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AI / HPC Hosting | Cloudnium

AI / HPC Hosting Knowledge

Understanding the infrastructure demands and differences of AI and HPC colocation.

Solutions Designed for Modern AI/HPC Workloads

High-Density Power Delivery

Cloudnium offers cabinets capable of sustaining over 40kW of continuous load with A/B redundant 208V and 3-phase options. Dynamic load balancing and per-circuit monitoring ensure resiliency and real-time management.

Cabinets are pre-equipped with smart PDUs capable of advanced telemetry, threshold alarms, and remote reboot support to keep critical workloads online without manual intervention.

Liquid & Immersion Cooling Options

Our AI datacenter pods are purpose-built for advanced thermal control, supporting rear-door heat exchangers, direct-to-chip liquid loops, and full immersion tank solutions.

Customers can choose traditional hot/cold aisle containment or opt into enhanced liquid-cooled rows with active monitoring for rack-level thermal optimization and energy efficiency.

High-Speed Cluster Networking

We deliver dark fiber paths, 100G/400G Ethernet, Infiniband, and RoCE-ready fabrics designed for AI model training clusters and distributed HPC frameworks.

Cross-connects and backbone links are provisioned with ultra-low latency in mind, ensuring AI clusters achieve synchronization speeds necessary for modern LLM training and deep learning pipelines.

Understanding the Demands of AI and HPC Hosting

Artificial Intelligence (AI) and High-Performance Computing (HPC) workloads have redefined what infrastructure must deliver. No longer are traditional enterprise hosting environments — designed around moderate-density servers and predictable traffic patterns — sufficient to meet the challenges of next-generation compute demands.

Training AI models now requires hundreds or thousands of high-wattage GPUs operating in tightly synchronized clusters. HPC workloads, from scientific research to financial simulations, demand extremely low-latency, high-throughput interconnects and massive sustained compute density.

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Massive Power Requirements

AI clusters routinely demand 20kW–40kW per rack, pushing facilities beyond conventional design thresholds.

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Advanced Cooling Challenges

Traditional air cooling often fails. Liquid, immersion, and rear-door heat exchanger solutions are rapidly becoming standard.

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Extreme Network Fabric Needs

HPC and AI training rely on sub-millisecond latency across thousands of nodes — requiring dark fiber, Infiniband, and 400G fabrics.

At Cloudnium, we engineer facilities capable of delivering this new scale of compute. Our AI-optimized datacenters enable customers to focus on training, deploying, and scaling — without constraint.

In this guide, we’ll explore the unique challenges of hosting AI and HPC environments, strategies for overcoming them, and why Cloudnium's infrastructure gives you a competitive advantage.

Unique Infrastructure Challenges for AI / HPC

Extreme Power Density

Traditional server cabinets were designed for loads between 2-5kW. AI and HPC deployments regularly exceed 20kW per rack, with many reaching beyond 30-40kW. This creates entirely new challenges for power provisioning, redundancy, and safety.

Facilities must offer multiple redundant 208V and 3-phase circuits, capable of dynamically adjusting to the draw from variable GPU workloads. Simply adding more outlets isn't enough — the entire electrical architecture must be built for persistent, high-wattage compute loads.

Advanced Cooling Requirements

Traditional air cooling quickly becomes insufficient once rack power exceeds 10kW. AI clusters generate sustained thermal loads that overwhelm basic CRAC-based systems. High-efficiency liquid cooling, rear-door heat exchangers, and immersion systems are no longer optional — they are critical.

Designing for cooling redundancy, containment airflow optimization, and scalable liquid loops are all essential to support dense deployments without thermal throttling or operational risk.

Low-Latency Networking and Fabric

AI training often requires parallel distributed computing across hundreds of nodes. High-speed networking — such as 100GbE, Infiniband, and RDMA fabrics — is vital to enable synchronized model updates, real-time data processing, and scalable distributed learning.

Cabling, switching architecture, and path optimization inside the datacenter must be pre-planned for high-throughput cluster fabrics, not just general-purpose IP networking.

Optimized Facility Layout

Physical layout impacts everything from cooling efficiency to cable management. AI deployments benefit from specially designed aisles, hot/cold containment, and modular scalable pods that can expand clusters without reworking infrastructure.

Planning the physical topology of your deployment early allows for seamless scaling as projects grow from a few racks to hundreds of GPUs across multiple aisles or even multiple datacenters.

Common Use Cases

Machine Learning Training

Support for multi-GPU rigs and long-duration batch jobs with redundant power and cooling.

Scientific Computing

Colocate HPC workloads for simulations, genomics, and physics with high compute density.

Private AI Cloud

Run proprietary LLMs and models in isolated, secured environments with cloud-like scale.

Have Questions About AI Infrastructure?

Our team is here to help you design, deploy, and scale your workloads efficiently.

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