What’s the difference between GPUs and CPUs?

A CPU, or central processing unit, is a hardware component that is the core computational unit in a server. It handles all types of computing tasks required for the operating system and applications to run. A graphics processing unit (GPU) is a similar hardware component but more specialized. It can more efficiently handle complex mathematical operations that run in parallel than a general CPU. While GPUs were initially created to handle graphics rendering tasks in gaming and animation, their uses now extend far beyond that.

Similarities between GPUs and CPUs

Both CPUs and graphics processing units (GPU) are hardware units that make a computer work. You can think of them as the brain of a computing device. Both of them have similar internal components, including cores, memory, and control units.


Both GPU and CPU architecture have cores that run all computations and logical functions. The core pulls instructions from memory in the form of digital signals called bits. It decodes the instructions and runs them through logical gates in a time frame called an instruction cycle. CPUs initially had a single core, but today multi-core CPUs and GPUs are common.


Both CPUs and GPUs complete millions of calculations every second and use internal memory to improve processing performance. The cache is the built-in memory that facilitates quick data access. In CPUs, the labels L1, L2, or L3 indicate cache arrangement. L1 is the fastest, and L3 is the slowest. A memory management unit (MMU) controls data movement between the CPU core, cache, and RAM in every instruction cycle.

Control unit

The control unit synchronizes processing tasks and determines the frequency of electric pulses that the processing unit generates. CPUs and GPUs with higher frequency provide better performance. However, the design and configuration of these components differ in a CPU and GPU, so the two are useful in different situations.

Key differences: CPUs vs. GPUs

The arrival of computer graphics and animation resulted in the first compute-intensive workloads that CPUs were simply not designed to handle. For example, video game animation required applications to process data to display thousands of pixels—each with their own individual color, light intensity, and movement. Geometric mathematical calculations on CPUs at the time led to performance issues.

Hardware manufacturers began to recognize that offloading common multimedia-oriented tasks could relieve the CPU and increase performance. Today, graphics processing unit (GPU) workloads handle several compute-intensive applications—like machine learning and artificial intelligence—more efficiently than CPUs.


The main difference between a CPU and GPU lies in their functions. A server cannot run without a CPU. The CPU handles all the tasks required for all software on the server to run correctly. A GPU, on the other hand, supports the CPU to perform concurrent calculations. A GPU can complete simple and repetitive tasks much faster because it can break the task down into smaller components and finish them in parallel.


GPUs excel in parallel processing through several cores or arithmetic logic units (ALU). GPU cores are less powerful than CPU cores and have less memory. While CPUs can switch between different instruction sets rapidly, a GPU simply takes a high volume of the same instructions and pushes them through at high speed. As a result, GPU functions play an important role in parallel computing.

Example of the differences

To understand better, consider the following analogy. The CPU is like a head chef in a large restaurant who has to make sure hundreds of burgers get flipped. Even if the head chef can do it personally, it’s not the best use of time. All kitchen operations may halt or slow down while the head chef is completing this simple but time-consuming task. To avoid this, the head chef can use junior assistants who flip several burgers in parallel. The GPU is more like a junior assistant with ten hands who can flip 100 burgers in 10 seconds.

When to use GPUs over CPUs

It's important to note that the choice between CPUs and graphics processing units (GPU) is not an either-or one. Every server or server instance in the cloud requires a CPU to run. However, some servers also include GPUs as additional coprocessors. Specific workloads are better suited to run on servers with GPUs that perform certain functions more efficiently. For example, GPUs can be great for floating point number calculations, graphics processing, or data pattern matching.

Here are some applications where it may be useful to use GPUs over CPUs.

Deep learning

Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way inspired by the human brain. For example, deep learning algorithms recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions. GPU-based servers provide high performance for machine learning, neural networks, and deep learning tasks.

Read about deep learning »

Read about machine learning »

Read about neural networks »

High-performance computing

The term high-performance computing refers to tasks that require very high computing power. Here are some examples:

  • You need to run geoscientific simulations and seismic processing at speed and scale
  • You need to project financial simulations to identify product portfolio risks, hedging opportunities, and more
  • You need to build predictive, real-time, or retrospective data science applications in medicine, genomics, and drug discovery

A GPU-based computer system is better suited for high-performance computing tasks like these.

Read about high-performance computing »

Autonomous vehicles

To develop and deploy advanced driver-assistance systems (ADAS) and autonomous vehicle (AV) systems, you need highly scalable computing, storage, networking, and analytics technologies. For example, you require capabilities for data collection, labeling and annotation, map development, algorithm development, simulations, and verification. Such complex workloads require the support of GPU-based computer systems to function efficiently.

Summary of differences: CPU vs. GPU



Graphics processing unit (GPU)


Generalized component that handles main processing functions of a server

Specialized component that excels at parallel computing


Designed for serial instruction processing

Designed for parallel instruction processing


Fewer, more powerful cores

More cores than CPUs, but less powerful than CPU cores

Best suited for

General purpose computing applications

High-performance computing applications

How can AWS support your CPU and GPU server requirements?

Amazon Web Services (AWS) offers Amazon Elastic Compute Cloud (Amazon EC2), the broadest and deepest compute platform. It has more than 500 instances and your choice of the latest processor, storage, networking, operating system, and purchase model to help you best match the needs of your workload.

Here are some highlights of what Amazon EC2 offers:

  • General purpose instances provide a balance of computing, memory, and networking resources. You can choose between configurations with 2-128 virtual CPUs.
  • Accelerated computing instances provide additional graphics processing unit (GPU) cores for extra computing power. You get up to eight GPUs in each instance.

Get started with server instances on AWS by creating a free account today.

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