From a technical perspective, @0G_labs addresses the core conflict between scalability and decentralization. Looking back, all previous solutions have been compromises between these two—either fast but centralized, or decentralized but painfully slow. The breakthrough from 0G Labs lies in the DiLoCoX framework, which successfully trained an AI model with 10.7 billion parameters using just 1 Gbps bandwidth. This number might not seem impressive at first glance, but in reality, it’s 10 times larger than PrimeIntellect’s Intellect-1! And the bandwidth used is just the kind you’d find in a regular office. What’s even more critical is that this technological breakthrough bridges the final gap for DeAI. Previously, with LLM-based large models, the consensus was that decentralized AI is the future, but in practice, training LLMs still relied heavily on supercomputing centers. Now, distributed ordinary devices can handle this task—and do it efficiently. 0G Modular Architecture Breakdown: - 0G Storage: Decentralized storage - 0G DA: Data availability layer - 0G Serving: AI inference services - 0G Chain: High-performance consensus network This combination pushes the system’s throughput to 50 GB/s, which is 50,000 times faster than existing competitors, while cutting costs by 100 times. Forbes highlighted a key point in their report: this isn’t just a flashy tech demo—it’s about truly turning AI into a public good. When training large models no longer requires exorbitantly expensive infrastructure, and when ordinary developers can participate in building AI, the entire industry landscape will be reshaped. And @0G_labs is the one driving that change.