In this blog, we’ll explore the key imperatives driving this evolution and how architecture is reshaping enterprise AI. Discover how colocation providers, like Digital Realty, are at the heart of this foundational shift.

The parallel imperatives of distributed private AI

The nature of traditional centralized AI architecture can force organizations to make compromises. Often on data sovereignty and security by sending sensitive data to public clouds. This architectural approach no longer meets the performance and control needs of the modern enterprise.

The development of enterprise AI has followed a trajectory similar to the cloud adoption lifecycle. In the initial stages, many organizations shifted workloads to the public cloud for greater speed and scalability, often trading off some control over data and security. Over time, hybrid cloud has become favored as it provides the flexibility to leverage public cloud resources while maintaining security and control.

The same is now occurring with AI.

Centralized AI architectures aligned with the technical requirements for building large models. These models demanded significant compute resources, making distributed computing less practical. As a result, centralization became the preferred solution.

As AI technologies continue to evolve, there is a growing trend among enterprises to explore distributed architectures and micro-models. This progression is balancing factors such as performance, real-time intelligence, and data sovereignty.

Today's AI workloads require infrastructure that delivers three critical capabilities simultaneously:

Enhanced privacy from isolated AI models and localized data processing
Low latency for real-time inference
Flexibility to scale across multiple geographies and regulatory environments

The shift toward distributed private AI is largely being driven by converging forces. Data sovereignty regulations across Europe, Asia-Pacific, and other regions mandate that private and sensitive data remain within specific geographic boundaries. Simultaneously, real-time AI applications demand response times measured in milliseconds, not seconds.

These forces are ultimately changing infrastructure design—moving AI compute closer to data sources and end users while maintaining private, secure environments.

Additionally, there’s an increasing transition from large-language AI model training to smaller (and eventually micro) models for inference workloads. Ingesting and fine-tuning LLMs in private infrastructure empowers enterprises to leverage their own data for highly accurate, real-time decision-making. This is the primary value driver for distributed private AI.

This transition amplifies the need for distributed architecture that can deliver intelligent responses at the edge while orchestrating complex workflows across hybrid environments.

Rethinking the architectural foundations

Unlike traditional cloud-based AI that centralizes compute resources, distributed private AI disperses workflows. These workflows are distributed across a wider, often global network of interconnections. This brings AI capabilities to the point of data creation and consumption.

A shift in architecture like this gives organizations more control over their data and models. It also achieves the critical performance characteristic required for real-time business impact.

The three architectural pillars

The foundation of distributed private AI rests on three architectural pillars:

Geographically distributed compute resources: Enabling organizations to deploy AI workloads in proximity to data sources. Reducing latency and ensuring compliance with local data residency requirements.
Private network connectivity: Creating secure, high-bandwidth pathways between high-performance computing (HPC) infrastructure components. This eliminates the exposure risks associated with public internet transit.
Unified orchestration platforms: Providing seamless management across distributed resources, enabling organizations to deploy, monitor, and optimize AI workloads regardless of physical location.

This approach transforms how we think about an AI deployment. Organizations can avoid building large, centralized GPU clusters that serve many use cases. Instead, they can strategically place AI compute resources where they deliver maximum business value. This supports the ongoing evolution in multi-model AI strategies, where the distinction between training and inference is further refined by the roles of service providers and enterprises.

As many enterprises adopt AI cloud platforms, demands for low-latency inference, data sovereignty, and compliance are leading to hybrid approaches. Where organizations are combining public AI platforms with private, distributed infrastructure. This supports federated learning, maintains data control, and efficiently manages inference workloads.

This balance enables enterprises to maximize multi-model AI potential while meeting regulatory and performance needs from distributed, private infrastructure.

Infrastructure requirements driving the future of enterprise AI

At Digital Realty, we recognize that deploying AI at scale demands infrastructure exceeding those of many traditional data centers. To support the unique requirements of modern AI workloads, our approach centers on several core imperatives:

High-density power delivery

Average power densities have more than doubled in the last three years. By 2030, rack power densities are expected to reach 30-50kW. AI-specific racks may reach 100kW to 150kW. This represents a massive increase from 2024, where average rack densities were frequently below 10kW.

Digital Realty’s High-Density Colocation solution works to solve HPC ecosystem and power density challenges with peak efficiency. It ensures uninterrupted performance for even the most demanding AI environments.

Advanced cooling for AI & HPC

Traditional workload densities can largely be air cooled. However, broadly speaking, most AI and HPC workflows require specialized cooling such as; direct liquid cooling (DLC), air-assistant liquid cooling (AALC), or a rear-door heat exchanger.

Robust connectivity ecosystems

Distributed AI workloads generate massive data flows between training systems, inference engines, data lakes, and application layers.

Digital Realty’s ServiceFabric® supports low-latency, high-throughput connections. Access direct, private on-ramps to a broad ecosystem of cloud, AI, and network partners. IT leaders are able to architect the precise and performant connectivity ecosystem that the workloads demand.

Unified orchestration across hybrid environments

Effective distributed private AI requires unified data management that spans data center facilities, cloud environments, and edge locations.

Digital Realty’s global data center platform, PlatformDIGITAL®, offers globally standardized, HPC-ready infrastructure configurations to support high-density workloads. Securely deploy, connect, and control critical infrastructure at any capacity for any workload at any scale.

Digital Realty prioritizes power density, advanced cooling, robust connectivity, and unified orchestration. We deliver the infrastructure foundation enterprises need to support AI at scale.

Accelerating interconnected AI architecture with Digital Realty

Digital Realty’s Private AI Exchange (AIPx) is a purpose-built open ecosystem platform for interconnected AI architecture. AIPx enables enterprises to rapidly assemble, deploy, and orchestrate advanced AI solutions at scale.

Placing enterprise data at the core of AI workflows directly addresses Data Gravity challenges. Specifically, by integrating compute, storage, and service providers within a secure, high-density colocation environment on PlatformDIGITAL.

Through AIPx, IT leaders gain immediate access to a vast partner ecosystem via open APIs and a unified portal. Which enables dynamic, multi-directional orchestration of data and workloads across 700+ interconnected locations. Enterprises can seamlessly layer new technologies, interconnect with strategic partners, and deploy AI factories on demand. All while maintaining compliance and control.

As the foundation for next-generation, data-centric AI operations, AIPx is reshaping how customers build, connect, and scale intelligent solutions. Empowering enterprises to realize the full potential of distributed private AI on a global stage.

Securing your AI future

The transition to distributed private AI represents both a strategic opportunity and a technical challenge. Enterprises that move decisively to build AI-ready infrastructure will gain competitive advantage through:

Faster innovation cycles
Enhanced data privacy
Improved regulatory compliance
Superior customer experiences

However, achieving these benefits requires careful planning and partnerships to build a distributed ecosystem that balances privacy, performance, and scalability.

The future of enterprise AI is distributed, private, and intelligent

Digital Realty provides the infrastructure that makes this future achievable today. Our solutions architects and engineers work directly with your teams. They assess your infrastructure requirements, design distributed deployment workflows, and implement solutions that deliver measurable business value.

Ready to accelerate your AI journey? Contact our Expert Teams today and discover how distributed private AI can transform your organization.

Explore the Digital Realty Innovation Lab (DRIL) to test your AI workloads in production-grade environments before scaling globally.

Digital Realty Reports Third Quarter 2017 Results