SST vs. GaLore: The Battle for the Most Efficient AI Brain

This study examines how the SST (Singular Spectrum Transformation) model improves compression and efficiency in large language models compared to GaLore. Results show that SST retains lower perplexity across high pruning ratios and consistently outperforms GaLore in memory-efficient training experiments on datasets like IWSLT’14 and OpenWebText. By concentrating essential information into fewer singular values, SST enables lighter, faster, and more capable models—making it a leading approach for scalable, high-performance AI inference.