December 23, 2024:
Quantization Struggles with AI Model Efficiency Limits - Quantization, a method to improve AI efficiency by cutting computational needs, has notable drawbacks. Research from leading universities indicates that heavily trained quantized AI models underperform, leading to suggestions to focus on high-quality data in smaller models. Despite AI companies using massive datasets, evidence shows diminishing returns and quality loss with very low precision.
Extreme low precision results in degradation of model quality, suggesting new architectures and careful data curation are required. This approach could stabilize low precision training and keep inference costs manageable. The findings challenge the trend of scaling AI models with massive datasets, pointing to the necessity for precise data handling.
