Characterization of Request and Token Energy Costs for LLM Inference Workloads on GPU Platforms
Large language model (LLM) inference serving is priced by tokens, but GPU energy is consumed over inference windows. This accounting mismatch makes token-normalized metrics incomplete, since average output-token energy can decrease even when total request energy increases. We characterize this behavior with a decomposed energy model: a fixed one-time prefill with a fixed generation setup cost, while each output-token generation step adds marginal step energy. We evaluate this LLM inference energy model on NVIDIA H100 and H200 GPUs across dense and mixture-of-experts (MoE) models, reporting both request energy and token energy as functions of model type (M), phase (P), batch size (B), cont...
| Authors | Prabhu Vellaisamy, Vanessa Lam, Shawn Blanton et al. |
|---|---|
| Primary category | cs.PF |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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