Infrastructure & Deployment
GPU (Graphics Processing Unit)
The specialised chip used to train and run AI models at speed.
Definition
GPUs were originally designed for rendering graphics in video games, but their massively parallel architecture — capable of performing thousands of calculations simultaneously — turned out to be ideal for training neural networks. Modern AI infrastructure is GPU-centric: training large models requires clusters of thousands of GPUs, and running (inferencing) even medium-sized models efficiently requires GPU acceleration. NVIDIA dominates the GPU market for AI, though alternatives from AMD and bespoke AI chips are growing.
Related Terms
TPU (Tensor Processing Unit)
Google's custom chip designed specifically for AI workloads.
Inference
The process of a trained AI model actually running and producing outputs — as opposed to being trained.
Model Serving
The infrastructure that makes a trained model available to receive and respond to requests.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026
Knowledge Base·Infrastructure & Deployment·GPU (Graphics Processing Unit)