How Do You Design Infrastructure for AI Workloads?

 

The artificial intelligence revolution is reshaping how we think about data center infrastructure. Traditional IT systems used about 12kW per rack. Today, AI needs more than 40kW per rack, and that number may double by 2026. This shift isn’t just about more power, it’s about rethinking how we design and run data centers.

 

If you’re tasked with supporting AI initiatives at your organization, you’re facing a complex challenge that goes far beyond simply adding more servers. The question isn’t whether your current infrastructure can handle AI workloads, it’s how quickly you can adapt before your competitors leave you behind.

 

What Makes AI Infrastructure Different?

 

AI workloads put new pressure on data centers. Instead of handling tasks one at a time, AI runs many processes at once, using huge datasets. It also needs real-time speed, which can affect business results.

 

The numbers tell the story: according to recent industry analysis, AI infrastructure spending is projected to reach $202 billion by 2025. This isn’t just growth, it’s a big change in how infrastructure supports business operations.

 

The Power and Cooling Challenge

Traditional CPU-based workloads make steady heat that normal cooling systems can handle. But AI systems with GPUs and specialized processors create much more heat, and standard cooling systems can fail within minutes.

 

“The AI infrastructure revolution demands fundamental rethinking of data center design, deployment strategies, and partnership models,” notes a recent industry report from Introl. This isn’t hyperbole, it’s the reality facing infrastructure teams worldwide.

 

Consider these critical differences:

• Power density: AI racks use 3 to 4 times more power than traditional servers
• Heat generation: GPU clusters produce concentrated heat loads requiring liquid cooling for AI
• Network throughput: AI training requires 10-100x higher bandwidth than typical applications
• Storage performance: Model training demands ultra-low latency (the delay before data moves) storage with massive throughput

 

Essential Components of AI Infrastructure Design

 

1. Power Infrastructure Planning

Power is the foundation of any AI setup. Unlike normal IT systems, which use power in steady amounts, AI workloads can spike suddenly—especially during training. That means your power system must support both everyday operations and big bursts of demand.

Key points to plan for:

• Redundant power feeds: These are backup power lines that keep servers running if one line fails. Designs often follow N+1 (one extra backup system) or 2N (a full duplicate system) setups. Both prevent AI training from stopping if something breaks.
• High-efficiency UPS systems: A UPS (Uninterruptible Power Supply) is a battery backup that keeps servers online during short outages. AI racks use much more power than traditional ones, so the UPS must handle higher power levels.
• Power Distribution Units (PDUs): These are smart outlets for server racks. Advanced PDUs can track how much power each rack is using and prevent overloads.
• Generator backup: Many data centers use diesel or natural gas generators. For AI, generators must be sized to handle long training sessions, not just short blackouts.

 

Getting power wrong can be extremely costly. Training a large AI model once can cost hundreds of thousands of dollars. If the power fails mid-training, all that money and time is wasted. So reliable power isn’t optional, it’s essential.

 

2. Advanced Cooling Solutions

Normal air cooling can handle about 15–20 kilowatts (kW) of power per rack. But AI workloads often use 40kW or more, and some setups can even reach 100kW per rack. Because of this, traditional cooling systems are not enough.

To handle these high heat levels, liquid cooling is now essential. Instead of blowing cold air, liquid cooling uses water or special fluids to pull heat away from the computer chips. Direct-to-chip cooling places liquid channels right on the processors, which removes heat more effectively than air and also uses less energy.

Cooling strategies to consider:

• Liquid cooling loops: Circulate coolant through GPU clusters and powerful processors to keep them from overheating.
• Hybrid cooling systems: Combine air and liquid cooling. For example, air handles lighter loads while liquid handles the hottest parts.
• Precision cooling: Systems that adjust quickly when heat levels rise or drop, keeping temperatures stable in real time.
• Heat recovery systems: Capture waste heat and reuse it, such as heating parts of a building.

 

3. Network Architecture for AI

AI creates an enormous amount of data that regular networks often can’t handle. Training AI models means moving terabytes of data (thousands of gigabytes) between storage systems and compute nodes (the servers that do the heavy processing). To work properly, the network must move this data very quickly and with almost no delay.

Key network requirements:

• High-bandwidth interconnects: These are very fast data links, often 400 gigabits per second (Gb/s) or more, that connect servers together so they can share data without slowing down.
• Low-latency switching: Latency means delay. Low-latency switches move data faster, which shortens training times and improves AI performance.
• Dedicated storage networks: Separate networks just for AI data traffic. This prevents AI from slowing down other business applications that share the same systems.
• Network monitoring: Tools that constantly watch the network to find bottlenecks (slow points where data gets stuck) before they disrupt training.

 

4. Storage Systems Optimization

AI storage works very differently from regular IT storage. Training AI models uses massive datasets, sometimes measured in petabytes (millions of gigabytes). The system also has to respond in microseconds (less than a millionth of a second). Because of this, storage must balance three things: size, speed, and cost.

Key storage options:

• Parallel file systems: These systems split data across many paths, like a highway with lots of lanes. This lets data move faster during training.
• NVMe-based storage: NVMe (Non-Volatile Memory Express) is a very fast type of memory used for active data and model checkpoints. It helps training run without delays.
• Object storage: This is used for long-term storage of datasets and older versions of models. It’s slower but more cost-effective.
• Data tiering: This strategy automatically moves data to the right type of storage. Frequently used data stays on faster systems, while older or less-used data moves to cheaper storage.

 

The Six-Phase AI Infrastructure Design Process

 

Phase 1: Workload Assessment and Requirements Analysis

Before building your infrastructure, you need to know what kind of AI work you’ll be running. Different AI applications, like image recognition or language models, have very different needs.

Things to check:

• Compute requirements: Decide how many CPUs (general-purpose processors) and GPUs (special processors for AI) are needed, along with memory and speed.
• Data needs: Look at how big your datasets are, how often they’ll be used, and how long they need to be stored.
• Performance goals: Set targets like how fast training should run or how quickly the system must respond (latency).
• Scalability: Plan for growth, seasonal spikes, or heavier workloads in the future.

 

Phase 2: Power and Space Planning

Once you know your workload needs, you can figure out how much power and physical space your systems will require. Many facilities discover they need upgrades before they can handle AI.

Planning steps:

• Add up total power use from compute, storage, networking, and cooling.
• Map power density (how much power is used in each area) to place equipment in the best spots.
• Calculate cooling needs based on how much heat the systems generate.
• Use space efficiently so you can fit more AI systems without wasting room.

 

Phase 3: Infrastructure Architecture Design

Here you turn your needs into a clear design plan. The design should solve today’s problems while staying flexible for tomorrow.

Design elements:

• Modular infrastructure: Build in small units that can grow step by step.
• Backup systems: Make sure no single failure can stop the system.
• Integration: Ensure the new setup works with existing tools and workflows.
• Monitoring systems: Track performance so issues can be fixed early.

 

Phase 4: Technology Selection and Procurement

With the design ready, you now pick the actual hardware and software. This stage is about balancing performance, cost, and long-term support.

Selection criteria:

• Performance benchmarks (tests) that match your AI workloads.
• Total cost of ownership—including power, cooling, and future maintenance.
• Vendor compatibility—making sure different products work well together.
• Strong support options for complex, business-critical systems.

 

Phase 5: Implementation and Integration

This is where everything comes together. The challenge is to install new technology without disrupting your current operations.

Implementation steps:

• Roll out in phases so performance can be tested before going all-in.
• Use testing protocols to check how the system works with real AI jobs.
• Train staff on how to use and manage the new tools.
• Document everything—settings, processes, and troubleshooting guides.

 

Phase 6: Optimization and Scaling

AI infrastructure isn’t a one-time setup. It must keep improving as workloads change and new technology appears.

Optimization areas:

• Monitor performance to find slow spots and fix them.
• Plan capacity based on real usage and future growth.
• Refresh old technology to stay competitive.
• Keep costs low by improving energy efficiency and using resources wisely.

 

Critical Success Factors

 

Scalability and Flexibility

AI workloads are inherently unpredictable. A successful research project can suddenly require 10x more compute resources, while changing business priorities can shift focus to entirely different AI applications. Your infrastructure must accommodate this uncertainty.

Scalability strategies:

• Modular designs that can grow step by step without major redesigns
• Cloud integration for burst capacity and specialized AI services
• Standardized components that simplify expansion and maintenance
• Automation that reduces the operational complexity of scaling

 

Security and Compliance

AI systems often process sensitive data and generate valuable intellectual property. Your infrastructure must protect these assets while enabling the collaboration AI development requires.

Security considerations:

• Data encryption at rest and in transit
• Access controls that limit exposure while enabling necessary collaboration
• Audit capabilities for compliance with industry regulations
• Incident response procedures specific to AI infrastructure

 

Cost Management

AI systems cost a lot, so they need to show clear business value. Effective cost management requires understanding both direct infrastructure costs and the business impact of performance limitations.

Cost optimization approaches:

• Right-sizing infrastructure to match actual workload requirements
• Utilization monitoring to identify underused resources
• Energy efficiency measures that reduce ongoing operational costs
• Lifecycle planning that improves refresh cycles and technology transitions

 

Real-World Implementation Insights

 

In our work with clients deploying AI infrastructure, we’ve observed several patterns that separate successful implementations from those that struggle to deliver value.

 

Successful projects typically:

• Start with pilot implementations that validate assumptions before major investments
• Involve cross-functional teams that understand both AI requirements and infrastructure constraints
• Plan for operational complexity from day one, not as an afterthought
• Establish clear performance metrics that tie infrastructure capabilities to business outcomes

 

Common pitfalls include:

• Underestimating cooling requirements, leading to thermal throttling and reduced performance
• Inadequate network planning that creates bottlenecks during critical training phases
• Insufficient power infrastructure that limits the ability to grow and creates reliability risks
• Lack of monitoring and management tools that make troubleshooting nearly impossible

 

The Path Forward

 

Designing infrastructure for AI workloads requires expertise across multiple domains, from power and cooling to networking and storage. The stakes are high: inadequate infrastructure can derail AI initiatives, while well-designed systems enable breakthrough capabilities that transform business operations.

 

The key is working with partners who understand both the technical complexities of AI infrastructure and the business imperatives driving AI adoption. At Camali Corp, we’ve spent over 35 years helping organizations design, build, and maintain critical infrastructure that supports their most important initiatives.

 

Whether this is your first AI project or you’re growing an existing one, your choices today will affect your AI for years. The real question is: can you afford not to invest in the right infrastructure?

 

Ready to design infrastructure that unleashes your AI potential? Contact our team to discuss your specific requirements and learn how we can help you build the foundation for AI success.