US 4-week Treasury auction results as of July 10 - Winning Intrerest Rate

The European Cup America's Cup is about to ignite the sports token market?

#AE# Brothers, WikiChain is much more powerful than Auntie

#WICC# why does a coin that is going to be delisted raise so much funds? Why does it attract so much capital if it barely has any movement?

GameFi is currently undergoing a development stage, currently in the 1.0 phase, focused on making money, and will transition to 2.0 and the Metaverse in the future. Investors need to calmly consider the long-term potential of projects, following mechanism design and market demand. Despite facing risks, excellent projects remain attractive, especially in a Bear Market where opportunities can still be found.

The evolution of AI training paradigms: from centralized control to a technological revolution of decentralized collaboration. In the full value chain of AI, model training is the most resource-consuming and has the highest technical threshold, directly determining the upper limit of the model's capabilities and its actual application effects. Compared to the lightweight calls in the inference stage, the training process requires sustained 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 decentralized training that this article focuses on. Centralized training is the most common traditional method, completed by a single organization within a local high-performance cluster, from hardware ( such as NVIDIA GPU ), underlying software ( CUDA, cuDNN ), cluster scheduling systems ( such as Kubernetes ), to training frameworks ( such as