The surge in demand for high-performance GPU computational resources, driven by AI large models, has resulted in a severe imbalance between supply and demand. This has further elevated the already high prices of GPU computational power, attributed to factors such as design and manufacturing costs and market monopolies.
GPU computational resources are primarily allocated to satisfy the demands of AI training scenarios, leading to a dispersed or fragmented nature of GPU computational resources for AI inference scenarios.