How to Optimize AI Workloads in a Data Center

Any conversation about optimization needs to begin with the physical and architectural foundation supporting AI. These workloads reshape demand patterns in ways traditional data center designs cannot fully handle. Many organizations now expect AI-ready capacity to rise sharply, with projections showing growth of around 33% per year through 2030 and roughly 70% of total demand becoming AI-ready.  

At the same time, operators are reporting rapid changes in rack density. Typical values used to sit around 4–6 kW, but surveys now show a clear rise toward 7–9 kW, with some extreme cases exceeding 50 kW per rack. These figures illustrate how optimization must consider density distribution and placement rather than treating the entire data center floor the same.  

Electricity consumption is another pressure point. Analysis suggests global data center usage may reach nearly 945 TWh by 2030, nearly doubling from recent baselines, and accelerated AI servers drive a large share of that growth. These numbers help explain why optimization begins with power and thermal planning, not tuning software alone. 

We also see global capacity expanding roughly 15% each year because of AI. That expansion makes workload distribution an essential part of any plan to optimize AI workloads in a data center. Placing every model in the same spot is rarely efficient anymore. Instead, teams explore hybrid placement frameworks to ensure the environment supports both performance and sustainability. 

A strong infrastructure foundation leads naturally to the next requirement: solving power, cooling, and density challenges. Nearly every source in the extraction points to thermal behavior as a primary risk factor for AI workloads.  

Certain facilities have experienced outages tied directly to cooling failures, and in some locations, cooling alone accounts for up to 40% of all energy usage. This trend becomes more visible as AI clusters heat up faster and require more uniform cooling across dense racks. 

AI training clusters often reach power draws exceeding 100 kW per rack. That level of density cannot rely solely on traditional air cooling. Direct-to-chip liquid cooling, immersion systems, and redesigned airflow paths offer the stability necessary for these loads.  

Industry writers describe this shift as a move into “uncharted” thermal territory, which is one reason optimizers look for more adaptive and efficient systems. When a workload regularly maxes out GPU utilization, temperature fluctuations can shape performance more than expected. 

Energy efficiency benchmarks tell another part of the story. Older sites may hover around a PUE of 1.56, while newer designs achieve around 1.3. That contrast matters because small improvements in efficiency scale dramatically across large AI clusters. When power draw rises with every generation of hardware, lower PUE becomes part of the optimization strategy instead of an optional advantage. 

Different cooling methods change the limits of what an operator can safely support. 

• Air cooling remains suitable for moderate-density racks but begins to struggle as GPU clusters expand. 

• Direct-to-chip liquid cooling offers more stable thermals for high-density environments that host multi-GPU nodes. 

• Immersion cooling pushes the efficiency envelope even further, especially in extremely high-density scenarios. 

After addressing power and cooling, optimization shifts to the bigger architectural question: Where should each AI workload run?  

One reason this matters is that training and inference behave very differently. Training tends to require large GPU clusters, high-bandwidth interconnects, and steady data flow. Those characteristics match best with centralized cloud or colocation data centers. Meanwhile, inference can sit closer to end users through edge computing so that responses arrive quickly and bandwidth costs remain predictable. 

Hybrid strategies place training in high-density data centers, versioning and governance in centralized environments, and inference across edge sites. This also matches the rise of modular and hybrid data centers, which support rapid scaling and situational flexibility. Some teams want to scale hardware quickly without committing to full-size builds, and modular facilities help fill that gap. 

Optimization must also minimize unnecessary data movement. Training pipelines often require massive datasets, and if they travel frequently between locations, they increase both cost and latency.  

On the other hand, inference endpoints benefit from staying close to where decisions happen, especially in industrial IoT or healthcare environments. Good placement ensures that models run efficiently while still complying with data residency rules. 

As AI adoption grows, automation and governance shape how well workloads perform in real environments. The average breach costs sit around USD 4.44 million, according to the 2025 IBM report. It also shows that 97% of AI-related breaches occurred in organizations lacking proper AI access controls. A number like that forces operators to rethink how they manage identity, access, and monitoring. 

Security AI appears more frequently in data center operations than before. Automated detection tools reduce breach lifecycles by identifying suspicious behavior faster than manual review. This trend ties directly to optimization because improved security reduces operational risk, and stable environments allow AI workloads to compute without interruption. 

Automation extends beyond security. Predictive analytics help anticipate load changes so that systems scale GPU and CPU resources proactively. For example, autoscaling frameworks can avoid bottlenecks by allocating additional compute precisely when demand rises. Monitoring platforms track thermal behavior, power usage, and performance anomalies. Instead of reacting after a failure, teams detect patterns early. 

Governance frameworks add another dimension. NIST’s AI RMF and ISO/IEC 42001 outline principles for observability, lifecycle management, and risk controls. These standards give operators guidance for structuring workflows and audit trails.  

On the regulatory side, obligations under the EU AI Act, especially for systemic-risk foundation models, require adversarial testing, incident reporting, and a strong cybersecurity posture. All these expectations lead to environments where AI workloads run more predictably because monitoring and compliance tools reduce uncertainty. 

Automation improves the flow of work inside the data center. 

• Autoscaling assigns GPU or CPU resources based on predicted demand rather than static thresholds. 

• Monitoring tools track temperature, power, and component behavior, raising alerts before a problem amplifies. 

• Data-pipeline orchestration removes friction from training workflows by ensuring each stage of data movement is coordinated. 

As AI continues reshaping data center design, optimization depends on combining placement, power, cooling, automation, and governance into a coherent strategy. These pieces determine how efficiently an organization can compute as AI models grow larger and more computationally intense. Teams that understand these relationships gain the flexibility to adapt quickly while keeping performance steady. 

If your organization is working to optimize AI workloads in a data center, we can help design or refine the infrastructure supporting them. At OTAVA, our cloud, colocation, and hybrid environments are built to handle high-density compute, and our managed services add resilience, observability, and security.  

Reach out to our team to plan, upgrade, or deploy environments that keep your AI workloads running reliably at scale.