AINeutralarXiv – CS AI · Apr 146/10
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Characterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows
Researchers present the first systematic study of performance-energy trade-offs in multi-request LLM inference workflows, using NVIDIA A100 GPUs and vLLM/Parrot serving systems. The study identifies batch size as the most impactful optimization lever, though effectiveness varies by workload type, and reveals that workflow-aware scheduling can reduce energy consumption under power constraints.
🏢 Nvidia