𝓤𝓷𝓲𝓽𝓮𝓭 𝓝𝓮𝔀𝓼

Delta Electronics is a Business Reporter client

The explosive growth of AI is one of the primary drivers of data centre expansion in the EMEA region. In Europe alone, data centre capacity is projected to grow at a compound rate of 25 per cent through 2030, outpacing the impact of the move to public cloud infrastructure over the past decade.

And the challenge is not just scale: AI processing brings its own demands, requiring a substantial increase in rack power, cooling and supporting infrastructure. Unlike traditional enterprise workloads, AI applications rely on GPU-based architectures that dramatically increase power density and thermal output at rack level.

For data centre operators, colocation providers and cloud companies, this means deploying AI-ready capacity faster – within constrained electricity grids, regulatory limits and tightening sustainability requirements – without sacrificing uptime or economics.

Approaches such as Delta’s Grid-to-Chip model are designed to address these challenges within an integrated, modular data centre strategy that optimises every step of the power conversion process.

Customer challenges

In the face of AI’s rapid expansion, traditional methods of planning, powering and constructing data centres can no longer keep pace. The challenges begin before construction even starts. In many regions, the supporting infrastructure – including grid connections and fibre networks – must first be upgraded to enable connectivity.

At the same time, grid and fibre expansion are often slowed by lengthy permitting processes, while regulatory requirements can restrict how and where facilities are built.

Beyond these external constraints, AI workloads are fundamentally reshaping infrastructure requirements. High-density compute clusters driven by graphics processing units (GPUs), which power large language models (LLMs), are transforming the size, design and sustainability profile of modern data centres.

With AI workloads already pushing rack densities beyond 100kW – and projections reaching up to 1.2 MW by 2028 – data centre design is crossing a critical threshold. This represents an order-of-magnitude increase compared to traditional rack densities of 5 to 10kW, fundamentally changing how facilities must be engineered.

At these levels, incremental upgrades to legacy power and cooling architectures are no longer sufficient. Instead, operators must rethink power distribution, thermal management and energy efficiency as a unified, system-level challenge.

Grid-to-chip integration

This shift is driving a move away from siloed optimisation – where power, cooling and IT systems are designed independently – towards a more integrated approach that treats energy as a continuous value chain, from grid connection to silicon.

In high-density AI environments, even small inefficiencies in power conversion can result in significant energy losses and additional heat generation. As power levels increase, every conversion step becomes critical.

Delta’s grid-to-chip strategy focuses on integrating the entire power pathway from the electrical grid to the processors with minimal energy loss. By combining high-voltage DC power (HVDC), advanced power conversion and efficient cooling technologies, the approach aims to improve both component-level and system-level performance.

Higher-voltage distribution reduces current and helps minimise resistive losses, while fewer conversion steps improve overall efficiency. By optimising each stage of the energy pathway, operators can reduce energy consumption, lower heat generation and decrease cooling demand.

In parallel, embedded AI and machine learning technologies are increasingly being used to optimise operations across the infrastructure stack. These include cooling optimisation, predictive maintenance for uninterruptible power supplies (UPS) and batteries, and intelligent energy orchestration.

For operators, this translates into lower energy consumption, extended equipment lifetime and improved uptime. At scale, even small efficiency gains can result in meaningful cost savings and reduced environmental impact. Delta estimates that, in large-scale deployments, these efficiencies could translate into annual electricity savings of several million dollars.

Grid-to-chip architecture in practice

A grid-to-chip architecture focuses on optimising each stage of the infrastructure stack to support high-density AI workloads:

Together, these elements enable data centres to operate more efficiently while meeting the extreme power and thermal requirements of AI infrastructure.

The benefits of modularity

Alongside integrated power management, modular data centres (MDCs) are becoming an increasingly important part of AI infrastructure strategies.

MDCs can range from micro or single-rack deployments to larger containerised units. Their modular design allows operators to scale capacity incrementally as demand grows, reducing the risk of overprovisioning and stranded assets.

Each module is typically prefabricated and pre-tested using standardised components, which significantly reduces on-site construction time and accelerates deployment. This enables faster time-to-market for new services, an important advantage in the rapidly evolving AI landscape.

This approach is particularly relevant for AI deployments, where demand can scale quickly and infrastructure must be deployed in phases rather than through large, upfront builds.

In addition, integrated power and cooling systems within modular designs help improve overall energy efficiency and support compliance with increasingly stringent sustainability targets.

Where regulatory or aesthetic constraints limit the use of containerised solutions, modular systems can be adapted with external cladding to meet local architectural requirements.

Real-world impact

Delta EMEA has supported a range of data centre projects delivering double-digit improvements in energy efficiency and power usage effectiveness (PUE) compared with conventional designs.

In one example, a European telecom operator expanded its 5G edge network using prefabricated modular data centres. While a traditional facility would have taken approximately 2.5 years to build, the modular deployment was operational within 16 months.

This approach not only accelerated deployment, but also reduced operating expenses through improved energy efficiency, while enhancing uptime, operational resilience and regulatory readiness, including ESG requirements.

Looking ahead

AI data processing and training requirements are expected to expand significantly over the coming years, placing increasing demands on data centre infrastructure.

As GPU architectures continue to evolve, driving higher power density and thermal output, data centres will need to adopt more integrated, system-level designs that combine power, cooling and compute into a unified architecture.

At the same time, constraints on grid availability, sustainability targets and deployment timelines will continue to shape how infrastructure is built and operated.

The organisations that succeed in this environment will be those that can align speed, efficiency and sustainability as a single design objective – and treat energy not as a supporting utility, but as a core element of compute architecture.

To find out more about how Delta can support next-generation AI data centre infrastructure, click here