November 17, 2024: Quantization Limitations Challenge AI Efficiency - Quantization, a key technique for enhancing AI model efficiency by lowering bit precision, has its drawbacks, especially for models trained extensively on large datasets. Research involving top institutions highlights potential performance issues, suggesting smaller models might be more viable. Despite industrys large-scale approach, diminishing returns and degradation of small models are evident. Promising solutions include training in low precision and improving data curation. However, pursuing extreme low precision can impair model quality. The evolving nature of AI efficiency demands that shortcuts like quantization be carefully evaluated.
