0G Labs has just accomplished something that has shocked the AI community — under a "normal network environment with only 1Gbps speed," yes, even at your office WiFi speed, they successfully trained the world's first distributed LLM model with 100 billion parameters. This could very well disrupt the AI industry landscape: AI training is no longer exclusive to the "supercomputing club." Now, small and medium enterprises, startup teams, and even university labs can participate in training with their own equipment. The key technology behind this is called DiLoCoX (Low Communication Training Framework), with the core idea: through "communication delay tolerance," "adaptive gradient compression," and "pipeline parallelism" it transforms the training of large models, which originally relied heavily on bandwidth and hardware, into a distributed task that can be handled even on "ordinary networks." The training speed has improved by 300 times compared to before, and the infrastructure cost has been cut by 95%. Most importantly, what 0G has created is completely decentralized. It does not rely on any single giant, nor does it require permission from cloud vendors, anyone can become an AI training node as long as they are connected to the network. Data is always stored locally, and model synchronization is fully transparent across the entire chain. To achieve this transformation, 0G Labs is collaborating with companies, operators, and developers worldwide to build a "decentralized AI training network." The game rules of AI computing power depend on centralized giants, and @0G_labs is rewriting them. @0g_CN @Galxe #Starboard #0G #Yaps #KaitoAI @KaitoAI