Breaking down AI applications: edge versus inference
Apr 23, 2025
By Adolfo Morales, Global Alliances Manager at Eaton-D-IT
As artificial intelligence (AI) continues to expand in both scope and magnitude, it has become more important than ever for channel partners to be aware of the intricacies involved in the deployment of AI systems. While there has been much excitement surrounding AI, the increasingly mainstream computer science can also spark some confusion. For many, one area of uncertainty exists around the differences between edge AI and inference models. Yet while each space poses specific power protection requirements, you can rest assured that Eaton offers solutions tailored to both. Channel partners selling Dell and HP AI servers can even cash in on additional discounts and higher margins.
As your customers scramble to keep pace with rapidly changing AI specifications, Eaton can help you quote, configure and deploy the optimal solutions.
Edge vs. Inference: know the difference
First, it is essential to understand the distinctions between the two types of AI models. The “standard” AI model is based on two main elements: training and inference modules. Training refers to the process in which AI learns from data, like a teacher instructing a student. This data can come from various sources, databases or even another trained AI, which is known as distillation. Inference, on the other hand, signifies the method of AI that a trained model uses to make predictions or decisions based on input data. Continuing the example above, inference would be like the student taking a test or solving a real-world problem. While edge AI systems are able to unify training and inference modules, they must be located close to the user, the very definition of edge. Inference modules, however, have the flexibility to reside either in the cloud or in a physical format near the end user. It is important to note that inference systems must be able to scale in computing capacity anytime.
As is evident from recent announcements from several companies focused on GPU production, we can conclude that AI systems will increase in power density (KW/area). However, at this time, edge AI is not scaling dramatically, as the applications are very defined. Currently, edge AI systems with computing and inference capabilities can reside directly at the user's site with very low power consumption, latency and data handling requirements. Still, size requirements will remain a limitation for the model's capabilities in general. Yet if we identify latency as a metric that must be reduced for better reaction and response, then inference modules should be designed in a way that situates them very close to the users but within a highly protected and monitored environment.
Solutions built for both AI models
Eaton makes it easy for you to supply the solutions your customers need to satisfy both types of AI scenarios. For edge AI customers, who are likely deploying a HPE ProLiant DL145 or a Lenovo ThinkEdge SE100, channel partners can select from a variety of racks, uninterruptible power systems (UPSs), power distribution units (PDUs), aisle containment systems and more. For example, Eaton’s small wall-mount racks (2U to 26U) and server cabinets are ideal for edge AI deployments, as is the 9PX G2 double-conversion online UPS. Horizontal rack PDUs, which are available within our portfolio of ready-to-deploy managed rack PDUs, represent another essential solution. Eaton also offers a variety of options in network and connectivity, including both fiber and copper cables.
When seeking a more robust backup solution for inference modules, where you probably bring to the space at least a Dell PowerEdge R740 or maybe something even bigger like a XE9680 air cooled, Eaton’s upcoming 93PX rack-mount 3-phase UPS ― which can be easily deployed and configured with N+1 redundant capacities up to 40kW+1 ― represents an ideal fit, with both 208V and 400V models. In addition, the HDX G4 PDU provides up to 100 amps with 36kW, while extra-deep 54” racks with up to 5000 lbs. static weight capacities such as the Heavy-Duty SmartRack Enclosures meet increasing computing requirements. Eaton’s fiber connectivity equipment, as well as the comprehensive line of KVM switches and console servers, complement the range of solutions available for inference applications.
Cooling and control for every scenario
Finally, Eaton also fulfills two major product needs that apply to both edge AI and inference systems: cooling and security/monitoring. While each of the two AI models requires a different cooling density, Eaton’s range of options for in-row and rack cooling span a variety of sizes and applications. In addition, to ensure your customers maintain the security and monitoring capabilities required for uptime and disaster avoidance, you can supply the Brightlayer Data Center suite. The modular software solution provides a robust set of features with ease of management and the ability to scale.
Understanding the differences between AI model types ― and the unique considerations for each ― is necessary to help your customers determine and deploy the correct solution across today’s fast-moving AI environment. With a wide variety of products and a clear commitment to the white space, Eaton provides the partnership you need to thrive in both AI physical layers.