The CPU and GPU are essential components of laptop hardware. Before exploring their differences, let’s first understand what hardware is and its components.

The Components of a Computer

The components of a computer are hardware and software.

What is Hardware?

Hardware refers to the physical devices, both internal and external, of a computer. It includes all the parts you can touch, such as the keyboard, mouse, and screen, as well as internal components like the motherboard, CPU, and RAM.

What is Software?

Software, on the other hand, is intangible part of a computer , a set of instructions that tell the hardware what to do and the hardware makes it happen. in other words, It is what you interact with on the screen. Software includes operating systems like Windows or macOS, applications such as web browsers, development tools…

How Hardware Components Work Together

When you turn on your computer, the power supply (which provides electricity) activates all hardware components. The BIOS/UEFI (firmware stored on the motherboard), which is triggered by the ROM (Read-Only Memory – a chip that stores permanent startup instructions and firmware needed to boot the computer), performs a Power On Self Test (POST – checks if all hardware components are working properly).

Once the system confirms everything is functional, it hands control to the boot device (usually the storage drive which contains the operating system).

The boot loader (in the boot device) starts the operating system. The CPU (Central Processing Unit – the brain that processes instructions) then takes over, executing commands and running programs. When you open an application, the CPU retrieves data from the storage drive and loads it into RAM (Random Access Memory – temporarily holds active data for quick access). As you use the application, the CPU continues processing instructions while RAM temporarily stores active tasks. When you close the application, the CPU ensures necessary data is saved to storage (HDD/SSD – where data and the operating system are stored), while unused data is cleared from RAM to free up space for future tasks.

After gaining a general understanding of hardware components, let’s take a deeper look at what a CPU and a GPU are.

What is the CPU?

The Central Processing Unit (CPU) is the brain of the computer. It handles all types of computing tasks required for the operating system and applications to run. The CPU is designed for sequential processing, meaning it excels at executing tasks one after another efficiently.

Example: When you type in a document or browse the internet, the CPU processes these tasks.

What is the GPU?

The Graphics Processing Unit (GPU) is a specialized processor designed for parallel computing. It can handle complex mathematical calculations much more efficiently than a CPU, making it ideal for graphics rendering, video processing, and artificial intelligence tasks.

There are two main types of GPUs: integrated and dedicated. An integrated GPU is built into the CPU and shares resources with the system, offering basic graphics performance (Not all computers have an integrated GPU). A dedicated GPU, on the other hand, is a separate unit that provides higher performance for demanding tasks like gaming and video editing.

Example: When you play a video game, the GPU processes the game’s graphics, rendering thousands of pixels at once for a smooth experience.

It’s important to mention that a laptop cannot function without a CPU! Even though a GPU is needed for complex computations, it is not a primary component. The CPU remains the core and essential part, responsible for running the operating system and other crucial tasks, as previously mentioned.

CPU vs. GPU: What’s the Difference?

CPUs and GPUs have similar components, including cores, memory, and control units, but they function differently.

Core

A core is a processing unit within the CPU or GPU that executes instructions. The more cores a processor has, the more tasks it can handle simultaneously. For example, most modern CPUs typically have between 4 to 16 cores, while high-performance models can have up to 64 cores. In contrast, GPUs have thousands of smaller cores optimized for parallel processing.

The CPU has fewer, more powerful cores optimized for sequential tasks (performing one task at a time very quickly).

The GPU has many smaller cores designed for parallel processing, making it ideal for tasks that require repetitive, parallel computations, like 3D rendering or deep learning.

Figure 1 illustrates the difference in the number of cores between the CPU and GPU. It also highlights an essential component within each core called the ALU, which is responsible for mathematical calculations. The ALU, or Arithmetic Logic Unit, is an internal circuit within the CPU that performs arithmetic operations—such as addition, subtraction, multiplication, and division—as well as logical operations like AND, OR, and NOT.

Memory

Memory stores and quickly accesses data while the processor runs applications. Different types of memory serve different purposes:

CPU memory

The CPU uses cache memory, which is a small, high-speed memory located inside the CPU that stores frequently accessed data and instructions. It reduces the time needed to fetch data from the main memory (RAM), thus improving processing efficiency. There are three levels of cache memory:

• L1 Cache (Level 1) → Fastest but Smallest (Few KBs, per core)
• L2 Cache (Level 2) → Larger but Slower than L1 (Few MBs, per core)
• L3 Cache (Level 3) → Largest but Slowest Cache (Tens of MBs, shared across all cores)

The three levels of cache memory (L1, L2, L3), and sometimes L4, provide varying speeds and capacities.

Example: When you open an Excel spreadsheet, the CPU checks multiple memory caches to fetch the required data quickly. The CPU first checks L1 Cache to see if the data (e.g., cell values or formulas you recently accessed) is already stored there. Since L1 is the fastest and closest to the CPU core, fetching from L1 is almost instantaneous. If the required data isn’t there, the CPU moves to L2. If not found in L2, the CPU checks L3 Cache. If found here, the CPU fetches the data and stores it in L1/L2 for faster access next time. If the data isn’t found in L3, some high-performance CPUs check L4 Cache (if it exists).

If the data isn’t in any cache, the CPU must fetch it from RAM (Random Access Memory).

GPU Memory: VRAM vs. Shared Memory

The GPU relies on high-speed memory to handle large data loads efficiently, especially for rendering graphics and AI computations. Dedicated GPUs (dGPUs) use VRAM (Video Random Access Memory)—a specialized type of memory separate from system RAM—to store image data, textures, and graphical assets. This dedicated memory allows fast rendering of high-resolution images, videos, and 3D models without lag, making it essential for gaming, video editing, and professional graphics applications.

However, integrated GPUs (iGPUs) do not have dedicated VRAM. Instead, they borrow system RAM, meaning they share memory with the CPU. Since system RAM is slower than VRAM, integrated GPUs generally offer lower performance for graphics-intensive tasks.

Control Unit

The control unit is responsible for managing the execution of instructions and coordinating different components of the processor.

• The CPU’s control unit directs data flow, ensures instructions are executed in the correct order, and manages system resources.
• The GPU’s control unit optimizes massive workloads by breaking them into smaller tasks and running them in parallel to enhance performance.

When to Use GPUs Over CPUs

While every computer requires a CPU to function, some workloads benefit more from a GPU, including:

• Deep Learning & AI: GPUs excel in training neural networks and handling large datasets for artificial intelligence.
• High-Performance Computing (HPC): Tasks such as scientific simulations, financial modeling, and medical research require parallel processing capabilities.
• Graphics & Video Processing: GPUs render high-quality graphics for gaming, video editing, and animation.
• Autonomous Vehicles: Self-driving cars rely on GPUs to process real-time data and make driving decisions efficiently.

This was a brief overview of hardware components, GPU, and CPU. More articles in depth are coming.

If you have any questions, leave them in the comment section below.