The evolution of data center hardware is marked by the emergence of AI, the need for more computing power per rack, and regulatory and social pressure towards energy sustainability. In this context, hardware for data centers becomes a strategic factor to guarantee availability, efficiency, resilience and growth capacity in the medium term.
The importance of data center hardware
The importance of data center hardware is essential. The equipment that makes up data centers is the basis on which cloud services, AI platforms, edge computing and, in general, the entire digital infrastructure of companies are built. Without proper server, networking, storage, power and cooling architecture, no digital business model can scale securely and sustainably.
In addition, data center hardware impacts total cost of ownership (TCO), carbon footprint, and the ability to meet service level agreements (SLA). The densification of workloads, especially those associated with AI and advanced analytics, forces us to review traditional designs of generalist CPUs, low-density racks and exclusively air cooling. Therefore, data center hardware purchasing decisions have ceased to be purely technical and have become critical business decisions.
Availability and resilience also depend on data center hardware designed to fail in a controlled manner, with electrical redundancies, generator sets, advanced UPS systems, and fine-grained integration between IT and facilities. In this sense, coordination between generating sets, batteries and other energy sources is essential to guarantee continuity of service in large facilities. In parallel, the rise of the edge and distributed data centers requires homogeneous hardware standards for data centers in different locations, from large hyperscale campuses to micro data centers at the edge.
Top trends in data center hardware
Trends in data center hardware revolve around five major axes: AI accelerators (GPU and DPU), new server architectures, advanced cooling, energy and sustainability, and intelligent infrastructure automation/management. Added to this are architectural models such as edge and high-density colocation, which require rethinking both the physical design and the way data center hardware operates.
1. AI Accelerators: GPU and DPU
Generative AI and HPC workloads are driving massive investment in data center-specific GPUs, capable of handling thousands of cores and per-chip consumption that can exceed a kilowatt. This transition from CPU to GPU as the main engine of intensive computing translates into very high-density racks, with more power per unit of space and unprecedented thermal demands on data center hardware.
In parallel, a third piece emerges: the DPUs (Data Processing Units), which take on network, security and data management tasks to offload CPUs and GPUs. This tri-processor approach improves overall performance, optimizes internal traffic, and increases the energy efficiency of data center hardware by removing general-purpose processors from infrastructure functions. Manufacturers such as NVIDIA are integrating CPUs, GPUs and DPUs into specific platforms for “AI factories”, which requires purpose-designed electrical and cooling infrastructures.
2. New server architectures (Arm and modular systems)
Another clear trend is the adoption of servers based on Arm architecture, which offer a better performance/watt ratio compared to traditional x86, especially in cloud environments and scalable workloads. This change has a direct impact on data center hardware, which must adapt to heterogeneous platforms where Arm CPUs, high-performance GPUs, and specialized accelerators for specific tasks coexist.
At the same time, modular designs and prefabricated data centers are becoming widespread, allowing computing capacity to be deployed quickly and in a scalable manner. These modules integrate servers, storage, networking, power and cooling as standard, reducing deployment time and facilitating hardware standardization for multi-site data centers. For colocation providers and operators, this modularity is key to accompany the growth of the multi-cloud model and the needs for proximity to the end user.
3. Liquid cooling and advanced thermal management
Increasing superchip thermal power and rack densities are leading to accelerated adoption of liquid cooling technologies, both direct-to-chip and immersion. These solutions allow much more heat to be extracted per unit area than traditional air cooling, making the operation of new AI-oriented data center hardware viable without penalizing energy efficiency.
Infrastructure providers are developing joint reference architectures with GPU manufacturers to ensure power and cooling systems are capable of supporting the next generation of accelerators. Sector reports highlight that innovation in power and cooling will be one of the fundamental pillars of the CPD of 2025, especially in very high-density AI racks. In fact, several analyzes suggest that the deployment of liquid cooling will go from being a niche option to a standard requirement in any hardware project for next-generation data centers.
4. Energy, resilience and sustainability
The other major trend affects the energy-sustainability binomial, where hardware for data centers plays an essential role in reducing consumption and carbon footprint. Solutions such as high-efficiency UPS, optimized generator sets, integration of renewables and intelligent energy management systems are becoming differential elements in the CPD architecture.
Trends in data center hardware revolve around AI accelerators, new server architectures, advanced cooling, energy and sustainability, and intelligent infrastructure automation/management
In addition, regulatory pressure and decarbonization goals are pushing operators to improve indicators such as PUE and CUE through more efficient hardware, better thermal management practices and the adoption of technologies such as AI to optimize consumption. In contexts of accelerated growth in electricity demand, such as that expected for 2024-2025, these improvements are no longer optional and become a condition of economic viability of data center hardware.
5. Automation, AI and edge in CPD operation
Finally, the trend towards automation and the intensive use of AI to operate data center hardware more efficiently and safely is consolidated. Advanced DCIM platforms, combined with analysis algorithms and digital twins, allow you to simulate scenarios, anticipate failures and dynamically adjust parameters such as load distribution, cooling or energy use.
AI is already used to monitor server performance in real time, predict hardware failures, and automate the allocation of workloads between central hubs and edge nodes. This operational intelligence is key in a context of proliferation of distributed data centers, where edge computing requires compact, robust data center hardware capable of operating in less controlled environments than large traditional CPDs. In this way, data centers evolve towards more autonomous infrastructures, where the combination of automation, AI and efficient hardware design for data centers becomes a true engine of technological innovation.
