The future of AI Infrastructure

A technical guide to Scaling AI Workloads

In the realm of artificial intelligence and deep learning, infrastructure is as crucial as the algorithms themselves. With the increasing complexity of Large Language Models (LLMs) and the growing demand for GPU-accelerated computing, datacenter infrastructure faces unprecedented challenges. In this technical analysis, we examine the specific requirements for AI workloads and how the right colocation strategy can make the difference.

Power requirements for modern AI Computing

The latest generation NVIDIA H100 Tensor Core GPUs, built on the Hopper architecture, demand significantly more power than their predecessors. A single H100 PCIe GPU has a thermal design power (TDP) of 350W, while the SXM5 variant can consume up to 700W. For a typical AI training setup with 8 GPUs, this means:

• Base GPU consumption: 8 x 700W = 5.6kW

• Supporting CPUs (2x Intel Xeon Platinum): ~500W

• Memory and storage: ~400W

• Cooling and overhead: ~1.5kW

• Total per rack: 8-12kW

When scaling to multiple nodes for distributed training, these numbers can quickly escalate to 15-30kW per rack.

Cooling challenges

With such extreme power density, traditional air cooling often becomes insufficient. Modern solutions we support include:

• Direct-to-chip liquid cooling

• Immersion cooling with dielectric fluids

• Rear-door heat exchangers with optimized airflow

Network architecture for distributed AI

For effective distributed training, an ultra-low-latency, high-bandwidth network is essential:

• InfiniBand NDR 400Gb/s for inter-GPU communication

• RoCE (RDMA over Converged Ethernet) for distributed dataset access

• Direct AMS-IX connectivity for edge inferencing and model deployment

Our solution

At Datacenter Broker, we understand these complex requirements and help match you with colocation providers that can support:

• High-density power delivery (up to 50kW per rack)

• Advanced cooling solutions for optimal GPU performance

• Ultra-low latency networking through AMS-IX

• Scalable infrastructure for future expansion

• Green energy options for sustainable AI development

Power distribution considerations

Modern AI workloads require sophisticated power distribution systems:

• Busway power distribution systems rated for 400A or higher

• Three-phase power delivery to support high-density racks

• Redundant UPS systems with lithium-ion batteries for improved power density

• Real-time power monitoring and dynamic load balancing

Storage architecture trequirements

AI training demands a specialized storage architecture:

• High-performance parallel file systems (e.g., Lustre, GPFS)

• NVMe over Fabric for distributed storage access

• Tiered storage with hot tier on NVMe and cold tier on high-capacity HDDs

• Cache coherency protocols for distributed training

Our extensive network of colocation partners includes facilities specifically designed to handle these demanding requirements. We help you find the perfect match for your AI infrastructure needs while optimizing costs and ensuring scalability for future growth.

Contact us to discuss how we can help optimize your AI infrastructure deployment through strategic colocation choices.