Imagine training massive AI models like those powering ChatGPT, but significantly faster and cheaper. That's the promise of low-rank training, a technique explored in recent research. Large Language Models (LLMs) are notorious for their computational hunger, often requiring immense resources and energy. This research dives into a clever method to streamline the training process by targeting the most computationally intensive parts of the model: the feedforward networks (FFNs). Instead of using traditional, densely connected layers, this study employs a "low-rank" approach. This means they represent the connections within the network using a smaller number of parameters, making the model leaner and faster to train. The results are striking. By applying this low-rank trick to the FFNs, researchers saw substantial speed improvements without a major dip in performance. In one case, the training speed increased by a whopping 2.6 times, while only using 32% of the original parameters. Even more exciting, these streamlined models seem to learn more efficiently than their bulkier counterparts, suggesting even greater potential for future advancements in AI. The researchers also explored the "scaling curves" of these models. These curves help us understand how performance changes as models get bigger. Interestingly, low-rank models showed steeper curves, meaning they improve more rapidly as they scale. The implications are huge. Low-rank training could unlock new possibilities for even larger and more powerful AI models, making them more accessible and less resource-intensive. This breakthrough could lead to more efficient chatbots, more accurate language translation, and a host of other AI-powered applications. This research not only provides a significant step forward for current AI technology but also opens up exciting new avenues for exploration in the world of low-rank training, paving the way for a faster, more sustainable future for AI development.