GPU

GPU stands for Graphics Processing Unit and describes a processor optimised for highly parallel computations. GPUs originally rendered pixels and graphics operations for games and 3D applications. Today they are also the workhorse for machine learning, image processing, scientific simulations and video encoding.

Why GPUs matter for AI

AI models, particularly neural networks, consist of many matrix multiplications that can be executed in parallel. A modern GPU has thousands of compute units working simultaneously. Models therefore train much faster than on a classic CPU. For inference, applying a trained model, GPUs help especially with large models.

Practice and availability

GPUs are used locally in workstations and servers or booked by the hour from cloud providers such as Google Cloud, AWS and Azure. Options range from small development models to high end cards with large memory capacity for training huge models. Market shortage, power consumption and cost are the most common limits to large scale deployment.

Context in marketing and operations

In day to day marketing, GPUs appear mostly indirectly because they power image generators, AI translators or chat models used by marketing teams. In the operation of services like JD Mail, GPUs play no direct role. Sending, tracking and reporting run classically on CPU. AI features for text suggestions use models that run centrally on GPU infrastructure.