Comparing Data Center Requirements: AI Inference vs AI Training

As AI models mature and become integrated into everyday applications, the industry is experiencing a shift toward AI inference—the ongoing process of putting developed and tested models to work to create, make decisions, and predictions based on real-time data rather than training data.

While AI training and inference are often grouped together under the umbrella of “AI workloads,” they possess fundamentally different operational profiles. For data center architects, hyperscalers, and enterprise IT leaders, treating them as identical can lead to massive inefficiencies. Here is a breakdown of how data center infrastructure requirements diverge between AI training and AI inference.

Throughput vs. Latency

To understand the infrastructure, it helps to understand the goal.

AI Training is the process of teaching a model. It involves feeding massive datasets into neural networks, tweaking billions or trillions of parameters over weeks or months. It is like sending a student to medical school. The primary performance metric here is throughput—crunching the maximum amount of data in the shortest total time.

AI Inference is the model in action. Every time someone asks a chatbot a question, generates an image, or rides in an autonomous vehicle, inference is taking place. An AI model is making split-second decisions using real-time data. The primary performance metric here is latency. A model must deliver a unique response as quickly as possible.

Compute Hardware and Density

The hardware inside the racks in a data center looks vastly different depending on the type of workload. Training models requires raw, unadulterated compute power. Think thousands of the highest-end GPUs (like NVIDIA H100s or next-generation B200s and Rubin chips) working in parallel. The goal is maximum node count and massive VRAM to handle immense datasets.

AI inference requires significantly less computational brute force per request. While large language models (LLMs) still require GPUs, inference servers can often utilize mid-tier GPUs, CPUs, or specialized AI accelerators (ASICs) optimized for cost-per-query rather than maximum throughput. However, as new “reasoning” models (which “think” before they answer) become more common, inference compute demands are rising.

Power and Thermal Management

AI is notoriously power-hungry, but the density of that power dictates the cooling infrastructure.

Training workloads are heavy, sustained, and relentless. A single rack of modern training GPUs can consume anywhere from 100kW to more than 300kW of power. Air cooling is physically incapable of dissipating this much heat. Therefore, training data centers strictly require advanced Direct-to-Chip Liquid Cooling (DLC) or rear-door heat exchangers.

Because inference compute power is distributed across smaller, faster requests, inference racks historically operate at lower densities (typically 30kW to 150kW per rack). While still much higher than traditional cloud computing, this often allows operators to use advanced air cooling or hybrid liquid-air cooling systems, providing more flexibility in facility design.

Networking and Interconnects

Where the data travels dictates the network architecture of a facility. When thousands of GPUs are training a single model, they must constantly share parameter updates with one another. Therefore, the internal network is the bottleneck. Training clusters require incredibly complex, high-bandwidth, and low-latency internal fabrics (such as InfiniBand or NVLink) to keep the GPUs synchronized. The external connection to the internet is a secondary concern.

Inference nodes work largely independently of one another. The internal “east-west” traffic between servers is minimal. Instead, the priority is the external network (“north-south” traffic). The data center needs robust, low-latency fiber connections to the outside world to ensure end-users receive instant responses to their prompts.

Geographic Location and Site Selection

The physical location of the data center is arguably where training and inference diverge the most. Training infrastructure is reliant on massive amounts of power, but it does not interact with end-users in real time. So, it does not matter if the training data center is 50 miles away or 5,000 miles away. As a result, training data centers are built wherever land and electricity are cheapest and most abundant—often in rural areas or directly adjacent to power plants.

Inference is entirely dependent on user experience. If a fraud detection algorithm or a customer service bot takes too long to respond, the application fails. Therefore, inference infrastructure must be deployed at the “edge” or in metro-adjacent areas, physically closer to population centers to reduce round-trip network latency.

RAEDEN Specializes in Adapting Existing Structures for AI Inference

To sum it up, the AI infrastructure buildout is not a monolithic undertaking. Training requires isolated, power-dense fortresses of maximum compute, while inference demands highly connected, geographically distributed outposts optimized for speed and efficiency. As inference inevitably overtakes training as the dominant AI workload, data center design will continue to evolve to meet this new reality.

Many older industrial and commercial buildings are perfect candidates for adaptive reuse, and they are located in highly desirable urban or suburban areas, including urban centers. Because Raeden is a hybrid real estate and data center operations firm with experts in both fields at the helm, we are able to find viable sites in highly sought-after urban areas when other site selection teams can’t. These locations offer the strategic advantages of dense network infrastructure, latent power resources, and proximity to users. Reduction in latency for inference results in cost cuts by minimizing the need for long-distance data routing and improves user experience. RAEDEN has the experience, the know-how, and networks of relationships across public and private sectors to bring complex projects to fruition on strict timelines.

If you need data center space for AI inference, the adaptive reuse of existing commercial space could meet your infrastructure goals. Contact us to start your site search.