Characterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows
Large language models (LLMs) are increasingly used in applications forming multi-request workflows like document summarization, search-based copilots, and multi-agent programming. While these workflows unlock richer functionality, they also amplify latency and energy demand during inference. Existing measurement and benchmarking efforts either focus on assessing LLM inference systems or consider single-request evaluations, overlooking workflow dependencies and cross-request interactions unique to multi-request workflows. Moreover, the energy usage of such interdependent LLM calls remains underexplored. To address these gaps, this paper presents the first systematic characterization of per...
| Authors | Md. Monzurul Amin Ifath, Israat Haque |
|---|---|
| Primary category | cs.DC |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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