As artificial intelligence (AI) reaches the tipping point of commercialisation, enterprises are rapidly evolving their strategies to take advantage of the wide range of possibilities enabled by these modern technologies.
Key to navigating the AI evolution from an infrastructure perspective is the adept handling of high-power density deployments within a low-latency connectivity framework.
As AI use cases proliferate, a vital component of any adoption strategy is ensuring that infrastructure is in place to support the power requirements and management of data flows that machine learning demands.
Real world AI use cases
AI holds the potential to unveil a multitude of applications, including customer service chatbots, medical diagnostics, automation of insurance claims processing, and fraud detection mechanisms.
In the manufacturing sector, AI can help to transform maintenance workflows and stay one step ahead with predictive maintenance to optimise operations by reducing unplanned downtime, gaining operating efficiencies, and maximising equipment use.
Enterprises are turning to AI to provide value added customer service experiences that automate and accelerate time-to-resolution for common interactions such as placing orders, scheduling events, finding and understanding information in contracts and other unstructured documents, as well as accessing product or service information.
Financial services organisations are using AI to optimise fraud detection and enhance cybersecurity, while providing real-time transaction monitoring, automated credit checks, and analysing customer behaviour.
This may include using AI to improve and automate customer communications, optimising the creation and personalisation of services such as product recommendations, improving customer experiences, retention, and cross-sales while analysing markets and complex data in multiple formats, and managing investment portfolios.
Most enterprises believe AI will help their operations in new and fundamental ways. Telecommunications organisations will streamline customer and support staff communications, report and fix software errors, translate and summarise information, while optimising traffic flow across its networks through better network architecture, predictive maintenance, identifying new revenue opportunities by analysing customer behaviour and offering valuable insights.
In healthcare, AI is benefitting staff and patients by automating manual administrative processes to enhance the overall patient experience. This may include virtual nursing assistants, reducing patient medication dosage errors, and preventing fraud by recognising unusual insurance claims and payments.
Power and cooling requirements for AI infrastructure
The high-power densities of AI infrastructure require data centre facilities to be able to meet two important aspects: higher power densities per rack and specialised cooling to dissipate the heat generated by deployments.
Generative AI’s impact on AI cluster architecture necessitates larger neural networks, increasing hardware requirements, compute fabric, and dataset sizes, resulting in heightened power consumption, and the need for efficient cooling infrastructure. Denser server racks require advanced cooling methods to cope with the resulting substantial power requirements.
Traditional air cooling is inadequate for the power-intensive GPU racks used in generative AI, which drives the need for data centres equipped with liquid cooling capabilities to support power loads exceeding 40kW per rack.
Leveraging ecosystems key to optimising AI and cloud
Just like needing a cloud rich environment, most enterprises will access their AI processing engines as a cloud service given the cost and scale required. Enterprises therefore need to rethink the localisation of their AI processing requirements within a colocation facility with deep AI, cloud, and network ecosystems.
Enterprises engaged in AI-driven initiatives will encounter data streams originating from diverse sources. Whether customer interactions, IoT devices, or proprietary datasets, the convergence of data is where AI thrives. This amalgamation of data requires the placement of processing capability at the centre of these data intersections, providing enterprises with the agility to harness AI’s transformative potential.
Hybrid AI encapsulates the integration of cloud networks, proprietary data repositories, and external AI ecosystems.
Deploying within a Teraco data centre provides enterprises with autonomous, private and secure access to data, public cloud, and AI infrastructure through interconnectivity. This not only optimises AI performance but also enhances utilisation of the broader AI ecosystem.
AI data flows
The latency sensitivity of AI is a critical consideration. With immense data flows inherent in AI models, minimising latency and transit is a consideration. AI models draw upon extensive datasets sourced from diverse sources such as clouds, enterprise databases, and edge IoT devices.
To optimise performance, it becomes imperative to ensure the seamless integration of AI applications with existing IT systems without high network costs. AI infrastructure is thus ideally located within an ecosystem rich data centre environment with access to high-speed and secure networks able to facilitate efficient data exchange.
Enterprises are increasingly shifting their focus from connectivity to robust interconnection solutions, particularly within cloud environments. Interconnection emerges as the preferred approach, involving direct connections between edge routers or switches across multiple networks encompassing carriers, clouds, AI, content providers, enterprises, and application service providers.
As AI continues to gain prominence, it will further accelerate enterprises’ interconnection strategies, elevating the importance of strategic system placements to optimise performance, and mitigate cost and latency issues.
Localised AI access
An enterprise that has deployed within an ecosystem rich colocation facility is provided with direct interconnected access to data sets on premise or within the cloud, as well as localised AI processing where latency is minimised. This enables enterprises to process vast amounts of data and deliver actionable insights and feedback in real-time – especially when these data processes rely on instantaneous updates.
Consider the analogy of data lakes, vast reservoirs of information essential for AI applications. Attempting to traverse this data across networks introduces inefficiencies, bottlenecks, cost, and risks. Enterprises deploying in an ecosystem rich colocation environment unlock the full potential of all their information.
This centralised approach not only streamlines data management but also facilitates seamless integration with AI algorithms, enabling enterprises to extract actionable intelligence at scale.
Over time, AI is expected to improve customer relationships, increase productivity and improve sales growth. AI is perceived as an asset for improving decision making, reducing costs, streamlining processes, decreasing response times, and avoiding mistakes.
As we continue to navigate the AI-powered world, ecosystem dense colocation facilities will operate as the nucleus in optimising AI processing requirements.
