Energy-Efficient LLM Serving via Disaggregated Attention--FFN and Flexible Frequency Scaling
Large language model (LLM) serving spans diverse applications with stringent service-level objectives (SLOs), often requiring GPUs to run at maximum frequencies and increasing energy consumption. Existing energy-management approaches adapt GPU frequencies only at the request or inference-phase level, overlooking operator-level differences in frequency sensitivity between Attention and feed-forward networks (FFNs). We find that the energy-optimal frequencies of Attention and FFN (A/F) differ and vary with the inference phase, workload, and system configurations. However, runtime variability and independent A/F frequency control create a large search space and high communication overhead. T...
| Authors | Cunchen Hu, Liangliang Xu, Tian Liu et al. |
|---|---|
| Primary category | cs.DC |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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