[xAI Case Highlights GPU Large-Scale Parallel Usage Challenges: AI Computing Power 'Available ≠ Efficient'] Odaily Planet Daily reports that xAI's latest practices reveal that even with the successful acquisition of a large number of Nvidia server-grade GPUs, efficiently utilizing them remains one of the core bottlenecks in AI training. As AI developers continue to compete for Nvidia computing resources, the issue of GPU supply shortages has garnered widespread attention. However, the industry's new challenge lies in 'usage efficiency' itself. AI model training typically exhibits a distinct 'bursty' characteristic: GPUs operate at high intensity for short periods, followed by idle phases for result analysis and strategy adjustments. This uneven computing power usage pattern makes it difficult for large-scale GPU clusters to maintain consistently high utilization rates, leading to significant wastage of computing power even when hardware is sufficient. Industry experts point out that this issue is forcing AI companies to redesign training architectures and scheduling systems to improve the overall utilization efficiency of GPU clusters, rather than simply expanding computing power capacity. (The Information)