Platform Teraco

The foundation of AI implementation is rooted in the physical infrastructure. High-performance computing (HPC) and AI demand modern infrastructure with significant computing power, robust data storage solutions, and scalable frameworks that evolve as AI applications grow.

AI workloads, especially in training deep learning models, demand high-density power solutions. Today’s AI infrastructure can require up to 150kW per rack, which is five to ten times more than traditional data centre use cases. The efficiency of power management is critical to balancing energy consumption with cost effectiveness, ensuring AI environments run at optimal performance.

The density of AI computing workloads requires advanced cooling techniques. While air-assisted cooling works for some lower-density environments, high-performance AI applications typically require liquid cooling solutions like Direct Liquid Cooling (DLC) or air-assisted liquid cooling (AALC) to manage the heat generated by intensive information processing and machine learning.

Enterprises are turning to colocation for their AI deployments. Colocation provides the flexibility to scale computing resources without the need to invest in new on-premise infrastructure. Colocation offers a cost-effective solution for managing complex AI workloads while optimising proximity to data sources, ecosystems, and users, ensuring low-latency and high-performance data processing.

AI projects often begin as proof-of-concept or minimum viable products, which need to scale quickly. Scaling AI infrastructure requires flexibility in infrastructure design, reducing complexity, and controlling costs. By leveraging colocation and hybrid cloud models, businesses can achieve rapid deployment of AI solutions while maintaining scalability and controlling expenses.

Teraco data centres offer strong interconnection capabilities, providing direct and low-latency connections, reducing costs and boosting AI performance. Interconnecting multiple data sources, clouds, and AI applications is vital for success. AI infrastructure thrives on the free flow of information, so a robust and secure interconnection strategy is necessary to ensure seamless data transfer between ecosystems.

AI applications are highly latency sensitive. Deploying AI infrastructure close to data sources and users reduces latency, enhancing real-time processing and decision making. Colocating within dense cloud, AI, and network ecosystems at Teraco enables faster data exchange and more efficient AI operations, directly impacting the overall performance of AI-driven initiatives.

As AI infrastructure generates, processes, and stores massive volumes of data, information security and compliance become top priorities. Managing risks such as ransomware, data breaches, and regulatory requirements around data sovereignty is essential. Enterprises benefit by selecting secure, compliant partners such as Teraco with expertise in protecting sensitive AI workflows and data.

Sustainability is a growing concern as AI systems demand high energy consumption. Comparing on-premise infrastructure with colocation reveals that colocation often provides a more energy-efficient and environmentally friendly option, thanks to advanced energy management systems, renewable energy sourcing, and efficient cooling techniques.