Revolutionary Memory Reduction for LLM Training: Run 70B Models on a Steam Deck!

This research introduces Spectral Compact Training (SCT), a groundbreaking method that dramatically reduces the memory footprint required for training Large Language Models (LLMs). The ability to train a 70B-parameter model on a device like a Steam Deck demonstrates the immense potential of SCT to democratize LLM development, making it more accessible to researchers and developers.