CPU vs. GPU: What's the Difference?

The primary difference between a central processing unit and a graphics processing unit is that the CPU handles all of the main functions of a computer, while the GPU is a specialized component that excels at running many smaller tasks at once. The CPU and GPU are both essential, silicon-based microprocessors for modern computers — alike in some ways and distinct in others.

Here’s what you need to know about these fundamental components.

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The Central Processing Unit, often compared to the “brains” of a device, the CPU is a silicon chip that is attached to a socket on the motherboard. The CPU is responsible for everything you can do on a computer, executing commands for programs from your system’s memory via billions of microscopic transistors, with instructions from software. It’s like having billions of on-off switches that control the flow of electricity, translating tasks into zeros and ones. For example, in a gaming PC, the CPU handles the core functions of a game to make sure it runs smoothly.

The core functionality of the CPU is to fetch instructions from random access memory, or RAM, and then decode and execute the instructions. CPUs run processes serially, meaning one after the other.

Most modern processors are capable of anywhere from 1 billion to 5 billion operations per second. Usually, memory is accessed from RAM in order, but it can be fetched out of order (hence the “random” in random access memory).

Here's a rundown of the CPU’s basic functions:

1. Fetch: The CPU sends an address to RAM (or other program memory) and retrieves an instruction — a number or series of numbers, a letter, an address or other piece of data — which it then processes. Within these instructions from RAM are a number or numbers representing the next instruction to be fetched.
2. Decode: Once the CPU has data, it has an instruction set with which it can act upon the data. Some of the more common instructions include loading a number from RAM; adding numbers together; processing logical functions (such as Boolean logic); storing a number from the CPU back to RAM; receiving device input or outputting data to a device; comparing numbers; or jumping to a RAM address.
3. Execute: Finally, the instruction is passed to the instruction decoder, which converts the instruction into electrical signals sent to various parts of the CPU to be acted upon. The process begins again once the next instruction is fetched.

CPUs have a few universal features. For a deeper look, check out our guide on how to choose a CPU.

• Cores: A core is basically a CPU’s processor. Back in computing antiquity, processors had just one core. Now, computers commonly have anywhere from two to 64 cores. The more cores a CPU has, the better its performance and efficiency.
• Simultaneous multithreading/hyperthreading: With simultaneous multithreading (known as hyperthreading in Intel processors), processing is delegated to multiple software threads rather than given to a single core. This allows more work to be done simultaneously, effectively turning one core into two “logical” cores.
• Cache: CPUs have their own ultrafast memory built in, which is faster than any RAM or type of solid-state drive. The CPU cache is arranged from Level 1 to Level 3, with Level 1 being the fastest; that’s where the CPU stores information it needs quickly.
• Memory management unit: The MMU is responsible for all memory and caching operations. Typically integrated into the CPU, the MMU acts as the middleman between the CPU and RAM during the fetch-decode-execute cycle, shuttling data back and forth as necessary. It also translates virtual addresses provided by software to physical addresses used by RAM.
• Control unit: The control unit orchestrates the operations of the CPU. It tells the RAM, logic unit and input/output devices how to act according to the instructions received.

The Graphics Processing Unit, also known as a graphics card or video card , is the computer component that renders images and video. It is either independent of the motherboard (discrete) and has its own memory (vRAM), or it’s soldered to it (integrated) and shares memory with the CPU. Typically, integrated GPUs have lower performance than discrete GPUs because they are smaller and share resources with the CPU.

As computers are tasked to run more 3D graphics and other processing-intensive workloads (not to mention the increasing use of artificial intelligence), users may find that having the CPU handle everything reduces performance. A dedicated microprocessor can handle some of the burden to address these performance issues. In this regard, a GPU is like a specialized CPU and is particularly well suited for multitasking. In fact, CPUs used to do the job that GPUs do today.

Rather than processing tasks serially, as a CPU does, a GPU breaks up tasks and runs them in parallel. GPUs have many more cores than CPUs, although they are smaller. With the additional cores, GPUs can handle more mathematical and graphical calculations at once with greater efficiency, while CPUs, as general-purpose components, are restricted.

These attributes make GPUs particularly good for gaming applications, especially for games with demanding graphics. Many games have graphics with detailed textures and sophisticated lighting effects, which a GPU can render quickly and accurately. GPUs can also support higher frame rates, which contribute to a better gaming experience.

What's the difference between integrated and discrete GPUs?

An integrated GPU is built into a processor and shares memory with the CPU, reducing power consumption and heat generation. A discrete GPU has dedicated memory, enabling it to better handle graphically intensive tasks.

Can a computer run without a GPU?

A computer may have some limited capabilities without a GPU, but modern operating systems require a video card to run, making GPUs a necessity.

What is the lifespan of a CPU and a GPU?

Most CPUs and GPUs are built to last several years when they are properly cooled and used under normal conditions.

What is GPU overclocking, and is it safe?

Overclocking refers to increasing the operating speed of a GPU beyond its default settings. This practice improves the performance of graphics-intensive tasks such as video game frame rates, but it can produce more heat and thereby shorten the lifespan of the GPU.

How does the number of cores in a GPU compare with a CPU?

Generally, a GPU will have a much greater number of cores than a CPU — thousands versus dozens in a CPU.

• CPUs work well for a variety of tasks that involve arithmetic or logical operations as well as data manipulation and complex logic. This includes tasks such as data encryption or operating applications.
• CPUs can perform multiple tasks simultaneously, such as running applications and carrying out general computing operations, without adversely affecting performance of.
• CPUs are significantly less expensive than GPUs and generally have lower maintenance and energy costs.
• CPUs can run effectively under different operating systems (including Microsoft Windows and macOS), programming languages and platforms.

• GPUs can perform a large number of simple tasks in parallel, making them useful for processing large data volumes at high speed. GPUs are well suited for operations such as image processing and machine learning.
• The parallel architecture of GPUs provides greater scalability than CPUs, making it relatively easy to improve performance by adding more GPU cores.
• The high data throughput and low latency of GPUs enables them to provide the acceleration needed for specialized use cases such as deep learning and genomic sequencing.

Cryptocurrency mining: GPUs can generate an immense number of hashes per second, offering significant advantages over CPUs for mining cryptocurrency. By processing instructions in parallel with a greater number of cores, GPUs enable improved output for crypto mining. GPUs also use less power, improving the efficiency of mining operations. To protect against hackers mining cryptocurrency on their networks, organizations should have a plan to prevent cryptojacking.

Machine learning and AI: Because GPUs can process large quantities of data with great speed and efficiency, they have become essential for machine learning and AI deployments. The parallel-processing capabilities of GPUs make them better for AI-powered tasks such as speech recognition and natural language processing.

Data centers: The use of GPUs in data centers delivers several advantages, including improved energy efficiency and scalability. Because they can process multiple computations in parallel, GPUs are ideal for handling complex workloads. They also can scale easily to add computational power and operate more efficiently to reduce operational costs.

High-performance computing The power and efficiency of GPUs also make them appropriate for high-performance computing workloads. They can process data more rapidly and handle complex calculations better than CPUs.

Autonomous vehicles GPUs have become essential to the development of self-driving vehicles. Vehicle manufacturers use GPUs to run complex simulations, power perception systems, and handle real-time decision-making.

Some computing devices include a GPU integrated with the CPU on the motherboard. This enables GPU capabilities in devices with a smaller form factor, such as laptops, tablets, and smartphones, as well as some desktop computers.

While integrated GPUs may not offer the same level of performance as discrete GPUs, they do provide energy and cost savings. This makes them suitable for light gaming as well as less-demanding tasks such as video editing and streaming media.

If you want to learn more about vital computer components, be sure to check out our related article on CPU vs. RAM. CDW sells everything you need to build your own PC, or you can choose from among the best brands in personal computing, including Apple, Dell, Lenovo, HP, and more.