Large language models (LLMs) are becoming increasingly powerful, but training them requires immense computational resources. What if you could train a massive, 7-billion parameter LLM with a million-token sequence length on just a handful of GPUs? New research shows it’s possible, opening exciting possibilities for smaller research teams and faster model development. Researchers faced a significant hurdle: GPU memory limitations. The longer the input sequence, the more memory the model needs to store intermediate calculations (activations) for backpropagation during training. Traditionally, techniques like recomputation (recalculating activations instead of storing them) and swapping (moving activations to CPU memory) have been used. However, these come with their own trade-offs—recomputation increases training time, while swapping can be slow due to data transfer bottlenecks. The researchers developed an innovative training framework called "Memo" to solve this memory crunch. Memo employs a clever two-pronged approach. First, it uses a fine-grained activation management system that selectively swaps activations between GPU and CPU memory *during* computation, maximizing overlap and minimizing downtime. It also incorporates token-wise recomputation, reducing the computational overhead compared to full recomputation. Second, Memo tackles the problem of memory fragmentation. Memory fragmentation occurs when available memory gets divided into small, unusable chunks. Memo uses a Mixed Integer Programming (MIP) approach to plan memory allocation ahead of time, ensuring the memory space is used efficiently and avoiding costly reorganization during training. The results? Memo drastically reduces the resources needed to train large LLMs, achieving a Model FLOPS Utilization (MFU) of over 50%—much higher than other state-of-the-art frameworks like Megatron-LM and DeepSpeed. This efficiency gain allows Memo to train massive models on just eight A800 GPUs, a setup significantly smaller than what was previously thought necessary. This breakthrough has exciting implications for the future of LLM research. Smaller teams with more modest hardware setups will be able to train large models, accelerating the pace of innovation. Moreover, Memo's focus on memory management could inspire new training techniques for even larger and more complex AI models. While this research specifically focused on a particular model architecture and hardware, it showcases how innovative memory management strategies can dramatically improve LLM training efficiency, setting the stage for broader access to powerful AI.