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Transformer-based large language model (LLM) inference serving is now the backbone of many cloud services.
FaRM: Fast remote memory
Aleksandar Dragojević, Dushyanth Narayanan, Miguel Castro, and Orion Hodson · 2014
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Train big, then compress: Rethinking model size for efficient training and inference of transformers
Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, and Joey Gonzalez · 2020
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Rammer: Enabling holistic deep learning compiler optimizations with { \{ rTasks } \}
Lingxiao Ma, Zhiqiang Xie, Zhi Yang, Jilong Xue, Youshan Miao, Wei Cui, Wenxiang Hu, Fan Yang, Lintao Zhang, and Lidong Zhou · 2020
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Lightseq: A high performance inference library for transformers
Xiaohui Wang, Ying Xiong, Yang Wei, Mingxuan Wang, and Lei Li · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale
Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunji Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, et al · 2022
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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Llm. int8 (): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
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Optq: Accurate quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
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Clio: A Hardware-Software Co-Designed Disaggregated Memory System
Zhiyuan Guo, Yizhou Shan, Xuhao Luo, Yutong Huang, and Yiying Zhang · 2022
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Zeroquant: Efficient and affordable post-training quantization for large-scale transformers
Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, and Yuxiong He · 2022
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Orca: A distributed serving system for { \{ Transformer-Based } \} generative models
Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, Soojeong Kim, and Byung-Gon Chun · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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Sarathi: Efficient llm inference by piggybacking decodes with chunked prefills
Amey Agrawal, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S Gulavani, and Ramachandran Ramjee · 2023
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Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2023
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Spqr: A sparse-quantized representation for near-lossless llm weight compression
Memgpt: Towards llms as operating systems
Charles Packer, Vivian Fang, Shishir G Patil, Kevin Lin, Sarah Wooders, and Joseph E Gonzalez · 2023
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Splitwise: Efficient generative llm inference using phase splitting
Pratyush Patel, Esha Choukse, Chaojie Zhang, Íñigo Goiri, Aashaka Shah, Saeed Maleki, and Ricardo Bianchini · 2023
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Efficiently scaling transformer inference
Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Jonathan Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean · 2023
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Fairness in serving large language models
Ying Sheng, Shiyi Cao, Dacheng Li, Banghua Zhu, Zhuohan Li, Danyang Zhuo, Joseph E Gonzalez, and Ion Stoica · 2023
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Flexgen: High-throughput generative inference of large language models with a single gpu
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Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan Alistarh · 2023
Cited alongside, same era.
Towards next-generation intelligent assistants leveraging llm techniques
Xin Luna Dong, Seungwhan Moon, Yifan Ethan Xu, Kshitiz Malik, and Zhou Yu · 2023
Cited alongside, same era.
Flashdecoding++: Faster large language model inference on gpus
Ke Hong, Guohao Dai, Jiaming Xu, Qiuli Mao, Xiuhong Li, Jun Liu, Kangdi Chen, Hanyu Dong, and Yu Wang · 2023
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Gpt-zip: Deep compression of finetuned large language models
Berivan Isik, Hermann Kumbong, Wanyi Ning, Xiaozhe Yao, Sanmi Koyejo, and Ce Zhang · 2023
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The promise and peril of generative ai
A Jo · 2023
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Efficient memory management for large language model serving with pagedattention
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Cited alongside, same era.
Alpaserve: Statistical multiplexing with model parallelism for deep learning serving
Zhuohan Li, Lianmin Zheng, Yinmin Zhong, Vincent Liu, Ying Sheng, Xin Jin, Yanping Huang, Zhifeng Chen, Hao Zhang, Joseph E Gonzalez, et al · 2023
Cited alongside, same era.
https://docs.aws.amazon.com/bedrock/latest/userguide/inference-parameters.html
AWS Bedrock
Cited in the paper.
Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Ré, Ion Stoica, and Ce Zhang · 2023
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Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Fast distributed inference serving for large language models
Bingyang Wu, Yinmin Zhong, Zili Zhang, Gang Huang, Xuanzhe Liu, and Xin Jin · 2023
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Smoothquant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han · 2023
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A comprehensive study on post-training quantization for large language models
Zhewei Yao, Cheng Li, Xiaoxia Wu, Stephen Youn, and Yuxiong He · 2023
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Bytetransformer: A high-performance transformer boosted for variable-length inputs
Yujia Zhai, Chengquan Jiang, Leyuan Wang, Xiaoying Jia, Shang Zhang, Zizhong Chen, Xin Liu, and Yibo Zhu · 2023
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Response length perception and sequence scheduling: An llm-empowered llm inference pipeline
Zangwei Zheng, Xiaozhe Ren, Fuzhao Xue, Yang Luo, Xin Jiang, and Yang You · 2023
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