Scaling Law for Quantization-Aware Training (arxiv.org)

A recent study has introduced a unified scaling law for quantization-aware training (QAT) of large language models (LLMs), addressing significant challenges associated with deployment due to high computational and memory demands. The research highlights that most existing scaling laws overlook critical elements like the number of training tokens and the granularity of quantization, which can affect performance. Through an extensive set of 268 experiments, the paper demonstrates that while quantization error diminishes with increasing model size, it escalates with a larger training dataset and coarser quantization, particularly at 4-bit precision (W4A4). The findings reveal that quantization errors can be disaggregated into weight and activation components, both of which exhibit varying sensitivities to training data. Notably, weight quantization errors rise more sharply with additional training tokens, whereas activation errors, primarily influenced by outliers in the FC2 layer, emerge as a significant bottleneck. The researchers suggest that employing mixed-precision quantization can help mitigate these errors, ensuring both weight and activation errors converge to similar levels. This work not only enhances understanding of QAT dynamics but also provides actionable insights that could significantly improve the efficiency and performance of LLMs in real-world applications.