AI and IT
The Infrastructure Challenge For Deploying AI At Scale: Edge, Data Centres & Area Distribution Nodes
- What are the infrastructure considerations for AI deployment across distributed environments?
- How have prefabricated modular systems emerged as a viable solution to support mission-critical compute applications?
- What solutions do Workspace Technology offer?
Introduction
The rapid evolution of artificial intelligence—particularly in large language models, multi-domain decision support systems, and real-time analytics—is radically transforming how organisations approach IT infrastructure. For defence, government, and enterprise users alike, the challenge is no longer limited to centralised processing. Instead, it now requires scalable, secure, and efficient compute capacity at the edge, within Area Distribution Nodes (ADNs), and across decentralised, low-latency environments.
This article explores the infrastructure considerations for AI deployment across distributed environments and how prefabricated modular systems have emerged as a viable solution to support mission-critical compute applications.
1. AI at the Edge: A New Paradigm
Traditional cloud and data centre architectures struggle to meet the real-time requirements of AI workloads when deployed remotely or at the tactical edge. Applications such as:
- Federated learning
- Target recognition and image classification
- Real-time threat detection
- Multinodal decision support (e.g., CJADC2 in defence contexts)
...require local processing capability to reduce latency, preserve bandwidth, and maintain resilience when disconnected from central networks.
Edge compute nodes must be rugged, compact, and capable of housing high-density IT loads in potentially austere or contested environments.
2. Infrastructure Requirements for AI Deployment
Whether at the core or the edge, AI deployments share common infrastructure needs:
a. High-Density Compute Support
AI training and inferencing tasks demand GPUs or AI accelerators that often exceed 30–60kW per rack. Supporting this density requires robust power delivery, efficient airflow management, and a range of cooling systems.
b. Low Latency & Data Locality
Applications like predictive maintenance or autonomous systems rely on sub-second response times, necessitating compute resources close to data sources—either on-premises or within area distribution nodes.
c. Scalability & Modularity
Given the evolving nature of AI models and datasets, infrastructure must be scalable. Modularity allows operators to deploy infrastructure incrementally (pay-as-you-grow) and reconfigure it as requirements shift.
d. Security & Sovereignty
AI systems often handle sensitive data. For defence, intelligence, and regulated sectors, this requires physical and electromagnetic protection (e.g., SCIFs, NPSA, ICD-705 compliance) and strict access control.
e. Speed of Deployment
Time-to-field is mission-critical in both defence and commercial arenas. Infrastructure that takes 12–18 months to deploy risks being overtaken by technology shifts, operational demands, and competitive pressures before it’s even live. Prefabricated, factory-tested systems, deployable in under 6 months, keep pace with AI innovation, accelerate operational readiness, and deliver a faster return on investment.
3. Area Distribution Nodes (ADNs) & AI
Originally designed to support network segmentation and distribution, ADNs are increasingly being equipped to host edge compute for AI. Their positioning between local users and core data centres makes them ideal for:
- Hosting shared AI processing for a region
- Supporting temporary or expeditionary operations
- Acting as redundant compute in disconnected or degraded environments
Modern ADNs now demand:
- Integrated power and cooling with N+1 resilience
- Secure network segmentation
- High-availability AI hardware racks
- Environmental control and remote management
4. The Role of Prefabrication in AI Infrastructure
Prefabricated, modular data centres are now a central enabler for scalable AI deployment. They offer:
- Factory-controlled build quality and rapid on-site delivery
- Reduced on-site construction complexity, critical in remote or secure areas
- Portability and redeployment potential—key for expeditionary or temporary operations
- Flexible customisation to support compute, storage, or SCIF functions
Recent deployments across NATO partners have demonstrated the viability of prefabricated DataCube® and SecureCube® nodes for AI-capable edge compute and command functions.
5. Summary: Workspace Technology’s Solutions
Workspace Technology has been at the forefront of this evolution, supporting AI and distributed compute projects across military and commercial sectors. Our UK-manufactured prefabricated solutions offer:
- DataCube®: Prefabricated data centres supporting high-density compute (up to 60kW/rack), modular UPS, aisle containment, and rapid deployment (as fast as 6–7 months).
- SecureCube®: SCIF-compliant buildings with acoustic protection, RF shielding, and physical access control for classified or sovereign AI applications.
- Telecoms Hubs: Compact edge nodes with integrated HVAC, UPS, and SATCOM readiness—ideal for squadron or brigade-level deployments.
Our infrastructure is built in the UK, aligned with NATO and US DoD standards, and designed for long-term flexibility, including relocation and future expansion.
To explore how we can support your AI deployment strategy—whether at HQ, at the edge, or in the field — contact our team:
Email: sales@workspace-technology.com
Telephone: +44 (0)121 354 4894
Web: workspace-technology.com