Rethinking AI-Ready Infrastructure: The Strategic Role of Accelerated CPUs

Guest IDC Blogger: Heather West, PhD

Rethinking AI Infrastructure: The Strategic Role of Accelerated CPUs

The rapid rise of generative AI (GenAI) triggered a surge in infrastructure investments and a wave of misconceptions. In the rush to seize GenAI’s potential, many enterprises defaulted to GPU-heavy architectures, assuming all AI workloads demand the same level of compute intensity. This has fueled the costly belief that high-end GPUs are the default—or only—path to AI success.  GenAI is just one segment in a broader spectrum of AI use cases that include, which also include traditional machine learning (ML), statistical analysis, and rule-based systems. These workloads vary significantly in complexity, performance requirements, and infrastructure needs. Using a one-size-fits-all approach often results in underutilized GPUs, higher energy and cooling costs, and fewer resources for other essential workloads. GPU availability remains constrained, and the high-power density of GPU-dense systems can challenge existing data center operations. IT leaders are increasingly seeking alternatives that balance AI performance with operational simplicity, cost control, and infrastructure consistency. In response, a more pragmatic, workload-aware strategy is taking hold—one that matches computing resources to actual workload needs. As part of this shift, accelerated CPUs are emerging as a smart option for enterprises building scalable and efficient AI infrastructure.

Accelerated CPUs: Enabling Scalable and Efficient AI Workloads

Accelerated CPUs are enhanced general-purpose processors designed with integrated AI acceleration capabilities. These include instruction sets optimized for matrix operations, expanded memory bandwidth, and larger caches—all of which improve performance for tasks like:

• ML model training and inference
• Feature extraction and data preprocessing
• Batch inference and latency-tolerant AI services

In addition, accelerated CPUs build on familiar x86-based architectures, making them easier to integrate into existing IT environments enabling IT teams to use the same operating systems, toolchains, and security models they already support—reducing the learning curve and accelerating deployment. For enterprises, this can translate into lower implementation risk, faster time to value, and reduced total cost of ownership. Since accelerated CPUs often fit within existing server configurations, they eliminate the need for costly redesigns or specialized cooling and power infrastructure associated with GPUs.

Optimizing AI Life-Cycle Performance with the Right Compute Resources

For most businesses, having the right AI infrastructure for the right task at the right time is crucial. From early experiments to full deployment, the performance, cost, and integration needs of AI workloads can vary greatly. Differences in model size, model type, data volume, expected accuracy, required training time, and required inferencing latency may necessitate a combination of both GPUs and accelerated CPUs to meet specific performance and efficiency goals.  To address this concern, forward-looking enterprises are embracing flexible, hybrid AI infrastructure strategies that align compute resources with workload demands throughout the AI life cycle. According to a recent IDC study sponsored by Intel, more than 40% of organizations are taking this approach. This model optimizes performance and efficiency, enabling faster decision-making, better budget management, and more agile scaling across different business units.

How to Align GPU and Accelerated CPU Infrastructure with AI Workload Demands?

Effective AI infrastructure planning begins with understanding how computing requirements evolve across the AI pipeline, enabling enterprises to match resources to workload characteristics at each stage:

• Development phase: optimize for flexibility and cost efficiency
  Enterprises should use accelerated CPUs when training small models, working with limited data sets, or experimenting with moderate accuracy requirements. In early prototyping or environments where training is outsourced, CPU-based systems offer sufficient performance at significantly lower costs (See the results below from IDC's Intel-Broadcom Accelerated CPUs Survey, September 2024, n = 509).

• Deployment phase: streamline inference at scale
  Accelerated CPUs excel at running batch-oriented, cost-sensitive, or latency-tolerant inference workloads. They’re ideal for deploying AI into existing enterprise systems, such as internal analytics tools, edge environments, or customer service automation, without requiring major infrastructure changes.
• GPU utilization: focus on high-demand use cases
  Enterprises should use GPUs selectively for training large-scale or highly complex models or running high-concurrency, real-time applications that demand strict latency thresholds.

Conclusion: Building a Balanced, Future-Ready AI Infrastructure

Accelerated CPUs are not just a tactical alternative—they represent a strategic pillar for modern AI infrastructure. When integrated into a hybrid, workload-optimized strategy, accelerated CPUs offer a blend of scalability, operational simplicity, and cost-effectiveness, making them a smart, sustainable foundation for enterprise AI success. Enterprises interested in integrating accelerated CPUs into their AI infrastructure should consider the following steps:

• Audit existing AI workloads to identify where accelerated CPUs can replace or complement GPUs without compromising performance.
• Adopt a hybrid infrastructure model that aligns compute resources with actual workload demands across the AI life cycle.
• Engage with infrastructure partners to explore deployment-ready solutions that integrate accelerated CPUs into their existing environment.

By adopting a workload-aware approach, enterprises can effectively unlock AI's full potential in a cost-efficient and scalable manner.

For more information on Intel’s accelerated processors, visit www.intel.com

For Efficient, Sustainable AI at Scale, Consider a Processor Spectrum with Various Performance Characteristics - IDC Research