What Is a Graphics Processing Unit (GPU)? Definition and Examples

A Graphics Processing Unit (GPU) is a specialized electronic circuit or chip designed to accelerate the creation and rendering of images, video, and animations for display. Originally developed to improve graphics performance for video games and video editing, GPUs are now widely used for other compute‑intensive tasks such as machine learning, scientific simulations, and cryptocurrency mining because of their ability to perform many calculations in parallel.

How a GPU works

Graphics are typically represented as geometric primitives (polygons) and transformed into pixel data through a process called rendering.
GPUs perform stages such as vertex transformations, lighting calculations, rasterization, texture mapping, and pixel shading to convert 3D scene data into a 2D image.
Unlike CPUs, GPUs have thousands of smaller cores organized for parallel processing, enabling them to execute many simultaneous calculations—ideal for workloads that can be parallelized (graphics, neural network operations, matrix math).

Brief history

The first widely available GPU is generally credited to Nvidia’s GeForce 256 (1999), which integrated transform and lighting engines and moved many graphics tasks off the CPU.
Over time GPUs evolved from gaming and video‑editing tools into general-purpose parallel processors used in AI, high-performance computing, and data centers.
Recent advances in GPU architecture and software ecosystems have broadened their role beyond graphics into areas that require massive parallel computation.

GPUs vs CPUs

CPUs (Central Processing Units) are optimized for low-latency, sequential processing and complex single‑threaded tasks. They have relatively few powerful cores.
GPUs are optimized for high throughput and parallelism, with many more (but simpler) cores that can run thousands of threads concurrently.
In many systems, CPUs handle control logic and serial tasks while GPUs accelerate parallel workloads such as rendering, simulations, and machine learning training/inference.

Special considerations

GPU vs graphics card: “GPU” refers to the chip or processor. A “graphics card” (or video card) is the physical board that houses one or more GPUs plus memory, cooling, power regulation, and display outputs.
Integrated vs discrete GPUs:
Integrated GPUs are built into the CPU or motherboard and share system memory, offering low power and cost but limited performance.
Discrete GPUs are separate cards with dedicated memory and power, providing much higher performance for gaming, content creation, and compute workloads.
Modern systems commonly use discrete GPUs for demanding graphics or compute tasks, while integrated GPUs suffice for everyday use and low‑end machines.

GPUs and cryptocurrency mining

Cryptocurrency mining involves performing many repetitive cryptographic calculations; GPUs’ parallelism made them attractive for mining certain coins.
High demand from miners has led to supply shortages and price spikes at times, affecting availability for gamers and professionals.
For some currencies (notably Bitcoin), miners shifted to specialized hardware (ASICs) because of better cost efficiency, but GPUs remain common for mining many other cryptocurrencies.

Examples of GPU companies

Nvidia: Pioneered the modern GPU with early GeForce products and remains a major supplier of discrete GPUs used in gaming, professional graphics, datacenters, and AI acceleration.
AMD: Competes in both consumer and professional GPU markets (including products from its ATI acquisition). AMD supplies GPUs for gaming, workstations, and integrated solutions.

FAQs

What is the difference between a GPU and VGA?
A GPU is the processor that renders graphics. VGA (Video Graphics Array) originally referred to a video connector and a display standard; today “VGA” often denotes a type of video output port, not the GPU itself.
How do you overclock a GPU?
Overclocking increases GPU clock speeds to gain performance. Typical steps: ensure adequate cooling, update drivers, use a trusted overclocking tool (e.g., Afterburner), increase clocks and voltage in small increments, and run stability/benchmark tests. Overclocking can shorten hardware life and void warranties, so proceed cautiously.
What is GPU scaling?
GPU scaling adjusts how an image is stretched or fitted to a display’s resolution, often to preserve aspect ratio or scale lower‑resolution content up to a monitor’s native resolution.

Key takeaways

A GPU is a processor specialized for parallel computation, originally for graphics but now used broadly for compute‑heavy tasks.
GPUs accelerate workloads by performing many operations in parallel, complementing CPUs in modern systems.
Major GPU vendors include Nvidia and AMD; GPUs can be discrete cards or integrated into system chips, and demand from applications like gaming, AI, and cryptocurrency has shaped supply and pricing.