Training large language models (LLMs) is like building a massive skyscraper—it requires immense resources and careful coordination. As these models grow larger, the demand for computing power becomes a major bottleneck. Imagine trying to build that skyscraper on a small island – you quickly run out of space! Researchers are exploring ways to distribute the construction process across multiple islands, allowing for larger, more sophisticated models to be built. This research paper dives into one such method called "local SGD." Instead of constantly coordinating every brick laid across all islands, which requires lots of communication, local SGD lets each island work more independently. They only synchronize their blueprints occasionally. This reduces the communication overhead, which is crucial when bandwidth is limited. The researchers explored how well local SGD scales with increasing model size and data. They found that it can achieve comparable results to traditional methods, even when distributing the training across multiple clusters. They also discovered interesting tradeoffs. Smaller, more frequent synchronizations improve performance but increase communication. When bandwidth is low, like sending messages between distant islands, less frequent updates are more efficient. Think of it as shipping larger crates of materials less often. The research also sheds light on the possibilities and challenges of using "edge computing" for LLM training. Edge computing is like setting up smaller construction sites closer to the source of materials, which can reduce transportation costs. However, coordinating these smaller sites effectively presents its own set of challenges. This study reveals exciting possibilities for the future of LLM training. By cleverly distributing the workload, we can build larger and more powerful AI models, even with limited resources. This opens doors to new AI applications and makes the development of cutting-edge AI more accessible.