From Training Models to Running Them at Scale
To understand why AI infrastructure requirements are evolving, it is important to distinguish between the two key stages of the AI lifecycle: training and inference.
Training is the process of developing and refining AI models, where vast amounts of data are processed across powerful compute environments to teach models how to recognise patterns, interpret information and make predictions. The scale of these workloads has been a major driver of demand for AI-ready infrastructure.
Inference, by contrast, is the process of applying a trained model to real-world interactions. For example, every time an employee interacts with an AI assistant, a trained model is performing inference to generate a response or complete a task.
As AI adoption expands across industries, the volume of inference activity is growing rapidly. This is changing not only how AI is consumed, but also the infrastructure required to support it.
What This Means for Data Centre Infrastructure
The growth of inference introduces a new set of infrastructure considerations as organisations deploy AI at scale.
Supporting a Broader Range of AI Workloads
To date, most of the focus has been on building infrastructure capable of supporting large-scale model training environments. As inference demand grows, data centre operators must support a broader range of AI workloads, each with its own requirements for performance, cooling, resilience and scalability.
While model training often relies on high-density infrastructure supported by advanced cooling technologies, inference workloads can present a broader range of requirements depending on the application being supported. Some environments will require liquid-cooled infrastructure capable of supporting the highest-density AI deployments, while others may be best suited to air-cooled or hybrid cooling architectures depending on the workload, power density or operational requirement.
This shift has important implications for facility design. Data centre operators must be able to accommodate diverse infrastructure requirements, from varying rack densities and cooling strategies to different resiliency and deployment models, while maintaining the flexibility to support evolving customer needs as AI adoption continues to expand.
Flexibility Is Essential
The pace of AI innovation shows no signs of slowing. New models, applications and deployment approaches are emerging at a rapid rate, making it difficult to predict exactly what future infrastructure requirements will look like.
As a result, data centres must be designed to accommodate change. Operators that can provide adaptable environments, scalable capacity and the ability to support evolving customer requirements will be best positioned to support the next generation of AI workloads.
Speed to Capacity Matters
As more organisations move AI initiatives from pilot projects into production environments, the ability to access infrastructure quickly is becoming more important.
This places greater emphasis on scalable design, efficient delivery and the ability to bring capacity online at pace. In a rapidly evolving AI market, speed to capacity can be a significant competitive advantage.
