Attention

Linear attention (gated DeltaNet)

Replaces the attention matrix with a fixed-size recurrent state. Cost stays constant per token, no KV cache growth.

First page of Transformers are RNNs: Fast Autoregressive Transformers with Linear AttentionIntroduced inJun 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear AttentionKatharopoulos et al. · arXiv 2006.16236 ↗
Attentionthe KV cache grows with every tokenGated recurrent stateevery token rewrites one fixed SσScost per token growscost stays constant

Linear attention keeps a compact running state that is updated once per token (here with the gated delta rule: the state is selectively decayed and rewritten), so generation cost does not grow with context length and there is no per-token KV cache. Qwen3.5-style hybrids use linear attention in most layers and keep periodic full-attention layers for precise long-range recall. The best of both worlds for long contexts.

Adoption over time

Share of new models that have included linear attention over time.

30%2022202320242025202630%20222023202420252026

See it in real models

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Qwen/Qwen3.5-9B architecture graphQwen/Qwen3.5-9Bimage-text-to-text · ↓ 13.6M · ♡ 2kOpen in visualizer Qwen/Qwen3.6-35B-A3B-FP8 architecture graphQwen/Qwen3.6-35B-A3B-FP8image-text-to-text · ↓ 13.4M · ♡ 358Open in visualizer nvidia/Qwen3.6-35B-A3B-NVFP4 architecture graphnvidia/Qwen3.6-35B-A3B-NVFP4text-generation · ↓ 12.2M · ♡ 569Open in visualizer Qwen/Qwen3.6-27B-FP8 architecture graphQwen/Qwen3.6-27B-FP8image-text-to-text · ↓ 9.0M · ♡ 347Open in visualizer Qwen/Qwen3.5-4B architecture graphQwen/Qwen3.5-4Bimage-text-to-text · ↓ 7.8M · ♡ 848Open in visualizer Qwen/Qwen3.6-27B architecture graphQwen/Qwen3.6-27Bimage-text-to-text · ↓ 6.3M · ♡ 2kOpen in visualizer Qwen/Qwen3.6-35B-A3B architecture graphQwen/Qwen3.6-35B-A3Bimage-text-to-text · ↓ 5.4M · ♡ 3kOpen in visualizer unsloth/Qwen3.6-27B-NVFP4 architecture graphunsloth/Qwen3.6-27B-NVFP4image-text-to-text · ↓ 3.7M · ♡ 276Open in visualizer Qwen/Qwen3.8-27B-FP8 architecture graphQwen/Qwen3.8-27B-FP8image-text-to-text · ↓ 3.4M · ♡ 693Open in visualizer ornith-ai/Ornith-1.0-35B architecture graphornith-ai/Ornith-1.0-35Btext-generation · ↓ 3.3M · ♡ 505Open in visualizer deepreinforce-ai/Ornith-1.0-35B architecture graphdeepreinforce-ai/Ornith-1.0-35Btext-generation · ↓ 3.3M · ♡ 505Open in visualizer Qwen/Qwen3.5-2B architecture graphQwen/Qwen3.5-2Bimage-text-to-text · ↓ 3.1M · ♡ 373Open in visualizer

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Related concepts

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

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