Under vLLM serving, per-request inference energy plateaus at 100 concurrent requests, tracks parameter count closely within the Pythia family, and shows little variation across 3B-scale architectures.
Energy and policy considerations for modern deep learning research.Proceedings of the AAAI Conference on Artificial Intelligence, 34(09):13693–13696, April 2020
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Benchmarking Energy Efficiency of Large Language Models Using vLLM
Under vLLM serving, per-request inference energy plateaus at 100 concurrent requests, tracks parameter count closely within the Pythia family, and shows little variation across 3B-scale architectures.