Awesome-Efficient-LLM

A curated list for Efficient Large Language Models

Full List

• Network Pruning / Sparsity
• Knowledge Distillation
• Quantization
• Inference Acceleration
• Efficient MOE
• Efficient Architecture of LLM
• KV Cache Compression
• Text Compression
• Low-Rank Decomposition
• Hardware / System
• Tuning
• Survey
• Leaderboard

Please check out all the papers by selecting the sub-area you're interested in. On this main page, we're showing papers released in the past 90 days.

🚀 Updates

• May 29, 2024: We've had this awesome list for a year now 🥰! It's grown pretty long, so we're reorganizing it and would divide the list by their specific areas into different readme.
• Sep 27, 2023: Add tag for papers accepted at NeurIPS'23.
• Sep 6, 2023: Add a new subdirectory project/ to organize those projects that are designed for developing a lightweight LLM.
• July 11, 2023: In light of the numerous publications that conduct experiments using PLMs (such as BERT, BART) currently, a new subdirectory efficient_plm/ is created to house papers that are applicable to PLMs but have yet to be verified for their effectiveness on LLMs (not implying that they are not suitable on LLM).

💮 Contributing

If you'd like to include your paper, or need to update any details such as conference information or code URLs, please feel free to submit a pull request. You can generate the required markdown format for each paper by filling in the information in generate_item.py and execute python generate_item.py. We warmly appreciate your contributions to this list. Alternatively, you can email me with the links to your paper and code, and I would add your paper to the list at my earliest convenience.

⭐ Recommended Paper

For each topic, we have curated a list of recommended papers that have garnered relatively high GitHub stars or citations.

Paper from June 13, 2024 - Now (see Full List from May 22, 2023 here)

Quick Link

• Network Pruning / Sparsity
• Knowledge Distillation
• Quantization
• Inference Acceleration
• Efficient MOE
• Efficient Architecture of LLM
• KV Cache Compression
• Text Compression
• Low-Rank Decomposition
• Hardware / System
• Tuning
• Survey

Network Pruning / Sparsity

Title & Authors	Introduction	Links
⭐ SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot Elias Frantar, Dan Alistarh		Github paper
⭐ LLM-Pruner: On the Structural Pruning of Large Language Models Xinyin Ma, Gongfan Fang, Xinchao Wang		Github paper
⭐ A Simple and Effective Pruning Approach for Large Language Models Mingjie Sun, Zhuang Liu, Anna Bair, J. Zico Kolter		Github Paper
⭐ Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, Danqi Chen		Github Paper
STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning Jaeseong Lee, seung-won hwang, Aurick Qiao, Daniel F Campos, Zhewei Yao, Yuxiong He		Paper
PAT: Pruning-Aware Tuning for Large Language Models Yijiang Liu, Huanrui Yang, Youxin Chen, Rongyu Zhang, Miao Wang, Yuan Du, Li Du		Github Paper
LLM Pruning and Distillation in Practice: The Minitron Approach Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov		Paper
Language-specific Calibration for Pruning Multilingual Language Models Simon Kurz, Zhixue Zhao, Jian-Jia Chen, Lucie Flek		Paper
LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models Yupeng Su, Ziyi Guan, Xiaoqun Liu, Tianlai Jin, Dongkuan Wu, Graziano Chesi, Ngai Wong, Hao Yu		Github Paper
Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism Guanchen Li, Xiandong Zhao, Lian Liu, Zeping Li, Dong Li, Lu Tian, Jie He, Ashish Sirasao, Emad Barsoum		Paper
A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models Pengxiang Zhao, Hanyu Hu, Ping Li, Yi Zheng, Zhefeng Wang, Xiaoming Yuan		Paper
Pruning Large Language Models with Semi-Structural Adaptive Sparse Training Weiyu Huang, Guohao Jian, Yuezhou Hu, Jun Zhu, Jianfei Chen		Paper
Greedy Output Approximation: Towards Efficient Structured Pruning for LLMs Without Retraining Jianwei Li, Yijun Dong, Qi Lei		Paper
Compact Language Models via Pruning and Knowledge Distillation Saurav Muralidharan, Sharath Turuvekere Sreenivas, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov		Github Paper
MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi		Paper
Reconstruct the Pruned Model without Any Retraining Pingjie Wang, Ziqing Fan, Shengchao Hu, Zhe Chen, Yanfeng Wang, Yu Wang		Paper
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated Hongyu Wang, Shuming Ma, Ruiping Wang, Furu Wei		Paper
Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations Bowen Shen, Zheng Lin, Daren Zha, Wei Liu, Jian Luan, Bin Wang, Weiping Wang		Github Paper
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression Zhichao Xu, Ashim Gupta, Tao Li, Oliver Bentham, Vivek Srikumar		Github Paper
Flextron: Many-in-One Flexible Large Language Model Ruisi Cai, Saurav Muralidharan, Greg Heinrich, Hongxu Yin, Zhangyang Wang, Jan Kautz, Pavlo Molchanov		Paper
BlockPruner: Fine-grained Pruning for Large Language Models Longguang Zhong, Fanqi Wan, Ruijun Chen, Xiaojun Quan, Liangzhi Li		Github Paper
Structured Pruning for Large Language Models Using Coupled Components Elimination and Minor Fine-tuning Honghe Zhang, XiaolongShi XiaolongShi, Jingwei Sun, Guangzhong Sun		Paper
FoldGPT: Simple and Effective Large Language Model Compression Scheme Songwei Liu, Chao Zeng, Lianqiang Li, Chenqian Yan, Lean Fu, Xing Mei, Fangmin Chen		Paper
Learning Neural Networks with Sparse Activations Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka		Paper
Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization Sungbin Shin, Wonpyo Park, Jaeho Lee, Namhoon Lee		Paper
ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models Yash Akhauri, Ahmed F AbouElhamayed, Jordan Dotzel, Zhiru Zhang, Alexander M Rush, Safeen Huda, Mohamed S Abdelfattah		Github Paper
Optimization-based Structural Pruning for Large Language Models without Back-Propagation Yuan Gao, Zujing Liu, Weizhong Zhang, Bo Du, Gui-Song Xia		Paper
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models Xiang Meng, Kayhan Behdin, Haoyue Wang, Rahul Mazumder		Paper

Knowledge Distillation

Title & Authors	Introduction	Links
⭐ Knowledge Distillation of Large Language Models Yuxian Gu, Li Dong, Furu Wei, Minlie Huang		Github Paper
The Mamba in the Llama: Distilling and Accelerating Hybrid Models Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, Tri Dao		Github Paper
FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation KaShun Shum, Minrui Xu, Jianshu Zhang, Zixin Chen, Shizhe Diao, Hanze Dong, Jipeng Zhang, Muhammad Omer Raza		Paper
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models Meiyun Wang, Masahiro Suzuki, Hiroki Sakaji, Kiyoshi Izumi		Paper
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models Aviv Bick, Kevin Y. Li, Eric P. Xing, J. Zico Kolter, Albert Gu		Paper
Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting Emmanuel Aboah Boateng, Cassiano O. Becker, Nabiha Asghar, Kabir Walia, Ashwin Srinivasan, Ehi Nosakhare, Victor Dibia, Soundar Srinivasan		Paper
LaDiMo: Layer-wise Distillation Inspired MoEfier Sungyoon Kim, Youngjun Kim, Kihyo Moon, Minsung Jang		Paper
BOND: Aligning LLMs with Best-of-N Distillation Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Nino Vieillard et al		Paper
Enhancing Data-Limited Graph Neural Networks by Actively Distilling Knowledge from Large Language Models Quan Li, Tianxiang Zhao, Lingwei Chen, Junjie Xu, Suhang Wang		Paper
DDK: Distilling Domain Knowledge for Efficient Large Language Models Jiaheng Liu, Chenchen Zhang, Jinyang Guo, Yuanxing Zhang, Haoran Que, Ken Deng, Zhiqi Bai, Jie Liu, Ge Zhang, Jiakai Wang, Yanan Wu, Congnan Liu, Wenbo Su, Jiamang Wang, Lin Qu, Bo Zheng		Paper
Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang		Paper
Don't Throw Away Data: Better Sequence Knowledge Distillation Jun Wang, Eleftheria Briakou, Hamid Dadkhahi, Rishabh Agarwal, Colin Cherry, Trevor Cohn		Paper
Multi-Granularity Semantic Revision for Large Language Model Distillation Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang		Paper
BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation Minchong Li, Feng Zhou, Xiaohui Song		Paper

Quantization

Title & Authors	Introduction	Links
⭐ GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh		Github Paper
⭐ SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, Song Han		Github Paper
⭐ AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, Song Han		Github Paper
⭐ OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, Ping Luo		Github Paper
⭐ SqueezeLLM: Dense-and-Sparse Quantization Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, Kurt Keutzer		Github Paper
⭐ Extreme Compression of Large Language Models via Additive Quantization Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh		Github Paper
The Uniqueness of LLaMA3-70B with Per-Channel Quantization: An Empirical Study Minghai Qin		Paper
Matmul or No Matmal in the Era of 1-bit LLMs Jinendra Malekar, Mohammed E. Elbtity, Ramtin Zand Co		Paper
MobileQuant: Mobile-friendly Quantization for On-device Language Models Fuwen Tan, Royson Lee, Łukasz Dudziak, Shell Xu Hu, Sourav Bhattacharya, Timothy Hospedales, Georgios Tzimiropoulos, Brais Martinez		Github Paper
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models Chao Zeng, Songwei Liu, Yusheng Xie, Hong Liu, Xiaojian Wang, Miao Wei, Shu Yang, Fangmin Chen, Xing Mei		Github Paper
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs Peijie Dong, Lujun Li, Dayou Du, Yuhan Chen, Zhenheng Tang, Qiang Wang, Wei Xue, Wenhan Luo, Qifeng Liu, Yike Guo, Xiaowen Chu		Paper
Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal Balance Ao Shen, Qiang Wang, Zhiquan Lai, Xionglve Li, Dongsheng Li		Github Paper
Scalify: scale propagation for efficient low-precision LLM training Paul Balança, Sam Hosegood, Carlo Luschi, Andrew Fitzgibbon		Github Paper
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models Mengzhao Chen, Wenqi Shao, Peng Xu, Jiahao Wang, Peng Gao, Kaipeng Zhang, Yu Qiao, Ping Luo		Github Paper
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices Jung Hyun Lee, Jeonghoon Kim, June Yong Yang, Se Jung Kwon, Eunho Yang, Kang Min Yoo, Dongsoo Lee		Github Paper
Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models Ayush Kaushal, Tejas Pandey, Tejas Vaidhya, Aaryan Bhagat, Irina Rish		Github Paper
Fast Matrix Multiplications for Lookup Table-Quantized LLMs Han Guo, William Brandon, Radostin Cholakov, Jonathan Ragan-Kelley, Eric P. Xing, Yoon Kim		Github Paper
LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid Tianyi Zhang, Anshumali Shrivastava		Paper
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization Seungwoo Son, Wonpyo Park, Woohyun Han, Kyuyeun Kim, Jaeho Lee		Paper
RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization Xijie Huang, Zechun Liu, Shih-Yang Liu, Kwang-Ting Cheng		Github Paper
FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation Liqun Ma, Mingjie Sun, Zhiqiang Shen		Github Paper
GPTQT: Quantize Large Language Models Twice to Push the Efficiency Yipin Guo, Yilin Lang, Qinyuan Ren		Paper
T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge Jianyu Wei, Shijie Cao, Ting Cao, Lingxiao Ma, Lei Wang, Yanyong Zhang, Mao Yang		Github Paper
Variable Layer-Wise Quantization: A Simple and Effective Approach to Quantize LLMs Razvan-Gabriel Dumitru, Vikas Yadav, Rishabh Maheshwary, Paul-Ioan Clotan, Sathwik Tejaswi Madhusudhan, Mihai Surdeanu		Github Paper
CDQuant: Accurate Post-training Weight Quantization of Large Pre-trained Models using Greedy Coordinate Descent Pranav Ajit Nair, Arun Sai Suggala		Paper
SDQ: Sparse Decomposed Quantization for LLM Inference Geonhwa Jeong, Po-An Tsai, Stephen W. Keckler, Tushar Krishna		Paper
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization Seungwoo Son, Wonpyo Park, Woohyun Han, Kyuyeun Kim, Jaeho Lee		Paper
Attention-aware Post-training Quantization without Backpropagation Junhan Kim, Ho-young Kim, Eulrang Cho, Chungman Lee, Joonyoung Kim, Yongkweon Jeon		Paper
Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models Dongwon Jo, Taesu Kim, Yulhwa Kim, Jae-Joon Kim		Paper
QQQ: Quality Quattuor-Bit Quantization for Large Language Models Ying Zhang, Peng Zhang, Mincong Huang, Jingyang Xiang, Yujie Wang, Chao Wang, Yineng Zhang, Lei Yu, Chuan Liu, Wei Lin		Github Paper

Inference Acceleration

Title & Authors	Introduction	Links
⭐ Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time Zichang Liu, Jue WANG, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Re, Beidi Chen		Github Paper
⭐ SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Xinhao Cheng, Zeyu Wang, Rae Ying Yee Wong, Zhuoming Chen, Daiyaan Arfeen, Reyna Abhyankar, Zhihao Jia		Github paper
⭐ Efficient Streaming Language Models with Attention Sinks Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, Mike Lewis		Github Paper
⭐ EAGLE: Lossless Acceleration of LLM Decoding by Feature Extrapolation Yuhui Li, Chao Zhang, and Hongyang Zhang		Github Blog
⭐ Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, Tri Dao		Github Paper
Sirius: Contextual Sparsity with Correction for Efficient LLMs Yang Zhou, Zhuoming Chen, Zhaozhuo Xu, Victoria Lin, Beidi Chen		Github Paper
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang		Github Paper
Path-Consistency: Prefix Enhancement for Efficient Inference in LLM Jiace Zhu, Yingtao Shen, Jie Zhao, An Zou		Paper
Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation Lujun Gui, Bin Xiao, Lei Su, Weipeng Chen		Paper
Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling Xianzhen Luo, Yixuan Wang, Qingfu Zhu, Zhiming Zhang, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che		Paper
Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion Jacob K Christopher, Brian R Bartoldson, Bhavya Kailkhura, Ferdinando Fioretto		Paper
Clover-2: Accurate Inference for Regressive Lightweight Speculative Decoding Bin Xiao, Lujun Gui, Lei Su, Weipeng Chen		Github Paper
Accelerating Large Language Model Inference with Self-Supervised Early Exits Florian Valade		Paper
An Efficient Inference Framework for Early-exit Large Language Models Ruijie Miao, Yihan Yan, Xinshuo Yao, Tong Yang		Paper
Inference acceleration for large language models using "stairs" assisted greedy generation Domas Grigaliūnas, Mantas Lukoševičius		Paper
LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference Qichen Fu, Minsik Cho, Thomas Merth, Sachin Mehta, Mohammad Rastegari, Mahyar Najibi		Paper
Adaptive Draft-Verification for Efficient Large Language Model Decoding Xukun Liu, Bowen Lei, Ruqi Zhang, Dongkuan Xu		Paper
Multi-Token Joint Speculative Decoding for Accelerating Large Language Model Inference Zongyue Qin, Ziniu Hu, Zifan He, Neha Prakriya, Jason Cong, Yizhou Sun		Paper
LiveMind: Low-latency Large Language Models with Simultaneous Inference Chuangtao Chen, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Ulf Schlichtmann, Bing Li		Github Paper
S2D: Sorted Speculative Decoding For More Efficient Deployment of Nested Large Language Models Parsa Kavehzadeh, Mohammadreza Pourreza, Mojtaba Valipour, Tinashu Zhu, Haoli Bai, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh		Paper
Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers Chao Lou, Zixia Jia, Zilong Zheng, Kewei Tu		Paper
EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang		Github Paper
Interpreting Attention Layer Outputs with Sparse Autoencoders Connor Kissane, Robert Krzyzanowski, Joseph Isaac Bloom, Arthur Conmy, Neel Nanda		Paper
Near-Lossless Acceleration of Long Context LLM Inference with Adaptive Structured Sparse Attention Qianchao Zhu, Jiangfei Duan, Chang Chen, Siran Liu, Xiuhong Li, Guanyu Feng, Xin Lv, Huanqi Cao, Xiao Chuanfu, Xingcheng Zhang, Dahua Lin, Chao Yang		Paper
MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression Tianyu Fu, Haofeng Huang, Xuefei Ning, Genghan Zhang, Boju Chen et al		Github Paper
Optimized Speculative Sampling for GPU Hardware Accelerators Dominik Wagner, Seanie Lee, Ilja Baumann, Philipp Seeberger, Korbinian Riedhammer, Tobias Bocklet		Paper
HiP Attention: Sparse Sub-Quadratic Attention with Hierarchical Attention Pruning Heejun Lee, Geon Park, Youngwan Lee, Jina Kim, Wonyoung Jeong, Myeongjae Jeon, Sung Ju Hwang		Paper

Efficient MOE

Title & Authors	Introduction	Links
⭐ Fast Inference of Mixture-of-Experts Language Models with Offloading Artyom Eliseev, Denis Mazur		Github Paper
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts Zeliang Zhang, Xiaodong Liu, Hao Cheng, Chenliang Xu, Jianfeng Gao		Paper
Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs Enshu Liu, Junyi Zhu, Zinan Lin, Xuefei Ning, Matthew B. Blaschko, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang		Github Paper
Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark Pingzhi Li, Xiaolong Jin, Yu Cheng, Tianlong Chen		Github Paper
ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models Jing Liu, Ruihao Gong, Mingyang Zhang, Yefei He, Jianfei Cai, Bohan Zhuang		Paper

Efficient Architecture of LLM

Title & Authors	Introduction	Links
⭐ MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT Omkar Thawakar, Ashmal Vayani, Salman Khan, Hisham Cholakal, Rao M. Anwer, Michael Felsberg, Tim Baldwin, Eric P. Xing, Fahad Shahbaz Khan		Github Paper Model
⭐ Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length Xuezhe Ma, Xiaomeng Yang, Wenhan Xiong, Beidi Chen, Lili Yu, Hao Zhang, Jonathan May, Luke Zettlemoyer, Omer Levy, Chunting Zhou		Github Paper
SentenceVAE: Enable Next-sentence Prediction for Large Language Models with Faster Speed, Higher Accuracy and Longer Context Hongjun An, Yifan Chen, Zhe Sun, Xuelong Li		Paper
Efficient LLM Training and Serving with Heterogeneous Context Sharding among Attention Heads Xihui Lin, Yunan Zhang, Suyu Ge, Barun Patra, Vishrav Chaudhary, Xia Song		Github Paper
Beyond KV Caching: Shared Attention for Efficient LLMs Bingli Liao, Danilo Vasconcellos Vargas		Github Paper

KV Cache Compression

Title & Authors	Introduction	Links
⭐ Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, Jianfeng Gao		Paper
A First Look At Efficient And Secure On-Device LLM Inference Against KV Leakage Huan Yang, Deyu Zhang, Yudong Zhao, Yuanchun Li, Yunxin Liu		Paper
Post-Training Sparse Attention with Double Sparsity Shuo Yang, Ying Sheng, Joseph E. Gonzalez, Ion Stoica, Lianmin Zheng		Github Paper
Eigen Attention: Attention in Low-Rank Space for KV Cache Compression Utkarsh Saxena, Gobinda Saha, Sakshi Choudhary, Kaushik Roy		Github Paper
Zero-Delay QKV Compression for Mitigating KV Cache and Network Bottlenecks in LLM Inference Zeyu Zhang,Haiying Shen		Paper
Finch: Prompt-guided Key-Value Cache Compression Giulio Corallo, Paolo Papotti		Paper
Palu: Compressing KV-Cache with Low-Rank Projection Chi-Chih Chang, Wei-Cheng Lin, Chien-Yu Lin, Chong-Yan Chen, Yu-Fang Hu, Pei-Shuo Wang, Ning-Chi Huang, Luis Ceze, Kai-Chiang Wu		Github Paper
ThinK: Thinner Key Cache by Query-Driven Pruning Yuhui Xu, Zhanming Jie, Hanze Dong, Lei Wang, Xudong Lu, Aojun Zhou, Amrita Saha, Caiming Xiong, Doyen Sahoo		Paper
RazorAttention: Efficient KV Cache Compression Through Retrieval Heads Hanlin Tang, Yang Lin, Jing Lin, Qingsen Han, Shikuan Hong, Yiwu Yao, Gongyi Wang		Paper
PQCache: Product Quantization-based KVCache for Long Context LLM Inference Hailin Zhang, Xiaodong Ji, Yilin Chen, Fangcheng Fu, Xupeng Miao, Xiaonan Nie, Weipeng Chen, Bin Cui		Paper
GoldFinch: High Performance RWKV/Transformer Hybrid with Linear Pre-Fill and Extreme KV-Cache Compression Daniel Goldstein, Fares Obeid, Eric Alcaide, Guangyu Song, Eugene Cheah		Github Paper
Efficient Sparse Attention needs Adaptive Token Release Chaoran Zhang, Lixin Zou, Dan Luo, Min Tang, Xiangyang Luo, Zihao Li, Chenliang Li		Github Paper
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches Jiayi Yuan, Hongyi Liu, Shaochen (Henry)Zhong, Yu-Neng Chuang, Songchen Li et al		Github Paper
MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding Zayd Muhammad Kawakibi Zuhri, Muhammad Farid Adilazuarda, Ayu Purwarianti, Alham Fikri Aji		Github Paper

Text Compression

Title & Authors	Introduction	Links
⭐ LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu		Github Paper
⭐ LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu		Github Paper
Efficient LLM Context Distillation Rajesh Upadhayayaya, Zachary Smith, Chritopher Kottmyer, Manish Raj Osti		Paper
Enhancing and Accelerating Large Language Models via Instruction-Aware Contextual Compression Haowen Hou, Fei Ma, Binwen Bai, Xinxin Zhu, Fei Yu		Github Paper
500xCompressor: Generalized Prompt Compression for Large Language Models Zongqian Li, Yixuan Su, Nigel Collier		Github Paper
QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression Wenshan Wang, Yihang Wang, Yixing Fan, Huaming Liao, Jiafeng Guo		Github Paper
Characterizing Prompt Compression Methods for Long Context Inference Siddharth Jha, Lutfi Eren Erdogan, Sehoon Kim, Kurt Keutzer, Amir Gholami		Paper
Entropy Law: The Story Behind Data Compression and LLM Performance Mingjia Yin, Chuhan Wu, Yufei Wang, Hao Wang, Wei Guo, Yasheng Wang, Yong Liu, Ruiming Tang, Defu Lian, Enhong Chen		Paper
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning Jiaru Zou, Mengyu Zhou, Tao Li, Shi Han, Dongmei Zhang		Paper
Brevity is the soul of wit: Pruning long files for code generation Aaditya K. Singh, Yu Yang, Kushal Tirumala, Mostafa Elhoushi, Ari S. Morcos		Paper

Low-Rank Decomposition

Title & Authors	Introduction	Links
MoDeGPT: Modular Decomposition for Large Language Model Compression Chi-Heng Lin, Shangqian Gao, James Seale Smith, Abhishek Patel, Shikhar Tuli, Yilin Shen, Hongxia Jin, Yen-Chang Hsu		Paper
MCNC: Manifold Constrained Network Compression Chayne Thrash, Ali Abbasi, Parsa Nooralinejad, Soroush Abbasi Koohpayegani, Reed Andreas, Hamed Pirsiavash, Soheil Kolouri		Paper

Hardware/System

Title & Authors	Introduction	Links
OPAL: Outlier-Preserved Microscaling Quantization A ccelerator for Generative Large Language Models Jahyun Koo, Dahoon Park, Sangwoo Jung, Jaeha Kung		Paper
Accelerating Large Language Model Training with Hybrid GPU-based Compression Lang Xu, Quentin Anthony, Qinghua Zhou, Nawras Alnaasan, Radha R. Gulhane, Aamir Shafi, Hari Subramoni, Dhabaleswar K. Panda		Paper
LUT Tensor Core: Lookup Table Enables Efficient Low-Bit LLM Inference Acceleration Zhiwen Mo, Lei Wang, Jianyu Wei, Zhichen Zeng, Shijie Cao, Lingxiao Ma, Naifeng Jing, Ting Cao, Jilong Xue, Fan Yang, Mao Yang		Paper
Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference Rohan Baskar Prabhakar, Hengrui Zhang, David Wentzlaff		Paper
SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving Andreas Kosmas Kakolyris, Dimosthenis Masouros, Petros Vavaroutsos, Sotirios Xydis, Dimitrios Soudris		Paper
Designing Efficient LLM Accelerators for Edge Devices Jude Haris, Rappy Saha, Wenhao Hu, José Cano		Paper
PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation Branden Butler, Sixing Yu, Arya Mazaheri, Ali Jannesari		Paper
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao		Github Paper Blog
Preble: Efficient Distributed Prompt Scheduling for LLM Serving Vikranth Srivatsa, Zijian He, Reyna Abhyankar, Dongming Li, Yiying Zhang		Paper
EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting Zhongzhi Yu, Zheng Wang, Yuhan Li, Haoran You, Ruijie Gao, Xiaoya Zhou, Sreenidhi Reedy Bommu, Yang Katie Zhao, Yingyan Celine Lin		Github Paper
Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization Jungi Lee, Wonbeom Lee, Jaewoong Sim		Paper

Tuning

Title & Authors	Introduction	Links
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs Afia Anjum, Maksim E. Eren, Ismael Boureima, Boian Alexandrov, Manish Bhattarai		Paper
Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning Yun-Da Tsai, Mingjie Liu, Haoxing Ren		Paper
PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs Dan Peng, Zhihui Fu, Jun Wang		Paper
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning Haobo Song, Hao Zhao, Soumajit Majumder, Tao Lin		Github Paper
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead Rickard Brüel-Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen et al		Paper
BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks Amrutha Varshini Ramesh, Vignesh Ganapathiraman, Issam H. Laradji, Mark Schmidt		Paper

Survey

Title & Authors	Introduction	Links
Hardware Acceleration of LLMs: A comprehensive survey and comparison Nikoletta Koilia, Christoforos Kachris		Paper
A Survey on Symbolic Knowledge Distillation of Large Language Models Kamal Acharya, Alvaro Velasquez, Houbing Herbert Song		Paper
Inference Optimization of Foundation Models on AI Accelerators Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis		Paper
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application Chuanpeng Yang, Wang Lu, Yao Zhu, Yidong Wang, Qian Chen, Chenlong Gao, Bingjie Yan, Yiqiang Chen		Paper
Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference Christopher Wolters, Xiaoxuan Yang, Ulf Schlichtmann, Toyotaro Suzumura		Paper