graphical processing unit (GPU)
This glossary entry explains graphical processing unit (GPU) for AI governance and model risk programs. The sections below summarize what the term means in plain language, why chief AI officers and cross-functional committees track it, where teams often get confused, and—when you are signed in—how it shows up across major industries and in expectations tied to the EU AI Act and NIST AI RMF. Use related links at the end of the page to explore neighboring concepts without losing context.
What It Means
A GPU is a computer chip originally designed to render graphics and video, but it turns out to be exceptionally good at the mathematical calculations needed for AI and machine learning. Unlike regular computer processors that handle tasks one at a time, GPUs can perform thousands of simple calculations simultaneously, making them much faster for training AI models.
Why Chief AI Officers Care
GPUs are often the largest cost component in AI infrastructure, potentially representing 60-80% of your compute budget for machine learning projects. The choice between buying, renting, or using cloud-based GPUs directly impacts project timelines, as training complex models that might take months on regular computers can be completed in days or weeks with proper GPU resources.
Real-World Example
A retail company training a computer vision model to detect damaged products on assembly lines found their project stalled when using regular servers - the model would take 6 months to train. After switching to a cloud GPU cluster, the same training completed in 3 days, allowing them to deploy the quality control system before the holiday shopping season.
Common Confusion
People often think any GPU will work for AI, but consumer gaming GPUs lack the memory and precision needed for large AI models. Additionally, many assume that more GPUs always means faster training, but without proper software optimization, adding GPUs can actually slow things down due to communication overhead.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, GPUs accelerate medical AI applications like radiology image analysis, drug discovery simulations, and re...
Finance: In finance, GPUs are essential for accelerating complex quantitative models including real-time risk calculations, algor...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"A specialized chip capable of highly parallel processing. GPUs are well-suited for running machine learning and deep learning algorithms. GPUs were first developed for efficient parallel processing of arrays of values used in computer graphics. Modern-day GPUs are designed to be optimized for machine learning."Source: NSCAI
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