Understanding the Energy Scaling of Large Language Model Inference Across Context Lengths and Attention Architectures
The growing adoption of large language models (LLMs) has raised increasing concerns about the energy consumption and environmental impact of inference. This paper presents a systematic empirical study of decode-phase energy consumption across representative open-source LLMs employing Multi-Head Attention (MHA), Grouped Query Attention (GQA), and Grouped Query Attention with Sliding Window Attention (SWA) to characterize how attention architecture influences decode-phase energy consumption under varying inference workloads. We evaluate four models across different context lengths, batch sizes, and generation workloads while measuring GPU energy using NVIDIA hardware counters. We examine th...
| Authors | Molka Chkir, Syed Muhammad Danish, Jos Höll et al. |
|---|---|
| Primary category | cs.LG |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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