Use of GPU (Graphics Processing Units) have become essential in many computing devices today, from desktop computers to mobile devices. In this comprehensive guide, we will provide an in-depth look at what GPUs are, how they work, their key uses and benefits, and how they compare to traditional CPUs (Central Processing Units).

What are GPUs?

A GPU, or Graphics Processing Unit, is a specialized processor originally designed to handle graphics and video processing. While the CPU (Central Processing Unit) is the brain of a computer, the GPU takes on all the advanced computations necessary to render complex 3D graphics and process video quickly.

In simple terms, a GPU is a single-chip processor that creates and accelerates video, photo, and graphical content. It takes some load off the CPU so it can focus on general computations.

Modern GPUs are massively parallel processors with thousands of small cores designed for handling multiple tasks simultaneously. This makes them much more effective than CPUs at manipulating and displaying computer graphics.

Some key attributes of GPUs:

  • Specialized in parallel processing – GPU cores are efficient at doing multiple simple tasks concurrently.
  • More cores than CPUs – Top-end GPUs have thousands of cores compared to a few dozen in CPUs.
  • Higher bandwidth – GPUs have access to fast memory bandwidth to drive computational power.
  • Optimized for visual processing – GPU stream processors are optimized for graphics and video tasks.

In summary, GPUs are processors optimized to rapidly manipulate and alter memory to accelerate the rendering of images, videos, and graphics. Their highly parallel structure makes them more effective than general-purpose CPUs for algorithms where processing large blocks of data is done in parallel.

Uses of Graphic Processing Units (GPUs)

While first designed for visual computing, the highly parallel nature of GPUs has led them to be used extensively in other fields as well:


The most obvious application of GPUs is in gaming. 3D games rely heavily on the GPU for rendering complex polygonal environments, textures, objects, and graphics effects like lighting, shadows, etc. GPUs allow modern video games to have life-like 3D graphics and high frame rates for smooth gameplay.

Video Editing and Encoding

GPUs speed up video editing applications by handling video decoding/encoding. Tasks like rendering visual effects, color correction, scaling, and transcoding video files benefit from parallel GPU processing.

AI and Machine Learning

The massively parallel architecture of GPUs makes them well-suited for training deep neural networks and running machine learning algorithms. Nvidia and AMD GPUs are extensively used to accelerate AI applications.

Cryptocurrency Mining

Cryptocurrencies rely on complex computations to verify and add transactions to their public ledger like the blockchain. Miners leverage the computational power of GPUs to solve the cryptographic puzzles involved in mining coins like Bitcoin and Ethereum.

Scientific Computing

Supercomputers use multiple GPUs to accelerate scientific workloads involving complex simu11lations, modeling, and analysis. Fields like climate research, molecular modeling, and physics simulations benefit from GPU acceleration.

Virtual Reality

Virtual reality headsets rely on GPUs to render stereoscopic 3D environments with low latency. As VR gets more immersive, the computational demands on the GPU increase further.

Cloud Gaming

Game streaming services like Nvidia GeForce Now use GPUs on the server-side to render and encode games that are then streamed to client devices. The client only needs to decode the video stream.

In summary, GPUs are now heavily used in gaming, professional media content creation, AI, scientific research, and other fields that require substantial parallel processing power. Their application continues to expand as GPU technology evolves.

How GPUs Work

GPUs achieve their parallel processing power and efficiency through their architecture:

Stream Processors

Each GPU contains hundreds of cores called stream processors or streaming multiprocessors. These handle floating point calculations that are essential for computer graphics. They can process vertices, textures, pixels simultaneously in an efficient manner.

Parallel Processing

The stream processors within a GPU execute instructions concurrently on different data. This allows GPUs to apply complex visual effects to thousands of objects in a scene simultaneously.

Memory Bandwidth

GPUs have access to their own dedicated high-speed memory like GDDR5 or GDDR6 with far higher bandwidth than CPU memory. This allows them to read and write visual data rapidly.

Low Latency

GPUs are designed to prioritize computational throughput over low latency. This is ideal for graphics and video processing where some lag is acceptable. In contrast, CPUs focus more on reducing latency.

Asynchronous Compute

Modern GPUs can handle graphics and general-purpose compute workloads concurrently. So they can run game physics or AI in parallel with graphics tasks.


Like CPUs, GPU cores make use of caches to reduce latency when accessing data. Especially helpful given GPUs have to process large textures and geometry data.


GPUs employ a stream processing architecture that is efficient for parallel operations. In contrast, CPUs use more general purpose cores optimized for low-latency sequential tasks.

In summary, key aspects like thousands of streaming processors, high memory bandwidth, parallel execution, and an architecture built for visual computing allow GPUs to carry out the compute-heavy and repetitive operations involved in 3D rendering much faster than CPUs.

GPU vs Graphics Card

Use of GPU


It’s easy to conflate the terms GPU and graphics card – but they are not the same thing.

A GPU refers to the actual graphics processing chip designed by companies like Nvidia, AMD, and Intel. For example, Nvidia’s latest GPUs are the RTX 4090, RTX 4080, and RTX 4070.

A graphics card is a complete board that contains a GPU along with other supporting components like:

  • Video memory – GDDR6 or GDDR5 chips
  • Cooling systems – Fans, heat sink
  • I/O interfaces – HDMI, DisplayPort
  • Power circuitry
  • PCIe connector

For instance, the Nvidia GeForce RTX 4090 is a complete graphics card built around the AD102 GPU. It comes with 24GB of GDDR6X memory, a vapor chamber cooler, and other components required to make it function.

So in summary:

  • GPU refers to the actual graphics processing chip or processor.
  • Graphics card refers to the complete add-in board that houses a GPU along with memory, power, and other supporting hardware.

The GPU powers the graphics card and performs all the 3D rendering computations. The other components on the card help feed data to the GPU and dissipate heat from it.

Which is Faster – GPU or CPU?

Use of GPU

When it comes to speed, GPUs vastly outperform CPUs on parallelizable graphical and compute workloads by around 10-100x. However, CPUs are still better suited for general purpose applications.

Here’s a deeper comparison between their capabilities:

Graphics Processing

  • GPUs can render and process graphics exponentially faster than conventional CPUs – around 50-100x times quicker.
  • They have specialized hardware for transform, lighting, texturing required in real-time 3D rendering.
  • Massive parallelism allows them to apply shaders and effects to thousands of objects simultaneously.


  • GPUs allow new games to have cinematic graphics, realistic visual effects, complex game physics and high frame rates.
  • Even top-end CPUs lack the graphics horsepower to run modern titles at high FPS beyond 720p resolution.

Video Editing

  • GPU acceleration speeds up video processing times by 5-10x for operations like rendering, encoding/decoding, stabilization etc.
  • Tasks like video transcoding see 20-30x speedups with a good GPU.

AI and Machine Learning

  • Training complex neural networks is much faster on GPUs, with speedups of 10-20x over CPUs.
  • Rapid matrix math capabilities allow quick processing of large datasets when training ML models.

Scientific Computing

  • GPUs speed up simulations, physics computations, data analytics etc by 10-100x over CPUs.
  • Their massive parallelism suits complex computations like weather prediction models, molecular simulations etc.

General Purpose Computing

  • GPUs lag far behind CPUs when it comes to latency-critical serial tasks and general purpose computing.
  • CPUs are still better suited for running everyday applications, operating systems, server backends etc due to their higher single-threaded performance.

In conclusion, for parallelizable visual computing workloads, GPUs are an order of magnitude faster than CPUs. But for general purpose, low-latency computations, the CPU remains the better choice. The ideal approach is pairing a CPU and GPU together to complement each other.


GPUs have evolved far beyond just accelerating video games – they now power advanced applications from AI to scientific research. Dedicated graphics processing units can offer performance that is multiples faster than even the most advanced CPUs when it comes to parallel, intensive computations.

Key strengths like thousands of stream processors, high memory bandwidth, a parallel architecture and specialization make GPUs indispensable. At the same time, CPUs still hold relevance for tasks requiring lower latency or sequential processing.

Thanks to rapid innovation in the GPU technology space, these processors will continue boosting everything from real-time graphics, physics simulations, deep neural networks and much more. The synergy between GPUs and CPUs is driving next-generation immersive experiences, discoveries and capabilities.

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