[2407.09577] FlashNorm: Fast Normalization for Transformers Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2407.09577 (cs) [Submitted on 12 Jul 2024 ( v1 ), last revised 24 Apr 2026 (this version, v5)] Title: FlashNorm: Fast Normalization for Transformers Authors: Nils Graef , Filip Makraduli , Andrew Wasielewski , Matthew Clapp View a PDF of the paper titled FlashNorm: Fast Normalization for Transformers, by Nils Graef and 3 other authors View PDF HTML (experimental) Abstract: 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 this https URL and see this https URL for code. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2407.09577 [cs.LG] (or arXiv:2407.09577v5 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2407.09577 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Nils Graef [ view email ] [v1] Fri, 12 Jul 2024 00:37:55 UTC (440 KB) [v2] Tue, 1 Apr 2025 23:19:22 UTC (449 KB) [v3] Sun, 1 Jun 2025 22:12:10 UTC (584 KB) [v4] Wed, 22 Apr 2026 03:03:18 UTC (597 KB) [v5] Fri, 24 Apr 2026 18:51:15 UTC (598 KB) Full-text links: Access Paper: View a PDF of the paper titled FlashNorm: Fast Normalization for Transformers, by Nils Graef and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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