When NPUs Are Not Always Faster: A Stage-Level Analysis of Mobile LLM Inference
Deploying large language models (LLMs) on mobile devices increasingly relies on heterogeneous execution, yet no prior study has systematically characterized NPU effectiveness at the operator and pipeline level. We present the first stage-aware, multi-level benchmarking study of mobile LLM inference on a CPU-NPU heterogeneous SoC. We introduce an OPMASK-based controlled pipeline decomposition methodology that isolates communication, quantization, and computation overheads within the NPU execution path. Our results reveal a counter-intuitive stage-level performance reversal: CPUs outperform NPUs in the compute-intensive Prefill stage (up to 1.6x), while NPUs provide only limited acceleratio...
| Authors | Pu Li, Jiawen Qi, Qinyu Chen |
|---|---|
| Primary category | cs.AR |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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