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The Holy Grail of Crypto AI: Cutting-edge Exploration of Decentralized Training In the AI full value chain, model training is the most resource-intensive and technically challenging link, directly determining the model's capability ceiling and practical application effects. Compared to the lightweight calls in the inference stage, the training process requires continuous large-scale computing power investment, complex data processing workflows, and high-intensity optimization algorithm support, making it the true "heavy industry" of AI system construction. From an architectural paradigm perspective, training methods can be divided into four categories: centralized training, distributed training, federated learning, and the focus of this article, Decentralization training. Centralized training is the most common traditional method, completed by a single organization within a local high-performance cluster, where all training processes, from hardware, underlying software, cluster scheduling systems, to all components of the training framework, are coordinated by a unified control system. This deeply collaborative architecture enables memory sharing, gradient synchronization, and fault tolerance.

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Vitalik discusses the "Year Wheel Model" of culture and politics. Vitalik recently published an article proposing the concept of "the cultural and political annular model" and used it to analyze the changes in the current global landscape of artificial intelligence and technology regulation. The article points out that people generally believe that American technology is more inclined towards open source, while China places greater emphasis on closure and control. However, the reality is quite different from this perception. Vitalik believes that this discrepancy can be explained through the "annual ring model": the attitude towards new things is shaped by the social atmosphere at the time of formation, while the attitude towards old things is determined by inherent inertia. Once these "cultural annual rings" are formed, they are difficult to change. When analyzing American society, Vitalik pointed out an interesting phenomenon: although people often say they live in a "deeply neoliberal society", the government's regulatory policies actually diverge from this idea.