The Rise and Development of AI Computing Power Sharing Platform

Recently, two AI Computing Power sharing platforms based on blockchain technology have attracted widespread attention in the market. These platforms aim to address an increasingly prominent issue: how to effectively utilize idle high-performance graphics card resources to provide more cost-effective Computing Power support for AI startups and game rendering companies.

The core of this business model lies in connecting two key groups: on one hand, AI startups that require a large amount of Computing Power but lack the funds to purchase expensive equipment, and on the other hand, individuals or institutions that own idle high-end graphics cards (such as 4090, 3090, A100, H100, etc.). By building an intermediary platform, these projects aim to create a win-win situation: AI companies can obtain the required Computing Power at a lower cost, while graphics card owners can earn income from their idle resources.

The emergence of this model is inevitable. First of all, for many AI startups, purchasing a large number of graphics cards is not only costly but also lacks flexibility. Secondly, the current global graphics card market is under tight supply, and there are even some trade restrictions, forcing many companies to obtain resources through gray channels, which undoubtedly increases costs and risks. Therefore, a decentralized Computing Power sharing platform has emerged to address these pain points.

However, the biggest challenge faced by this platform is how to break the "which came first, the chicken or the egg" dilemma. The platform needs to attract enough graphics card resource providers and Computing Power demanders at the same time to form a virtuous cycle. To solve this problem, these platforms cleverly utilize the incentive mechanism of cryptocurrency.

One of the platforms chose to first gather "hardware resources" as a strategy. Through token incentives, they successfully attracted a large number of graphics card resources, reaching a scale of hundreds of thousands of graphics cards at one point. This approach effectively addressed the issue of insufficient resources during the initial launch phase of the platform.

The token models of these platforms are not only used for subsidies but also have practical application value. Although the platform supports payments in fiat currency or stablecoins, it also provides the option to pay with the platform's native token, offering a certain discount on transaction fees. This design neither forces users to use the platform token nor does it provide practical uses for the token, which is beneficial for the widespread distribution of the token and the maintenance of its long-term value.

In terms of ecological construction, different platforms have adopted different strategies. Some platforms cultivate user loyalty and sunk costs by selling virtual mining machines and physical mining machines. This practice effectively maintains the community's basic base, making it more difficult for users to leave the ecosystem.

Interestingly, although these platforms are in a competitive relationship to some extent, there is also cooperation between them. For example, they all collaborate with GPU Computing Power standardization companies, which reflects the importance of graphics cards as standardized products in the new era.

From the perspective of the technical ecosystem, these platforms have chosen different blockchain networks as their infrastructure. This differentiated layout avoids direct competition to some extent, while also providing choices for users of different blockchain ecosystems.

In general, these blockchain-based AI Computing Power sharing platforms are exploring a new business model aimed at addressing the Computing Power demand and resource allocation issues faced by the AI industry. Through innovative token economic models and flexible ecological construction strategies, these platforms are gradually breaking through the limitations of traditional centralized models, providing new possibilities for the development of the AI industry.