Graphic Processing Units (GPUs) – Working, Uses, and Role in AI & Computing

Learn about Graphic Processing Units (GPUs), how they work, their architecture, and their critical role in gaming, AI, machine learning, data centres, and high-performance computing.

Table of Contents

Context

Graphics Processing Units (GPUs) have evolved from powering video games to becoming the backbone of artificial intelligence, scientific simulations, and the digital economy.

About GPU

● Basic Definition: A GPU (Graphics Processing Unit) is a specialized processor designed to perform many simple calculations simultaneously at extremely high speed.

● Core Idea: Unlike CPUs, GPUs are built for parallel processing, meaning thousands of small computing cores work together on repetitive tasks.

● Historical Origin: The first widely marketed GPU was the GeForce 256 by Nvidia in 1999, initially designed for improving video game graphics.

● Modern Role: Today, GPUs are central to AI training, cloud computing, cryptocurrency mining, simulations, and high-performance scientific research.

How Does a GPU Function

Parallel Architecture: A GPU contains hundreds or thousands of smaller cores that execute identical instructions across large datasets simultaneously.
Rendering Pipeline (Graphics Example): When rendering images, GPUs process objects in four stages: vertex processing, rasterization, pixel shading, and writing to frame buffer.
Shaders: Small programs called shaders run across thousands of cores to calculate lighting, textures, reflections, and color for each pixel.
Memory System: GPUs use high-bandwidth memory called VRAM to move massive data volumes quickly, reducing processing delays.
High Data Throughput: They are designed to read and write large data blocks efficiently, essential for graphics and AI computations.

CPU vs GPU

Feature	CPU	GPU
1. Core Structure	Has few powerful cores (4–32) designed for complex instructions.	Has thousands of smaller cores designed for parallel calculations.
2. Type of Tasks	Best for complex, sequential and decision-based tasks.	Best for repetitive, large-scale mathematical computations.
3. Parallel Processing	Limited parallel processing capability.	Extremely high parallel processing capability.
4. Multitasking Ability	Excellent at switching between multiple tasks quickly.	Performs best when focusing on one large task at a time.
5. Main Applications	Used for operating systems, applications, and system control.	Used for AI training, gaming graphics, simulations, and data-heavy workloads.

CPU Strength: CPUs excel in decision-making, branching tasks, and running operating systems.

GPU Strength: GPUs excel in performing identical mathematical operations across large arrays of data.

Architectural Difference: CPUs allocate more area to control logic and cache, while GPUs allocate more area to compute units.

Why Do Neural Networks Use GPUs?

Matrix and Tensor Operations: Neural networks rely heavily on matrix multiplication and tensor calculations, which are repetitive and parallelizable.
Massive Parameter Counts: Modern AI models contain millions to billions of parameters requiring simultaneous computations.
Parallel Efficiency: GPU cores can multiply and add thousands of numbers simultaneously, accelerating training.
Tensor Cores: Modern GPUs like Nvidia’s H100 include tensor cores optimized for AI matrix math.
Memory Bandwidth: High-speed memory movement ensures large datasets and model parameters are processed efficiently.

How Much Energy Do GPUs Consume?

Training Phase Consumption: An Nvidia A100 GPU consumes about 250 watts under load. Four GPUs running 12 hours use roughly 12 kWh.
Inference Phase Consumption: Once trained, using the model consumes less power, roughly 2 kWh for moderate inference workloads.
Server Overhead: Additional components (CPU, RAM, cooling) add 30–60% extra energy consumption.
Data Centre Scale: Large AI data centres consume megawatts of power, leading to rising concerns over energy demand.
Environmental Impact: AI training can significantly increase electricity demand, making renewable energy integration important.

Does Nvidia Have a Monopoly?

Market Share in GPUs: Nvidia holds around 90% market share in high-performance discrete GPUs for AI applications.
Competitors: AMD and Intel produce GPUs, but they lag behind Nvidia in AI-focused hardware and ecosystem maturity.

Alternatives to Nvidia GPUs

● AMD GPUs: AMD produces competitive GPUs but has smaller market share in AI data centres.

● Intel GPUs: Intel is entering AI accelerator markets but remains behind in performance.

● Google TPUs: Google’s Tensor Processing Units (TPUs) are custom AI chips optimized for neural network tasks.

● Specialized AI Chips: Companies are developing ASICs (Application-Specific Integrated Circuits) for energy-efficient AI computing.

CUDA Ecosystem Advantage: Nvidia’s CUDA software platform allows developers to run AI workloads efficiently on Nvidia GPUs.
Software Lock-In Effect: Switching from Nvidia requires rewriting code for other platforms, increasing dependency on Nvidia hardware.
Data Centre Dominance: Nvidia dominates AI data centre GPUs, especially with products like H100 and A100.
Regulatory Scrutiny: Regulators in the EU and US are investigating whether Nvidia’s dominance restricts competition.

Future of GPU Technology

Smaller Fabrication Nodes: GPUs are moving toward 3–5 nanometer fabrication technologies for efficiency.
Energy Efficiency Improvements: New architectures aim to improve performance per watt.
Integrated AI Accelerators: Future chips may integrate CPU, GPU, and AI accelerators in unified systems.