Consolidate to Accumulate: The Path to Accelerated AI Business Outcomes Starts with Server Consolidation

AI promises to transform enterprise operations and drive innovation. Yet the reality of implementing AI is rarely seamless. The challenges range from meeting new data center compute and energy demands, to aligning investments in resources with objectives around productivity gains or accelerated innovations.

Paired with the lack of institutional knowledge around AI infrastructure management and experience deploying AI, these challenges can lead to slowed adoption, costly miscues, and missed opportunities. To pre-empt these challenges and accelerate AI outcomes, enterprises need a data center modernization strategy that enables them to prepare for AI faster and then scale their success effectively and efficiently.

Prepare for AI faster through server consolidation

The single biggest step enterprises can take to prepare for AI faster is to consolidate servers using the latest CPUs. Today’s CPUs boast dozens — even hundreds — of cores on a single socket. This enables extensive parallelism for AI data preprocessing and smaller-scale inference tasks. It also means one modern server can potentially replace several older machines. This not only opens up valuable floor space that can be repurposed for racks focused on AI and their accompanying advanced cooling solutions. It enhances overall efficiency, too.

Additionally, the newest generation of CPU architectures deliver improved performance per watt compared to earlier models, helping to lower power usage and ongoing operational expenses for AI workloads.

Scale AI success with flexible infrastructure

A major consideration when scaling AI is that different AI workloads demand different compute capabilities. Where new infrastructure is required, sticking with x86 architectures rather than ARM-based options allows enterprises to retain existing x86 applications without extensive reprogramming. x86 architectures are also supported by a wealth of pre-optimized libraries and reference implementations that further cut down deployment timelines.

The compute profile of that infrastructure will then be defined by enterprise AI requirements. While CPUs are highly effective at managing AI data pre-processing and small- to medium-scale inference tasks, deep learning and other large-scale AI workloads often demand the parallel processing of GPUs. This makes flexibility in infrastructure design pivotal to long-term scalability. By adopting both CPU- and GPU-enabled architectures, enterprises can easily shift between different modes of AI compute.

Drive accelerated outcomes and with higher-performing systems

Faster and more efficient processing translates directly into quicker time-to-market on AI applications, and the ability to scale these applications more effectively and within a manageable operational budget. By starting with a robust server consolidation strategy and then building flexible, scalable infrastructure, organizations can accelerate near-term gains while staying flexible in the face of continuous AI innovations. This approach not only drives near- and long-term success but also positions enterprises to remain competitive as AI evolves.

For further insights on accelerating business outcomes with AI, tune in to Advancing AI 2025.