Training massive AI models like those powering ChatGPT is a computationally expensive undertaking, often requiring vast amounts of data and specialized hardware. But what if there was a more efficient way to scale these models, making them faster to train and less resource-intensive? Researchers have introduced a novel training methodology called EfficientScale, offering a potential solution to this challenge. The approach focuses on transferring knowledge from smaller, pre-trained models to larger ones, significantly reducing the data and computational resources needed. Imagine building with LEGOs – instead of starting with individual bricks each time, you could use pre-assembled sections to quickly construct larger, more complex structures. EfficientScale uses a similar strategy, initializing larger models with the learned 'knowledge' from smaller models. This process happens in two stages. First, a "Scale-Up" phase expands a smaller model, inheriting its pre-existing knowledge. Then, a "Scale-Out" phase converts this dense model into a Mixture of Experts (MoE) model, distributing the workload across multiple specialized 'experts' to further enhance efficiency and performance. This approach was tested by creating AquilaMoE, a large bilingual language model. The results? Significant improvements in training efficiency and model performance. Using EfficientScale, the team saw substantial time and computational savings compared to training from scratch, demonstrating a promising new path towards efficiently scaling even larger language models. This breakthrough could have a profound impact on AI development, enabling researchers to build more complex and capable language models while lowering the barrier to entry for those with limited resources. While challenges remain, EfficientScale marks a significant step towards democratizing AI research and development.