GPU
Also known as: Graphics Processing Unit
Definition
GPU is a programmable hardware accelerator designed to perform many arithmetic operations in parallel, originally for graphics processing and now widely used for general-purpose compute and AI workloads.
Key Points
- GPUs accelerate parallel matrix and tensor operations.
- They are essential for many modern AI workloads, especially deep learning.
- GPU availability affects whether models can be trained, served, or deployed at the edge.
- They are used in both centralized and edge environments.
- GPU capacity often becomes a limiting factor in AI deployment cost and scaling.
Concept
A GPU is valuable because many AI and vision workloads are dominated by repeated numerical operations that map well to parallel hardware. Deep learning training almost always depends on GPUs, and inference often does as well when the model is large or the latency target is strict. In operational terms, GPU availability influences not only performance but also where a model can run: a site with limited compute may need a smaller model, quantization, or a different deployment pattern to make AI practical.
Explainer
For ConnectedEarth, GPUs sit behind a large share of the AI stack. They are part of the reason deep learning, model serving, and computer vision can be operationally useful, but they also introduce cost, power, and infrastructure constraints. In remote sites and edge deployments, GPU presence can determine whether advanced AI is feasible locally or must remain centralized.