Training massive language models like GPT-3 is incredibly resource-intensive, often hitting a "memory wall" where even the combined memory of multiple GPUs isn't enough. This bottleneck slows down training and limits the size of models we can create. Existing methods try to offload data to the host CPU's memory, but this often leads to suboptimal performance due to the slower speed of CPUs and the limited bandwidth of the connection between the CPU and GPU. Researchers have developed a new technique called "Deep Optimizer States" that cleverly manages this offloading process. The key insight is that GPU memory usage fluctuates during training. Deep Optimizer States takes advantage of these fluctuations to dynamically shift parts of the optimizer state between the CPU and GPU, maximizing the use of both. It’s like a sophisticated juggling act, ensuring that the right data is in the right place at the right time. This new approach has shown impressive results, speeding up training iterations by up to 2.5 times compared to existing methods. This breakthrough has the potential to democratize access to large language model training, enabling researchers and developers with limited resources to train larger, more powerful models. While Deep Optimizer States significantly improves performance, the inherent limitations of CPU speeds and data transfer rates still pose challenges. However, with the advent of next-generation systems boasting even faster interconnects between CPUs and GPUs, techniques like Deep Optimizer States are crucial for unlocking the full potential of future AI models.