What is an AI data center?

We keep hearing that companies need to invest in building datacenters to enable AI. Has anyone wondered what the difference between a traditional data center and an AI data center really is?

A traditional data center is a facility that includes critical IT infrastructure, such as servers, storage systems and networking hardware to store, process, and manage data. These facilities enable digital services like websites and applications and require power, cooling, and security systems to ensure reliability and continuous operations.

An AI data center is an advanced facility designed to power complex AI and machine learning workloads by using hardware like GPUs. These centers require more power, cooling and high-speed networking than traditional data centers to manage the intense computational workload of training AI models and running AI applications. AI data centers typically consist of a large number of processors in a small space that requires significantly higher power and cooling needs compared to traditional data centers. They are equipped with chips like Graphics processing units (GPUs) that help in processing complex mathematical operations that are required for tasks involving Artificial Intelligence. These centers use high speed networking to ensure massive amounts of data can be moved efficiently between servers and processors without bottlenecks. The GPUs used for AI generate significantly more heat than traditional hardware, often consuming 10 to 15 times more power per processing cycle. To prevent equipment failure, AI datacenters use cooling methods like liquid cooling or immersion cooling, rather than traditional air-cooling. Since AI models require quick access to massive datasets for both training and inference the data centers utilize high-speed storage systems, such as Solid-State Drives (SSDs) to ensure fast data retrieval. Additionally, data centers employ highly optimized software management systems to manage resource allocation, streamline workflows and perform predictive maintenance. 

In conclusion, AI datacenters are different than traditional data centers and require significant investments in hardware and software to enable complex computational needs required by AI based systems.