Graphics Processing Unit: Powering the Global AI and Digital Economy

Nvidia Corporation, creator of the first GPU in 1999, is in focus as GPUs now power the global digital and AI economy.

• However, NVIDIA does not legally hold a monopoly, but it enjoys near-dominant market power in certain GPU segments, especially AI computing.
  • It holds around 90% market share in discrete Personal Computer GPUs and maintains a strong position in data centre GPUs. 

About Graphics Processing Unit

• A Graphics Processing Unit (GPU) is a specialised processor designed to perform many simple calculations simultaneously (parallel processing).

• Unlike a CPU (Central Processing Unit), which handles complex, sequential tasks, a GPU is optimised for large-scale repetitive computations.
• Initially developed for video game graphics, GPUs are now central to AI, data centres, simulations, and high-performance computing (HPC).
• Major manufacturers include: NVIDIA, AMD and Intel

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Origin & Evolution

• In 1999 NVIDIA launched GeForce 256, marketed as the world’s first GPU, initially for video games.
• Over 25 years, GPUs have moved from graphics rendering to becoming core infrastructure of the digital economy.
• Today, GPUs are indispensable for AI, machine learning, simulations, and high-performance computing.

Applications of GPU

• Artificial Intelligence & Deep Learning: GPUs are used for training and deploying AI models because they efficiently handle large-scale parallel computations.
  • For example: Models developed by OpenAI are trained using GPU clusters.
• Scientific Research & Supercomputing: GPUs accelerate complex scientific simulations such as climate modelling, genomics, and molecular dynamics; many supercomputers use GPUs developed by NVIDIA.
• Gaming & Graphics Rendering: GPUs were originally designed for rendering high-quality 3D graphics, enabling real-time gaming, virtual reality, and ray tracing technologies.
• Autonomous Vehicles: GPUs process real-time sensor and camera data in self-driving cars, enabling object detection, path planning, and decision-making systems.
• Cloud Computing & Big Data: GPUs power AI-as-a-Service platforms and data analytics, where cloud providers deploy GPU-enabled servers to accelerate machine learning workloads.

Difference between CPU and GPU

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Difference between GPU vs Graphics Card

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