In the rapidly advancing field of artificial intelligence (AI), NVIDIA’s graphics processing units (GPUs) have become integral to scaling complex AI workloads, from training large-scale models to performing intricate inferences. However, the dominance of GPUs raises essential questions regarding their performance, environmental impact, and cost compared to traditional central processing units (CPUs). This paper presents a unique case study that examines the efficacy of GPUs versus CPUs in the context of graph analytics. We evaluate performance metrics, energy consumption, and cost implications of GPU and CPU deployments, using data from a real-world application. Our findings reveal that while GPUs provide substantial computational power, their high energy consumption and cost can outweigh the benefits in certain scenarios. Notably, modern CPUs, when optimized for parallel processing, can offer very competitive, superior per se, alternative. Through detailed analysis, we demonstrate that CPUs can achieve significant cost savings—ranging from 35 to 70%—and reduced energy consumption—ranging from 50 to 75%—while achieving exponential (10×+) performance advantages. The results suggest a reconsideration of the prevalent GPU-centric approach in favor of more sustainable and cost-effective CPU solutions. This study aims to shift the narrative toward a more balanced and efficient utilization of computational resources, advocating for the inclusion of CPUs in AI infrastructure to enhance performance, greenness, and cost-efficiency.