GPU Full Form: What it means and Why it Matters?

Table of contents

What is a GPU(Graphics Processing Unit)? The role of GPUs in modern computing How does a graphics processing unit work? Types of GPUs GPU benchmarks Modern applications of GPU Difference between GPU and CPU Difference between GPU and graphics card Difference between GPU, NPU, and FPG Experience advanced GPU power with ASUS Frequently asked questions

 

Quick read Graphics Processing Unit - An electronic circuit designed for image processing and graphics rendering on laptops, gaming consoles, servers, embedded systems, desktops, and mobile phones. Key Features - High-speed processing, parallel processing, and energy efficiency. Function - GPUs have thousands of small processing units to handle parallel processing tasks. Types - Integrated GPUs, Discrete GPUs, and Virtual GPUs. GPU Benchmarks - Synthetic and real-world benchmarks. Modern Applications of GPU - Gaming, video editing and content creation, artificial intelligence, deep learning and machine learning, high-performance computing, simulation and blockchain.

Looking for a gaming laptop or a powerhouse for content creation? The first thing that truly matters is a GPU. It is the brain behind stunning images, visual displays, speed and performance. 

But what exactly is a GPU, how does it function, and how is it different from other components in your computer? 

In this blog, we will break down the basics of GPUs and walk you through their key features and applications. 

What is a GPU(Graphics Processing Unit)?

A Graphics Processing Unit(GPU) is an electronic circuit designed for image processing and graphics rendering on devices, such as mobile phones and computers. Beyond graphics, modern GPUs are also designed for advanced computing tasks, such as artificial intelligence (AI), parallel computing, and simulations.  

Images and graphics are displayed based on complex mathematical calculations, and a GPU can perform these calculations at extremely high speeds.

Unlike a regular processor, a graphics processing unit can execute the same mathematical operations on multiple data values simultaneously. This parallel processing enhances the efficiency and performance for delivering smoother visuals.

The role of GPUs in modern computing

Before the advent of GPUs, graphics controllers were primarily designed to process displays on the screen. They relied heavily on the CPU (Central Processing Unit) to carry out these tasks.

With the rise of computer-aided designing and gaming in the late 1990s, the industry demanded a more powerful technology to handle intensive graphics processing, and the GPU was discovered. 

In 2007, NVIDIA launched CUDA (Compute Unified Device Architecture), giving developers access to the GPU’s key features, such as parallel processing, high-speed computation and energy efficiency for smoother visuals. 

Since then, GPU graphics processing has undergone rapid advancements with various applications in modern computing. From rendering 3D graphics, supporting advanced software for video creation and powering virtual reality, their use has expanded widely. 

Today, they also play a critical role in artificial intelligence and machine learning. 

How does a graphics processing unit work?

A graphics processing unit breaks down complex tasks into smaller tasks and processes them in parallel with the help of its core components:

• Cores 

A GPU is built with thousands of small processing units called cores. These cores work together to handle multiple calculations in parallel. NVIDIA uses CUDA cores and AMD uses stream processors. 

• VRAM (Video Random Access Memory) 

GPUs have dedicated memory known as VRAM. It stores the graphical data for quick access. The memory type and memory bandwidth are key factors that impact the GPU graphics processing unit.

The type of memory is critical to GPU performance. GDDR7 (Graphics Double Data Rate 7) is one of the recent standards used in graphics processing. It has a higher bandwidth, which means it can transfer larger volumes of data in parallel. GDDR7 memory is used in NVIDIA RTX 5000 series GPUs.

• GPU Die 

It is the heart of the system that contains the GPU’s cores, electronic circuits, and millions of transistors. The design and size of the GPU Die determine the overall performance of the GPU. The number of transistors directly influences the GPU’s processing capabilities. The higher the number of transistors, the greater the parallel processing power. 

The specifications of some of the popular GPUs are as follows:

GPU	Process Size (nm)	Die Size (mm2)	Number of Transistors (million)
NVIDIA GeForce GTX 1080 Ti	16	471	11,800
NVIDIA GeForce RTX 5090	5	750	92,200

 

Types of GPUs

Integrated GPU and Discrete GPU are the two common types of GPUs. Let us quickly look at their differences.

Differentiating Factors	Integrated Graphics Processing Unit	Discrete Graphics Processing Unit
Design	Built into the device’s CPU or motherboard	Comes as a separate, dedicated component
Memory	Shares the system’s RAM	Built with a dedicated VRAM
Power Efficiency	Consumes less power	Consumes more power for high-performance needs
Performance	Comparatively lesser processing power, and suitable for lighter graphic tasks	Offers higher processing power for advanced graphic tasks
Cost	Comparatively lower	Expensive
Uses	Casual gaming, light photo or video editing	High-end gaming, professional photo and video editing, and graphic designing
Examples	Intel Iris Xe Graphics, AMD Radeon Graphics	NVIDIA GeForce RTX series, AMD Radeon RX series

Virtual GPU and Cloud GPU

Virtual GPUs perform the same functions as integrated or discrete GPUs but without hardware. They are software-based and capable of running heavy workloads. These are built for cloud applications. When a virtual GPU is offered as a service by a cloud service provider, it is referred to as a cloud GPU. 

GPU benchmarks

Now that we know what a graphics processing unit is, how it works, and its types, it is time to understand how to evaluate its performance. 

This is where GPU benchmark tools come in. They measure a GPU’s performance through tests conducted under specific conditions for different applications, such as 3D rendering or gaming.

Types of benchmarks:

• Synthetic benchmarks - Assess a GPU’s raw performance in a standardized environment. 

• Real-world benchmarks - Evaluate how a GPU performs in actual applications, such as gaming. 

The evaluation is usually based on key metrics, like frame rate, processing speed, power consumption, and thermal efficiency. 

Ultimately, the best graphics processing unit depends on your needs. Compare discrete vs integrated laptop GPUs and check GPU benchmark results to find the perfect fit.

Modern applications of GPU 

• Gaming

GPUs power modern gaming by rendering 2D and 3D graphics, determining colors, applying textures, and maintaining smooth frame rates. Advanced features like ray tracing deliver hyper-realistic visuals. With powerful GPUs, gamers can enjoy high frame rates, immersive game worlds, and seamless virtual reality experiences. 

Additional Read: How does ray tracing help while you're gaming?

• Video editing and content creation

For content creators, GPUs reduce long processing times and improve quality. With parallel processing and built-in AI features, they generate real-time playback and render 4K or even 6K videos seamlessly. 

Checkout: ASUS Picks You’ll Love for Video Editing

• Artificial Intelligence 

Training and running AI models need to process large and complex data sets. GPUs feature in running these algorithms at high speeds with parallel processing power. This makes the creation of AI models faster and enables real-time processing and analysis for smarter outcomes.

• Machine learning and deep learning

Machine learning, a branch of artificial intelligence, uses large volumes of data and complex algorithms to replicate human learning. Deep learning, an area within machine learning, uses neural networks to replicate human decision-making. Powerful GPUs accelerate these processes for accurate results. 

• High-performance computing

Powerful general-purpose GPUs provide the computational power needed for fields like data analytics, science research, and financial modeling. They do complex calculations on large data sets efficiently with their ability to process tasks in parallel. 

• Simulation

Simulations in fields like weather forecasting and astrophysics rely on powerful GPUs to deliver accurate results. They are also widely used in the automotive industry, for example, to study fluid dynamics and understand how vehicles work under certain conditions.  

• Blockchain

Blockchain technology is used in sectors like finance and supply chain management. It stores information in blocks linked together in a chain. Every new entry must be validated. This is based on a concept called proof of work that involves complex calculations. GPUs speed up this process through parallel processing, making it faster and more efficient. 

Difference between GPU and CPU

The central processing unit (CPU) is the brain of your computer that manages the system’s overall operations. From running the operating system to handling applications, executing input or output tasks, and managing multitasking, it does everything. 

However, CPUs have fewer cores, designed for sequential processing. This refers to taking up one task at a time. 

The graphics processing unit (GPU), in contrast, is like a powerhouse with thousands of smaller cores. It is designed for parallel processing, which means handling many tasks at once. This makes it essential for demanding jobs like rendering graphics, video editing, and powering AI and machine learning.  

Difference between GPU and graphics card

The graphics card is the hardware or the physical component that houses the GPU, VRAM, and the electrical circuits. The GPU is the core unit of the graphics card that does the processing for parallel computing tasks. The GPU can be integrated into the motherboard or available as a separate graphics card. 

Difference between GPU, NPU, and FPGA

The GPU is built for parallel processing and is specifically designed for tasks like graphics rendering, gaming, and other intensive computing tasks. 

The NPU (Neural Processing Unit) is designed to accelerate AI workloads. It is optimized for real-time data, faster performance, and low power consumption.

The FPGA (Field Programmable Gate Array) is a reprogrammable integrated circuit. Users can reconfigure it using a hardware description language. They are powerful for specialized applications like deep learning and medical imaging. 

Experience advanced GPU power with ASUS

ASUS laptops are engineered to deliver powerful GPU performance for every user category. 

Content creators can explore the Pro Art and Vivobook series, perfect for video editing, streaming, graphic design, animation or game development. 

Gamers can dive into the Republic of Gamers (ROG), ASUS Gaming, and TUF Gaming, offering everything from ultra-slim designs to esports-ready performance and Copilot+ PC devices. 

With NVIDIA GeForce RTX GPUs, expect immersive graphics, smooth performance and solid reliability. AI-accelerated features take your experience even further, making tasks faster and smarter. 

Choose from laptops powered by NVIDIA GeForce RTX 3000, 4000, and the all new powerful RTX 5000 series laptops, each designed with advanced specifications to deliver the performance you need. For even more power, select models also offer specialized neural processing units.

Finding your match is simple. Explore the ASUS categorization to pick the best option that aligns with your specific needs. 

Frequently asked questions

1. What is GPU full form and GPU meaning?

GPU stands for Graphics Processing Unit. It is an electronic circuit designed for image processing and graphics rendering on mobile phones and computers. 

2. How many graphics cards does a laptop have?

A laptop typically has one integrated GPU and may include a discrete GPU for intensive tasks like gaming. In addition, certain high-end laptops support external GPUs for enhanced power to handle heavy workloads.

3. How does a GPU process graphics?

Graphics are displayed based on complex mathematical calculations, and a GPU can perform these calculations at extremely high speeds through parallel processing. They can execute the same mathematical operations on multiple data values simultaneously. 

4. What is the graphics processing unit price?

The price for a graphics card can range from ₹13,000 to ₹2 lakhs.

5. What is the best GPU for gaming?

For gaming laptops, NVIDIA GeForce RTX series delivers the ideal combination of power and intelligent features for a smooth and immersive experience. Before you decide what GPU to buy, compare the available options to match your specific needs.
 

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