Scaling Shapley Energy Attribution to Large LLM Batches

arXiv:2611.13802v1 Announce Type: cross Abstract: LLM services have become essential infrastructure for applications such as chat assistants and coding agents. Service providers need to attribute resource consumption to individual requests for purposes such as billing. Among these resources, energy is a key metric for assessing the cost of serving each request. However, serving engines batch requests together, and GPUs report only aggregate energy consumption. Providers typically estimate each request's share in proportion to its token consumption, but this approach can produce substantially unfair estimates.