What are GPUs? Inside the processing power behind AI

AI and generative AI

Today’s increasingly sophisticated AI technologies — notably large language models (LLMs) and generative AI — require lots of speed, lots of data and lots of compute. Because they can perform simultaneous calculations and handle vast amounts of data, GPUs have become the powerhouse behind AI (e.g., AI networking and AI servers).

Notably, GPUs help train AI models because they can support complex algorithms, data retrieval and feedback loops. In training, models are fed huge datasets — broad, specific, structured, unstructured, labeled, unlabeled — and their parameters adjusted based on their outputs. This helps to optimize a model’s performance, and GPUs help to accelerate the process and get models more quickly into production. 

But a GPUs’ work doesn’t stop there.Once models are put into production, they need to be continuously trained with new data to improve their prediction cap abilities (what’s known as inference). GPUs can execute ever more complex calculations to help improve model response and accuracy. 

Edge computing and internet of things (IoT) 

GPUs are increasingly critical in edge computing, which requires data to be processed at the source – that is, at the edge of network. This is important in areas such as cybersecurity, fraud detection and IoT), where near-instant response times are paramount.  

GPUs help to reduce latency (compared to sending data to the cloud and back), lower bandwidth (transmitting large amounts of data over networks is not necessary) and enhance security and privacy measures (the edge keeps data local). 

With GPUs as their backbone, edge and IoT devices can perform object detection and real-time video and image analysis, identify and flag critical anomalies and perform predictive maintenance, among other important tasks.