Awesome

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

Authors: Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale, Farah Ferdaus, Aditya Tanikanti, Ken Raffenetti, Valerie Taylor, Murali Emani, Venkatram Vishwanath

Affliation: Argonne National Laboratory

Metrix of Evaluated Frameworks and Hardwares :

Framework/ Hardware	NVIDIA A100	NVIDIA H100	NVIDIA GH200	AMD MI250	AMD MI300X	Intel Max1550	Habana Gaudi2	Sambanova SN40L
vLLM	Yes	Yes	Yes	Yes	Yes	Yes	No	N/A
llama.cpp	Yes	Yes	Yes	Yes	Yes	Yes	N/A	N/A
TensorRT-LLM	Yes	Yes	Yes	N/A	N/A	N/A	N/A	N/A
DeepSpeed-MII	Yes	No	No	No	No	No	Yes	N/A
Sambaflow	N/A	N/A	N/A	N/A	N/A	N/A	N/A	Yes

Key Insights

Cite this work:

@misc{chittyvenkata2024llminferencebenchinferencebenchmarkinglarge,
     title={LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators}, 
     author={Krishna Teja Chitty-Venkata and Siddhisanket Raskar and Bharat Kale and Farah Ferdaus and Aditya Tanikanti and Ken Raffenetti and Valerie Taylor and Murali Emani and Venkatram Vishwanath},
     year={2024},
     eprint={2411.00136},
     archivePrefix={arXiv},
     primaryClass={cs.LG},
     url={https://arxiv.org/abs/2411.00136}, 
}

Acknowledgements

This research used resources of the Argonne Leadership Computing Facility, a U.S. Department of Energy (DOE) Office of Science user facility at Argonne National Laboratory and is based on research supported by the U.S. DOE Office of Science-Advanced Scientific Computing Research Program, under Contract No. DE-AC02-06CH11357. We gratefully acknowledge the computing resources provided and operated by the Joint Laboratory for System Evaluation (JLSE) at Argonne National Laboratory.