set. Although many CPU chipset manufacturers are now trying to embed increasingly complex instruction sets into their CPU architectures, the technology isn’t there yet. Doing so has several disadvantages. For instance, a CPU has to spin through thousands of clock cycles when working with complex instructions. Intel recently integrated some instruction-level parallelism to its newest chips to smoothen the process. However, it impedes overall CPU performance.

Context Switch Time

The Context Switch Time or Context Switch latency, in simple terms, is the amount of time it takes for a processing unit to execute a process. A CPU is relatively slow when it comes to switching between multiple threads. The reason being, it has to store information in registers. Restoring this information when needed, flush the cache and perform other clean-up operations at the same time that consume a big chunk of its resources. While modern processing chips try to overcome this problem by utilizing task state segments, the context switching remains slow. However, there is no inter-warp context switching in GPU, at least in the traditional sense of the word. They typically execute only one task at a time.

Hardware Limitations

Moore’s Law, the notion that the number of transistors per inch of a silicon chip doubles every two years is nearing its end. After all, you cannot just continue adding transistors on a piece of silicon. There is a hardware limit that is impossible to cross because of the simple laws of physics. This hardware limitation is a major roadblock for CPU manufacturers. Sure, they are now trying to overcome it with the help of distributed computing, quantum computers, and silicon replacements. However, how that goes is anyone’s guess. A GPU, on the other hand, has no such limitations. In fact, Huang’s Law, in contrast to Moore’s Law, predicts that the performance of GPUs will more than double every two years. As per Jensen Huang, CEO of Nvidia, “The innovation isn’t just about chips, anymore. It’s about the entire stack.”

API Limitations

GPUs also have very limited graphics APIs. Besides, they are hard to debug, which further limits their applications. The two most popular graphics rendering APIs, CUDA and OpenCL, are notorious in this regard. While OpenCL is open-source, it only works well with AMD hardware and is very slow on Nvidia. On the other hand, CUDA comes factory optimized for NVidia. Still, it locks you in their ecosystem, making a change impossible in the future. In comparison, there is no such API limitation on the CPUs of different manufacturers. Data APIs work flawlessly with the CPU, never hindering your work progress.

CPU vs. GPU Differences in a Nutshell

Central Processing Unit of Computer Graphics Processing Unit of Computer
Features Multiple Cores Features Thousands of Cores
Low Latency component High Throughput component
Excellent for Serial Processing Excellent for Parallel Processing
Has a Cache of its own NO Cache
Hardware limitations, No API limitations API limitations, No hardware limitations
Fewer Cores, more clock speed More Cores, less clock speed


Both CPU and GPU serve in different domains of computer processing. Both have different spheres of excellence, as well as limitations. Knowing each component helps you better optimize your hardware for whatever project you want to work on. Besides, it can help you avoid the dreaded CPU GPU bottleneck. We hope the information provided in this article will serve as a guide in the future. LinuxHint is an online resource for everything related to computers and Linux in particular. Be sure to check related articles for more information. Thank you for reading!

Frequently Asked Questions (FAQs)

Is CPU better or GPU?

The answer to this question depends on the applications you want to run on your system. If you do a lot of video rendering, gaming, and other graphics-intensive work, investing in a better GPU will be the right decision. However, get a better CPU if you are only using your computer for routine office work, internet browsing, and video streaming. You may not need a GPU at all.

GPU vs. CPU: What matters most for gaming?

Well, it depends on what kind of games you play. If you’re into fast-paced games like first-person shooters, such as CoD, Overwatch, or real-time strategy video games like The Age of Empires and Blades of the Shogun, or MMORPGs like Elder Scrolls and World of Warcraft, then we suggest upgrading your CPU first. However, get a better GPU if you like open-world online video games, such as GTA 5, Witcher 3, or Red Dead Redemption 2 with highly defined and immersive environments.

What is the best CPU GPU?

It depends on your application and uses. We talked in detail about the best CPU GPU Combos in our article. You can find it in our “Related Linux Hint Posts” section on the top left corner of this page.

References Used In This Article

1. What is GPU Computing

About the author

Syed Asad

Asad is passionate about all things tech. He brings you reviews of the latest gadgets, devices, and computers