The decentralized AI computing power market, like Render Network, faces significant supply-demand matching challenges. On the supply side, idle GPUs from data centers and crypto mining are abundant but often underutilized due to verification complexities and hardware heterogeneity. Demand for AI workloads, especially training and inference, is surging, yet small businesses struggle with high costs and limited access to scalable resources. Coordinating decentralized nodes to meet dynamic computational needs requires robust pricing, discovery mechanisms, and trustless verification, which are hindered by technical bottlenecks like state-dependent deep learning models. Additionally, economic incentives must align to sustain participation, but current tokenomics often fail to reflect true cost advantages over centralized cloud providers.