Definition
A graphics processing unit (GPU) is a parallel computing processor designed for rendering graphics, accelerating artificial intelligence (AI) workloads, and processing compute-intensive tasks like image processing and simulation. With hundreds to thousands of cores, GPUs excels at executing many operations concurrently, making them suitable for high-throughput, data-parallel applications across graphics and AI [1].
For example, a GPU in AI is like a large Aba tailoring workshop with 50 tailors, each working on different parts of senator wear, one sewing sleeves, another doing embroidery, another attaching buttons, all working simultaneously on multiple orders. In contrast, a CPU is like a single master tailor who is extremely skilled and works very fast, but can only sew one complete outfit from start to finish before beginning the next. So in situations such as where a big owambe party orders 100 matching aso ebi outfits needed in one week, the workshop with 50 tailors (GPU) delivers all outfits on time by working in parallel, while the single master tailor (CPU), despite superior individual skill, would take months finishing each outfit sequentially.
Origin
Initially in the 1970s GPUs were developed for the purpose of graphics rendering needed for video games. But with time, researchers saw them suited for scientific computing and machine learning due to their parallel processing capabilities. A major milestone was NVIDIA’s introduction of CUDA in the 2000s, which enabled developers to integrate GPUs for general-purpose computing, including AI and deep learning [3].
Context and
Usage
It is not an exaggeration
to say GPUs are some of the most important (if not the most important)
specialized chips in the world today. Apart from gaming, modern GPUs can be
used across a wide range of compute-intensive applications. Some of their use
cases are as follows:
- AI and Machine Learning: GPUs are good at executing parallel tasks, making them ideal for training and running neural networks .
- Data Centers: Data center GPUs accelerate data analytics, process large datasets, and support virtual desktops.
- Scientific Research: Simulations in physics, chemistry, and biology depend on GPU processing.
- Video Rendering and Editing: High-performance GPUs dramatically reduce rendering time in addition to enabling real-time editing [4].
Why it Matters
Artificial intelligence (AI) is assisting in everything, from securing data to diagnosing diseases, reshaping the world in the process. The GPU stands at the center of this AI revolution. Though original developed for rendering graphics, GPUs have become increasingly important for AI, enabling the training and deployment of advanced AI models that were previously infeasible.
Related Programming and Development Tools
- Python: Popular programming language widely used for AI and machine learning development.
In Practice
A good example
of a real-life case study of GPU services can be seen in the case of Amazon Web
Services (AWS) which offers Amazon Elastic Compute Cloud (Amazon EC2). With
Amazon EC2, it’s easy to rent and run GPUs in the cloud. You can utilize the
processing power of their GPUs for tasks such as video editing, render
graphics, artificial intelligence (AI), and other parallel processing
capabilities [5].
References
- Arm. (2025). Graphics Processing Units (GPU).
- Geeksforgeeks. (2025). Graphics Processing Unit (GPU).
- Sujatha, R. (2025). What is a GPU? The Engine Behind AI Acceleration.
- TRG. (2026). GPU: Graphics Processing Unit Explained.
- AWS. (2026). What is a GPU?