Abstract. Introduces RMSNorm, a simplified variant of LayerNorm that drops the mean-centring and the bias parameter. Rescales activations by their root-mean-square magnitude, applies a learned per-feature scale, and stops there. The mathematical motivation is that the mean-centring in LayerNorm is empirically less important than the rescaling, and removing it saves a reduction operation per layer. RMSNorm matches LayerNorm in quality while being slightly faster and is used in nearly every frontier LLM, LLaMA, PaLM, Qwen, Claude, Gemini.