CPU Vs. GPU: A Comprehensive Overview

December 9, 2021


With the growing popularity of fields such as deep learning, 3D modeling/rendering, VR gaming, and crypto mining, modern computing requirements have skyrocketed. The hardware components in charge of providing the computing power evolved in response to the demand. This evolution reached the point where sometimes it is difficult to differentiate between their roles in the computer system.

This article will provide a comprehensive comparison between the two main computing engines - the CPU and the GPU.

CPU vs. GPU: A Comprehensive Overview

CPU Vs. GPU: Overview

Below is an overview of the main points of comparison between the CPU and the GPU.

A smaller number of larger cores (up to 24)A larger number (thousands) of smaller cores
Low latencyHigh throughput
Optimized for serial processingOptimized for parallel processing
Designed for running complex programsDesigned for simple and repetitive calculations
Performs fewer instructions per clockPerforms more instructions per clock
Automatic cache managementAllows for manual memory management
Cost-efficient for smaller workloadsCost-efficient for bigger workloads

What Is a CPU? 

The CPU (Central Processing Unit) or the main processor executes computing instructions. Attached to the motherboard via a CPU socket, the CPU listens for input from a computer program or a peripheral such as a keyboard, mouse, or touchpad. It then interprets and processes the input and sends the resulting output to peripherals or stores it in the memory.

Note: Dual-Processor Servers are still around - check out the differences between Single Processor vs. Dual Processor Servers.

What Is a GPU? 

The GPU (Graphics Processing Unit) is a specialized graphics processor designed to be able to process thousands of operations simultaneously. Demanding 3D applications require parallel texture, mash, and light processing to keep images moving smoothly across the screen, and the CPU architecture is not optimized for those tasks. The original purpose of GPUs was to accelerate graphics rendering.

Difference Between CPU and GPU

Even though they both are silicon-based processing chips, CPUs and GPUs significantly differ in architecture and application.

CPU Vs. GPU Architecture

The CPU consists of billions of transistors connected to create logic gates, which are then connected into functional blocks. On a larger scale, the CPU has three main components:

  • Arithmetic and Logic Unit (ALU) comprises of circuits that perform arithmetic and logic operations.
  • The Control Unit fetches instructions from the input and forwards them to ALUs, Cache, RAM, or peripherals.
  • Cache stores intermediate values necessary for ALU computations or helps with keeping track of subroutines and functions in the program that is being executed.

CPUs can have multiple cores with their own ALUs, control units, and cache.

A diagram representing the CPU architecture.

The GPU consists of similar components, but it features a much larger number of smaller, specialized cores. The purpose of multiple cores is to enable the GPU to perform multiple parallel computing operations.

A diagram representing the GPU architecture.

CPU Vs. GPU Rendering

GPUs were primarily created for graphics manipulation, which explains why they are so superior to CPUs in rendering speed. Depending on the quality of individual pieces of hardware, GPU rendering can be up to a hundred times faster than CPU rendering.

However, a good rendering experience depends not only on speed. For example, working with 3D visuals requires performing multiple complex tasks while keeping the data in sync. Designed for complexity, CPUs tend to outperform GPUs in 3D rendering since GPUs are designed to perform simpler and more straightforward tasks.

Furthermore, GPUs are limited to their graphics card memory (usually up to 12 GB), which does not stack and cannot be easily expanded without causing bottlenecking and damaging the performance. The CPU uses the main system memory, which is easily expandable and goes up to 64 GB.

Note: GPUs are also a more flexible and economical solution, offering better value for money. By employing GPU rendering, freelance artists and designers can achieve excellent quality results cheaper than if they resorted to hiring CPU rendering farms.

CPU Cache Vs. GPU Cache

The CPU uses cache to save time and energy needed to retrieve data from the memory. The cache is designed to be smaller, faster, and closer to the other CPU components than the main memory.

The CPU cache comprises of multiple layers. The level closest to the core is used only by that core, while the furthest layer is shared between all CPU cores. Modern CPUs perform cache management automatically. Each layer decides if the piece of memory should be kept or evicted based on the frequency of usage.

The GPU local memory is structurally similar to the CPU cache. However, the most important difference is that the GPU memory features non-uniform memory access architecture. It allows programmers to decide which memory pieces to keep in the GPU memory and which to evict, allowing better memory optimization.

CPU Vs. GPU Deep Learning

Deep learning is a field in which GPUs perform significantly better than CPUs. The following are the important factors contributing to the popularity of GPU servers in deep learning:

  • Memory bandwidth - The original purpose of GPUs was to accelerate the 3D rendering of textures and polygons, so they were designed to handle large datasets. The cache is too small to store the amount of data that a GPU repeatedly processes, so GPUs feature wider and faster memory buses.
  • Large datasets - Deep learning models require large datasets. The efficiency of GPUs in handling memory-heavy computations makes them a logical choice.
  • Parallelism - GPUs use thread parallelism to solve the latency problem caused by the size of the data - the simultaneous use of multiple processing threads.
  • Cost efficiency - Large neural network workloads require a lot of hardware power. For this purpose, GPU-based systems offer significantly more resources for less money.

Note: Learn more about the benefits of GPUs in deep learning by reading How GPU Computing is Advancing Deep Learning.

CPU Vs. GPU Mining

While GPU mining tends to be more expensive, GPUs have a higher hash rate than CPUs. GPUs execute up to 800 times more instructions per clock than CPUs, making them more efficient in solving the complex mathematical problems required for mining. GPUs are also more energy-efficient and easier to maintain.

How CPU and GPU Work Together?

When comparing the two, it is important to understand that GPUs were designed to complement CPUs, not to replace them. The CPU and the GPU work together to increase the amount and speed of processed data.

A GPU cannot replace a CPU in a computer system. The CPU is necessary to oversee the execution of tasks on the system. However, the CPU can delegate specific repetitive workloads to the GPU and free its own resources necessary for maintaining the stability of the system and the programs that are running.


After reading this comparison article, you have a better understanding of the similarities and differences between CPUs and GPUs. The article dealt with architectural differences between the two processing units and compared their performance in popular usage scenarios.

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Marko Aleksic
Marko Aleksić is a Technical Writer at phoenixNAP. His innate curiosity regarding all things IT, combined with over a decade long background in writing, teaching and working in IT-related fields, led him to technical writing, where he has an opportunity to employ his skills and make technology less daunting to everyone.
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