How Chicago Data Centers Support AI and Machine Learning Projects

Artificial intelligence (AI) and its subset, machine learning (ML), have become hugely important to businesses. Businesses are therefore prioritizing AI and machine learning hosting options when choosing data centers. With that in mind, here is a quick guide to how Chicago data centers support AI and machine learning projects.

AI and machine learning in data centers

The move toward supporting AI and machine learning in data centers did not happen overnight. Here is an overview of the five key steps in the process (so far).

Shift toward high-density infrastructure

Traditional data centers were not designed for AI’s hardware-intensive requirements. Over time, providers began offering high-density racks with support for GPUs, TPUs, and advanced cooling systems to meet AI workloads.

Integration of specialized hardware

AI hosting accelerated with the adoption of hardware specifically designed for ML tasks, such as NVIDIA GPUs and custom AI chips. These innovations enabled faster model training and more efficient inference processing.

Enhanced network capabilities

AI applications, especially distributed training and edge-AI models, require high-bandwidth, low-latency networking. Data centers have evolved to support technologies like InfiniBand and high-speed Ethernet to facilitate rapid data exchange.

Scalable storage architectures

As AI projects grew, so did data storage needs. Hosting providers began deploying scalable, high-throughput storage systems to handle large datasets, backups, and real-time access.

Energy efficiency and sustainability

Given AI’s high energy demands, newer facilities are focusing on sustainable operations, including liquid cooling, energy-efficient hardware, and renewable energy integration.

Infrastructure requirements for AI hosting

AI hosting brings its own set of infrastructure requirements. Here are the 6 main ones.

Security and compliance

Although not specific to AI hosting, the use of AI-driven workflows does tend to create additional security and compliance challenges. This is because AI is often used for big data analysis, and this analysis may involve sensitive and/or protected data.

High-density compute power

AI training and inference demand powerful hardware, particularly GPUs, TPUs, or other accelerators. These systems must support parallel processing and handle large-scale mathematical operations efficiently. Dense rack configurations with multi-GPU servers are common in AI hosting environments.

Scalable storage solutions

AI workloads involve large datasets, often in the range of terabytes or petabytes. Fast, scalable storage systems such as NVMe SSDs and distributed storage architectures are crucial for managing training data, model checkpoints, and output efficiently.

High-speed networking

Low-latency, high-bandwidth networking is vital for moving data quickly between nodes and storage, especially in distributed training scenarios. AI hosting infrastructure should support technologies like InfiniBand or 100GbE to ensure minimal bottlenecks.

Advanced cooling systems

High-performance hardware generates substantial heat. Efficient and scalable cooling systems, such as liquid cooling or high-efficiency air systems, are required to maintain performance and hardware longevity, especially in high-density environments.

Power availability and redundancy

AI workloads are resource-intensive and require stable, continuous power. Hosting environments must provide high power densities per rack, along with redundant power sources (UPS, backup generators) to ensure uptime.

Best Chicago data centers for AI projects

If you’re looking for a Chicago data center for an AI project, here are four excellent options.

DataBank ORD1

Located in the heart of Chicago’s Financial District, DataBank’s ORD1 facility is a premier carrier-hotel site designed for high-performance computing and interconnection.

With over 10,000 sq ft of raised floor and roughly 1 MW of critical IT load, it supports dense rack configurations ideal for GPU-based AI/ML workloads. ORD1 features 18+ onsite carriers, multiple fibre entries, and diverse network paths. This makes it an excellent choice for latency-sensitive applications like real-time inference or edge AI.

The facility’s Tier III design and redundant 2N power configuration ensure resilience. Its proximity to key network hubs gives AI teams fast access to data sources, clouds, and partners.

DataBank ORD2

ORD2 is another downtown site offering 11,470 sq ft of raised floor and up to 2 MW of critical load. It provides a balance between accessibility and performance for AI development, testing, or production deployments.

Like ORD1, ORD2 benefits from Chicago’s dense network ecosystem, giving enterprises low-latency connections to major carriers and cloud providers.

The facility’s redundant infrastructure, robust security controls, and managed service options make it a strong option for mid-sized AI environments requiring a secure, high-uptime footprint close to Chicago’s urban innovation corridor.

DataBank ORD3

Located in the suburbs northwest of downtown, ORD3 offers nearly 29,000 sq ft of raised-floor space and 2.7 MW of critical IT capacity. This facility is ideal for organizations scaling AI training or data-processing clusters that demand more space and power.

With redundant N+1 cooling, multiple fiber providers, and easy access to O’Hare, ORD3 balances performance and cost efficiency. It’s particularly suited for AI workloads that require sustained compute but not immediate downtown proximity.

DataBank ORD4

ORD4 is DataBank’s largest Chicago-area facility, with 77,510 sq ft of raised floor and 8.75 MW of critical power. Designed for high-density deployments, it supports 10–25 kW+ per cabinet. This makes it perfect for large-scale AI/ML training using GPU or HPC clusters.

With 14 carriers onsite, direct cloud on-ramps, and extensive room for expansion, ORD4 offers both scalability and efficiency. It’s a prime choice for enterprises building or scaling sophisticated AI infrastructures across hybrid or multi-cloud environments.