# FlashNorm: Fast Normalization for Transformers

arXiv: https://arxiv.org/abs/2407.09577 · HTML: https://mlsanity.com/paper/2407.09577/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [Query-Key Normalization for Transformers](https://mlsanity.com/paper/2010.04245/) (2020)
- [Transformer Normalisation Layers and the Independence of Semantic Subspaces](https://mlsanity.com/paper/2406.17837/) (2024)
- [Efficient ConvBN Blocks for Transfer Learning and Beyond](https://mlsanity.com/paper/2305.11624/) (2023)

## Abstract (arXiv)

Normalization layers are ubiquitous in large language models (LLMs) yet represent a compute bottleneck: on hardware with distinct vector and matrix execution units, the RMS calculation blocks the subsequent matrix multiplication, preventing parallel execution. We present FlashNorm, an exact reformulation of RMSNorm followed by a linear layer that (i) eliminates the normalization weights by folding them into the subsequent linear layer, and (ii) defers the scalar RMS normalization to the output of the matrix multiplication, enabling the two operations to execute in parallel. Additionally, by the scale invariance of RMS, an RMSNorm followed by a linear layer followed by another RMSNorm allows the first RMSNorm to be eliminated entirely -- a mathematically identical simplification that removes the pre-attention RMSNorm in models using QKV-normalization (e.g., Gemma~4) and in MLA-models with latent normalization (e.g., DeepSeek-V2, Mistral Small 4, and OpenMythos). The same techniques extend to LayerNorm, Dynamic Tanh (DyT), feed-forward networks with GLU variants, and RoPE-based attention. On an NVIDIA T4 GPU, FlashNorm achieves 33 - 35% lower latency on the norm-then-project operation in the compute-bound (prefill) regime at SmolLM2-135M scale, and 12 - 14% at Llama-7B scale. We verify zero-loss weight folding on three models. Beyond inference speed, FlashNorm simplifies model implementations by reducing parameter tensor count. Watch our explainer video https://youtu.be/GEuJv34_XgU?si and see https://github.com/OpenMachine-ai/transformer-tricks for code.