<ul><li>Quantization-aware training (QAT) is a method that reduces model precision while maintaining performance of large language models (LLMs), addressing computational and memory challenges.</li><li>A unified scaling law for QAT was proposed in a recent paper, considering factors like model size, training data volume, and quantization group size.</li><li>Through 268 QAT experiments, it was shown that quantization error decreases with larger model sizes, but increases with more training tokens and coarser quantization granularity.</li><li>The primary bottleneck in 4-bit precision QAT was identified in the FC2 layer due to activation quantization errors caused by outliers, suggesting the importance of addressing these errors for improvement.</li></ul>

Scaling Law for Quantization-Aware Training

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