Attention

QK-Norm

Normalizes queries and keys right before attention so their dot products can’t blow up.

First page of Query-Key Normalization for TransformersIntroduced inOct 2020Query-Key Normalization for TransformersHenry et al. · arXiv 2010.04245 ↗
QK-Norm: normalize before comparingQ and K are normalized onto the unit circle · only the angle mattersQKq·k = 47.3 · saturatesq·k = 0.8 · stabledot products can’t blow up

QK-Norm applies RMSNorm to the query and key vectors just before they are compared. Without it, query-key dot products can grow so large during training that attention saturates onto a single token and destabilizes the loss. It became standard in recent models (Qwen3+, Gemma) because it lets them train at higher learning rates with almost no extra compute.

Adoption over time

Share of new models that have included QK-Norm ops over time.

53%2022202320242025202653%20222023202420252026

See it in real models

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Qwen/Qwen3-0.6B architecture graphQwen/Qwen3-0.6Btext-generation · ↓ 29.7M · ♡ 2kOpen in visualizer Qwen/Qwen3-8B architecture graphQwen/Qwen3-8Btext-generation · ↓ 16.3M · ♡ 1kOpen in visualizer Comfy-Org/MiniMax-H3 architecture graphComfy-Org/MiniMax-H3↓ 11.8M · ♡ 1kOpen in visualizer nvidia/Qwen3.6-35B-A3B-NVFP4 architecture graphnvidia/Qwen3.6-35B-A3B-NVFP4text-generation · ↓ 11.7M · ♡ 553Open in visualizer Qwen/Qwen3.6-35B-A3B-FP8 architecture graphQwen/Qwen3.6-35B-A3B-FP8image-text-to-text · ↓ 11.1M · ♡ 351Open in visualizer google/gemma-4-26B-A4B-it architecture graphgoogle/gemma-4-26B-A4B-itimage-text-to-text · ↓ 10.1M · ♡ 1kOpen in visualizer google/gemma-4-31B-it architecture graphgoogle/gemma-4-31B-itimage-text-to-text · ↓ 10.0M · ♡ 4kOpen in visualizer Qwen/Qwen3.6-27B-FP8 architecture graphQwen/Qwen3.6-27B-FP8image-text-to-text · ↓ 9.0M · ♡ 346Open in visualizer Qwen/Qwen3-32B architecture graphQwen/Qwen3-32Btext-generation · ↓ 8.0M · ♡ 732Open in visualizer Qwen/Qwen3.5-4B architecture graphQwen/Qwen3.5-4Bimage-text-to-text · ↓ 7.5M · ♡ 812Open in visualizer Qwen/Qwen3-1.7B architecture graphQwen/Qwen3-1.7Btext-generation · ↓ 7.3M · ♡ 517Open in visualizer Qwen/Qwen3.6-27B architecture graphQwen/Qwen3.6-27Bimage-text-to-text · ↓ 6.9M · ♡ 2kOpen in visualizer

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Related concepts

Multi-head latent attention (MLA) Sparse attention (top-k token selection) Sliding-window attention Linear attention (gated DeltaNet) Mamba / state-space block (SSM) Short convolution block Grouped-query attention (GQA) Self-attention

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