Artificial Intelligence has been dominating the IT sphere for some time now — it’s redefining how we build our infrastructure, changing the nature of power procurement and delivering massive amounts of value to customers and end users.
Many of the most well-known AI applications include anything from autonomous vehicles to smart city infrastructure, and as a result, we’ve come to know AI as an instantaneous decision-maker, processing data in milliseconds. Naturally, this is where edge data centers shine. Positioned closer to the data source, edge data centers minimize latency, ensuring that AI applications can operate at the speed that their users desire.
However, AI is also bringing a nuance to the table that opens up a whole new world of data center siting and location opportunities: While fast data transmissions and low latencies are important, many of the most prevalent AI applications don’t necessarily require data centers to be near large population centers.
While intelligent end-user applications like autonomous vehicles, AI chatbots and facial recognition-based security systems get lots of press, AI is being used for so many other applications that don’t go directly to end users in urban hubs. For instance, research use cases and AI model training are massive opportunities that can create huge impact — but while high-speed connectivity is key, these applications can be supported by data centers that are much further afield and remote.
As a new era of AI data center use begins, the map of data center value is shifting too.
The Edge Stays Essential
On the whole, our increasing reliance on 5G, AI and IoT devices has further underscored the value of edge data centers. Processing at the edge is essential in today’s data-driven landscape for a few reasons, including better bandwidth and cost efficiencies, enhanced security and more. Of course, edge data center use cases for real-time and hyper-efficient applications are staying put — the foundational level of demand for edge data centers remains since our demand for instantaneous results isn’t fundamentally changing.
Here’s the interesting part, however: The term ‘edge’ has long been used to describe facilities and processes that are as close to the end user as possible. In fact, the very edge has often been the users themselves. In the era of AI, we have the opportunity to give a new meaning to the term edge — all while we leverage some newfound opportunities.
So, while edge data centers will continue to be critical catalysts for innovation, the way they can be sourced and applied is creating some interesting new ripples in IT infrastructure strategies.
Redefining ‘Edge’, Discovering New Meaning for AI and Beyond
While AI adds pressure to data centers and IT infrastructure in many ways (we’ve always needed an abundance of power and space, but the demands are skyrocketing even further when facing AI’d requirements), it also gives us wiggle room. Depending on the AI application, the data doesn’t necessarily need to be located in large, urban and populated centers. In fact, there are a number of ways that AI can get the processing it needs while expanding its data center map, including:
Batch Processing: Many AI applications, especially those that involve large-scale data analysis or training of machine learning models, can be executed in batch mode. This means that data can be collected and processed in intervals rather than requiring immediate, real-time processing, allowing for flexibility in data center location.
Training Tolerances: Not all AI applications are time-sensitive. Workloads that don’t require instantaneous responses — such as large-scale training of AI models or analytics that can be run periodically — can afford somewhat higher latencies, making them suitable for data centers located farther from users.
Data Preprocessing and Locality: Some AI workloads involve extensive preprocessing of data before the actual AI algorithms are applied. This preprocessing can be handled at edge locations or closer to data sources, with the refined data then sent to more distant data centers for deeper analysis.
Scalability and Flexibility: Data centers in less urbanized areas can be designed to scale rapidly, accommodating growing workloads as demand increases. This scalability can often outweigh the drawbacks of increased latency for certain applications.
Resource Optimization: Many AI workloads can be optimized for specific hardware configurations that are more readily available in dedicated data centers, regardless of location. This allows for high-performance computing capabilities that can compensate for the distance from end users.
With this newfound flexibility in AI, we’re allowing data center and AI customers to opt for more strategic investments in infrastructure with a better regional connectivity presence — and in areas where power and space are more available and cost effective. In many ways, the proliferation of AI has loosed up a long-constricted, rigid data center landscape.
An Edge Expert’s Opinion
Of course, we’re still in the middle of this story, and we can’t know the ending until the book comes to a close. However, as the future unfolds before us — and as we continue to build new value into our IT ecosystems — finding these synergies between AI and data center locations helps customers make decisions that suit their futures.
Here at 1623 Farnam, we’ve had the pleasure of seeing how the edge has been shaped and reshaped by evolving technologies, and from our point of view here at the edge in Omaha, we’re excited to see the new life taking shape.
As a facility delivering hyperconnected local, regional, national and international solutions, we know that the Heartland makes a great home for AI applications. Plus, we’ve long known about this area’s strategic renewable power resources, business incentives, low natural disaster risk and central location — so we know it’s an ideal location for any deployment. Still, as AI continues to rise, we’re excited to see the rest of the world expand their own horizons and take advantage of the value that waits here and in other up-and-coming markets.
For more on edge computing, check out this blog — or learn more about our facility’s location here.