# What is GPU? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Hardware & Infrastructure  
> **Canonical Citation:** [https://spidits.com/ai-glossary/gpu](https://spidits.com/ai-glossary/gpu)

## Definition
A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory. Because training neural networks involves massive matrix multiplication, the parallel processing power of GPUs is critical for modern AI workloads.

## Architectural Mechanics & Deep Dive
A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images. Due to their massively parallel architecture containing thousands of cores, GPUs are exceptionally efficient at performing the matrix multiplications that form the computational core of deep learning training and inference.

## Industry Applications & Real-World Use Cases
Model training compute, high-throughput inference hosting, and heavy image synthesis

## Frequently Asked Questions

### Why are GPUs better than CPUs for AI?
CPUs have a few powerful cores optimized for sequential tasks. GPUs have thousands of smaller cores designed to perform simple operations (like matrix multiplication) simultaneously.

### What is VRAM?
Video RAM (VRAM) is the fast dedicated memory on the GPU. Modern AI models require massive VRAM (e.g. 24GB to 80GB) to store parameters during training and inference.

## Related Terminology
- [deep-learning](https://spidits.com/ai-glossary/deep-learning)
- [inference](https://spidits.com/ai-glossary/inference)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*