Empowering Hybrid Attention Models on NPUs
Hybrid attention models have emerged as a crucial architecture for Large Language Models (LLMs) (e.g., the Qwen3.5 and Kimi series). Their memory and computational efficiency make them highly attractive for on-device inference, forming a promising synergy with edge Neural Processing Units (NPUs). However, naive execution of these hybrid models on edge NPUs fails to deliver these benefits, often bottlenecking the prefill stage due to severe memory-system inefficiencies and architectural mismatches within the linear attention (LA) layers. We present HA-NPU, the first system to enable efficient hybrid attention LLM inference on edge NPUs without modifying the underlying algorithms. HA-NPU en...
| Authors | Yinyuan Zhang, Daliang Xu, Xiaolong Huang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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