Inference Optimization of Foundation Models on AI Accelerators

Author:

Park Youngsuk1ORCID,Budhathoki Kailash2ORCID,Chen Liangfu1ORCID,Kübler Jonas M.2ORCID,Huang Jiaji1ORCID,Kleindessner Matthäus2ORCID,Huan Jun1ORCID,Cevher Volkan3ORCID,Wang Yida1ORCID,Karypis George1ORCID

Affiliation:

1. AWS AI, Santa Clara, CA, USA

2. AWS AI, Tübingen, Germany

3. AWS AI & EPFL, Tübingen, Germany

Publisher

ACM

Reference140 articles.

1. Amey Agrawal, Nitin Kedia, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S Gulavani, Alexey Tumanov, and Ramachandran Ramjee. 2024. Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve. arXiv preprint arXiv:2403.02310 (2024).

2. Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. 2023. Gqa: Training generalized multi-query transformer models from multi-head checkpoints. arXiv preprint arXiv:2305.13245 (2023).

3. Ebtesam Almazrouei Hamza Alobeidli Abdulaziz Alshamsi Alessandro Cappelli Ruxandra Cojocaru Mérouane Debbah Étienne Goffinet Daniel Hesslow Julien Launay Quentin Malartic et al. 2023. The falcon series of open language models. arXiv preprint arXiv:2311.16867 (2023).

4. AMD. 2024. AMD matrix cores. https://rocm.blogs.amd.com/software-tools-optimization/matrix-cores/README.html

5. N. Yang amd T. Ge, L. Wang, B. Jiao, D. Jiang, L. Yang, R. Majumder, and F. Wei. 2023. Inference with Reference: Lossless Acceleration of Large Language Models. arXiv preprint arXiv:2304.04487 (2023).

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