Over the past few years, discussions regarding enterprise artificial intelligence infrastructure have focused primarily on GPUs. The reason is clear: GPUs provide the massive amounts of parallelism required to develop and deploy large language models, as well as some of the most computationally intense AI workloads.

However, as organizations transition beyond the experimental phase of AI adoption and begin integrating AI throughout various business functions, the complexity of developing and managing AI infrastructure grows. Many organizations are now using pretrained models instead of developing them from the ground up, which shifts a larger portion of the AI lifecycle toward inference.

Additionally, agentic AI increases the demand to integrate AI models with applications, data and systems currently running on traditional, CPU-based infrastructure.

According to David Bartley, an Intel channel account manager, “CPUs have been running AI workloads for decades.” He added, “The narrative only shifted when generative AI showed up, and suddenly everything was seen through the eyes of a GPU.”

DISCOVER: Intel technologies can help support your AI investments.