Computing power is like the power supply for AI. Without it, even the most advanced models can't take shape. In recent years, as AI parameter scales have skyrocketed, traditional centralized computing architectures have become increasingly difficult—costs are prohibitively high, scaling is lagging, and idle machine resources are often wasted.

One approach worth paying attention to is: aggregating globally idle GPUs into a network and automatically allocating tasks through algorithms. Compared to the old centralized solutions, this distributed computing pool has clear advantages—training and inference costs can be significantly reduced, and computing power supply becomes more flexible and capable of handling sudden demand spikes. Tasks like film rendering, 3D modeling, or high-frequency needs such as AI model training and real-time inference can all be strongly supported by this network.

A practical case worth noting is the AI rendering acceleration solution developed through collaboration between Ruiyun and Huawei Cloud. By combining distributed computing with AI optimization, they increased rendering efficiency by over 40%. This approach is increasingly being adopted in decentralized computing ecosystems.

Looking ahead, the AI industry is expected to reach a scale of $860 billion, and the gap in computing power demand will only grow larger. The distributed model, through decentralization and integration, not only addresses the persistent mismatch between supply and demand but also turns computing power into a truly tradable and configurable production factor. As a result, both large enterprises and small teams of developers will have the opportunity to access the necessary computing resources at lower costs.