Awesome Efficient LLM

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 / Serving
• Tuning
• Efficient Training
• Survey or Benchmark

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

Updates

• May 29, 2024: We've had this awesome list for a year now ?!
• Sep 6, 2023: Add a new subdirectory project/ to organize efficient LLM projects.
• July 11, 2023: A new subdirectory efficient_plm/ is created to house papers that are applicable to PLMs.

? 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 a lot of GitHub stars or citations.

Paper from Sep 2, 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 / Serving
• 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
Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking Marco Federici, Davide Belli, Mart van Baalen, Amir Jalalirad, Andrii Skliar, Bence Major, Markus Nagel, Paul Whatmough		Paper
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs Akhiad Bercovich, Tomer Ronen, Talor Abramovich, Nir Ailon, Nave Assaf, Mohammad Dabbah et al		Paper
Reassessing Layer Pruning in LLMs: New Insights and Methods Yao Lu, Hao Cheng, Yujie Fang, Zeyu Wang, Jiaheng Wei, Dongwei Xu, Qi Xuan, Xiaoniu Yang, Zhaowei Zhu		Github Paper
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Zichen Song, Sitan Huang, Yuxin Wu, Zhongfeng Kang		Paper
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Yonggan Fu, Zhongzhi Yu, Junwei Li, Jiayi Qian, Yongan Zhang, Xiangchi Yuan, Dachuan Shi, Roman Yakunin, Yingyan Celine Lin		Github Paper
Scaling Law for Post-training after Model Pruning Xiaodong Chen, Yuxuan Hu, Jing Zhang, Xiaokang Zhang, Cuiping Li, Hong Chen		Paper
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization Hexuan Deng, Wenxiang Jiao, Xuebo Liu, Min Zhang, Zhaopeng Tu		Github Paper
Sparsing Law: Towards Large Language Models with Greater Activation Sparsity Yuqi Luo, Chenyang Song, Xu Han, Yingfa Chen, Chaojun Xiao, Zhiyuan Liu, Maosong Sun		Github Paper
AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis Zichen Song, Yuxin Wu, Sitan Huang, Zhongfeng Kang		Paper
Tailored-LLaMA: Optimizing Few-Shot Learning in Pruned LLaMA Models with Task-Specific Prompts Danyal Aftab, Steven Davy		Paper
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment Ge Yang, Changyi He, Jinyang Guo, Jianyu Wu, Yifu Ding, Aishan Liu, Haotong Qin, Pengliang Ji, Xianglong Liu		Github Paper
Beyond 2:4: exploring V:N:M sparsity for efficient transformer inference on GPUs Kang Zhao, Tao Yuan, Han Bao, Zhenfeng Su, Chang Gao, Zhaofeng Sun, Zichen Liang, Liping Jing, Jianfei Chen		Paper
EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh		Github Paper
FedSpaLLM: Federated Pruning of Large Language Models Guangji Bai, Yijiang Li, Zilinghan Li, Liang Zhao, Kibaek Kim		Paper
Pruning Foundation Models for High Accuracy without Retraining Pu Zhao, Fei Sun, Xuan Shen, Pinrui Yu, Zhenglun Kong, Yanzhi Wang, Xue Lin		Github Paper
Self-calibration for Language Model Quantization and Pruning Miles Williams, George Chrysostomou, Nikolaos Aletras		Paper
Beware of Calibration Data for Pruning Large Language Models Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang		Paper
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W. Mahoney, Yaoqing Yang		Github Paper
Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix Yingyu Liang, Jiangxuan Long, Zhenmei Shi, Zhao Song, Yufa Zhou		Paper
DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models Shangqian Gao, Chi-Heng Lin, Ting Hua, Tang Zheng, Yilin Shen, Hongxia Jin, Yen-Chang Hsu		Paper
Self-Data Distillation for Recovering Quality in Pruned Large Language Models Vithursan Thangarasa, Ganesh Venkatesh, Nish Sinnadurai, Sean Lie		Paper
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models David Hoffmann, Kailash Budhathoki, Matthaeus Kleindessner		Paper
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning Abhinav Bandari, Lu Yin, Cheng-Yu Hsieh, Ajay Kumar Jaiswal, Tianlong Chen, Li Shen, Ranjay Krishna, Shiwei Liu		Github Paper
Mitigating Copy Bias in In-Context Learning through Neuron Pruning Ameen Ali, Lior Wolf, Ivan Titov		Paper
SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models Juan Pablo Munoz, Jinjie Yuan, Nilesh Jain		Github Paper
MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models Gongfan Fang, Hongxu Yin, Saurav Muralidharan, Greg Heinrich, Jeff Pool, Jan Kautz, Pavlo Molchanov, Xinchao Wang		Github Paper
Search for Efficient Large Language Models Xuan Shen, Pu Zhao, Yifan Gong, Zhenglun Kong, Zheng Zhan, Yushu Wu, Ming Lin, Chao Wu, Xue Lin, Yanzhi Wang		Paper
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information Yuxin Wang, Minghua Ma, Zekun Wang, Jingchang Chen, Huiming Fan, Liping Shan, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin		Github Paper
OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition Stephen Zhang, Vardan Papyan		Paper
KVPruner: Structural Pruning for Faster and Memory-Efficient Large Language Models Bo Lv, Quan Zhou, Xuanang Ding, Yan Wang, Zeming Ma		Paper
Evaluating the Impact of Compression Techniques on Task-Specific Performance of Large Language Models Bishwash Khanal, Jeffery M. Capone		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

Knowledge Distillation

Title & Authors	Introduction	Links
Knowledge Distillation of Large Language Models Yuxian Gu, Li Dong, Furu Wei, Minlie Huang		Github Paper
Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation Xunyu Zhu, Jian Li, Can Ma, Weiping Wang		Paper
Generative Context Distillation Haebin Shin, Lei Ji, Yeyun Gong, Sungdong Kim, Eunbi Choi, Minjoon Seo		Github Paper
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models Jahyun Koo, Yerin Hwang, Yongil Kim, Taegwan Kang, Hyunkyung Bae, Kyomin Jung		Paper
Beyond Autoregression: Fast LLMs via Self-Distillation Through Time Justin Deschenaux, Caglar Gulcehre		Github Paper
Pre-training Distillation for Large Language Models: A Design Space Exploration Hao Peng, Xin Lv, Yushi Bai, Zijun Yao, Jiajie Zhang, Lei Hou, Juanzi Li		Paper
MiniPLM: Knowledge Distillation for Pre-Training Language Models Yuxian Gu, Hao Zhou, Fandong Meng, Jie Zhou, Minlie Huang		Github Paper
Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling Wenda Xu, Rujun Han, Zifeng Wang, Long T. Le, Dhruv Madeka, Lei Li, William Yang Wang, Rishabh Agarwal, Chen-Yu Lee, Tomas Pfister		Paper
Evolutionary Contrastive Distillation for Language Model Alignment Julian Katz-Samuels, Zheng Li, Hyokun Yun, Priyanka Nigam, Yi Xu, Vaclav Petricek, Bing Yin, Trishul Chilimbi		Paper
BabyLlama-2: Ensemble-Distilled Models Consistently Outperform Teachers With Limited Data Jean-Loup Tastet, Inar Timiryasov		Paper
EchoAtt: Attend, Copy, then Adjust for More Efficient Large Language Models Hossein Rajabzadeh, Aref Jafari, Aman Sharma, Benyamin Jami, Hyock Ju Kwon, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh		Paper
SKIntern: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models Huanxuan Liao, Shizhu He, Yupu Hao, Xiang Li, Yuanzhe Zhang, Kang Liu, Jun Zhao		Github Paper
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward Dongheng Li, Yongchang Hao, Lili Mou		Github Paper
Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models Jun Rao, Xuebo Liu, Zepeng Lin, Liang Ding, Jing Li, Dacheng Tao		Paper
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights Mohamad Ballout, Ulf Krumnack, Gunther Heidemann, Kai-Uwe Kühnberger		Paper
The Mamba in the Llama: Distilling and Accelerating Hybrid Models Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, Tri Dao		Github 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
SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization Runsheng Bai, Qiang Liu, Bo Liu		Paper
CPTQuant -- A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models Amitash Nanda, Sree Bhargavi Balija, Debashis Sahoo		Paper
Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format Chao Fang, Man Shi, Robin Geens, Arne Symons, Zhongfeng Wang, Marian Verhelst		Paper
MixPE: Quantization and Hardware Co-design for Efficient LLM Inference Yu Zhang, Mingzi Wang, Lancheng Zou, Wulong Liu, Hui-Ling Zhen, Mingxuan Yuan, Bei Yu		Paper
BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Yuzong Chen, Ahmed F. AbouElhamayed, Xilai Dai, Yang Wang, Marta Andronic, George A. Constantinides, Mohamed S. Abdelfattah		Github Paper
AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference Janghwan Lee, Jiwoong Park, Jinseok Kim, Yongjik Kim, Jungju Oh, Jinwook Oh, Jungwook Choi		Paper
Bi-Mamba: Towards Accurate 1-Bit State Space Models Shengkun Tang, Liqun Ma, Haonan Li, Mingjie Sun, Zhiqiang Shen		Paper
"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization Eldar Kurtic, Alexandre Marques, Shubhra Pandit, Mark Kurtz, Dan Alistarh		Paper
GWQ: Gradient-Aware Weight Quantization for Large Language Models Yihua Shao, Siyu Liang, Xiaolin Lin, Zijian Ling, Zixian Zhu et al		Paper
A Comprehensive Study on Quantization Techniques for Large Language Models Jiedong Lang, Zhehao Guo, Shuyu Huang		Paper
BitNet a4.8: 4-bit Activations for 1-bit LLMs Hongyu Wang, Shuming Ma, Furu Wei		Paper
TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction Yuhang Li, Priyadarshini Panda		Github Paper
BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments Xinghao Wang, Pengyu Wang, Bo Wang, Dong Zhang, Yunhua Zhou, Xipeng Qiu		Github Paper
The Impact of Inference Acceleration Strategies on Bias of LLMs Elisabeth Kirsten, Ivan Habernal, Vedant Nanda, Muhammad Bilal Zafar		Paper
Understanding the difficulty of low-precision post-training quantization of large language models Zifei Xu, Sayeh Sharify, Wanzin Yazar, Tristan Webb, Xin Wang		Paper
1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs Jinheng Wang, Hansong Zhou, Ting Song, Shaoguang Mao, Shuming Ma, Hongyu Wang, Yan Xia, Furu Wei		Github Paper
QuAILoRA: Quantization-Aware Initialization for LoRA Neal Lawton, Aishwarya Padmakumar, Judith Gaspers, Jack FitzGerald, Anoop Kumar, Greg Ver Steeg, Aram Galstyan		Paper
Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language Benchmarks Enkhbold Nyamsuren		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
Pyramid Vector Quantization for LLMs Tycho F. A. van der Ouderaa, Maximilian L. Croci, Agrin Hilmkil, James Hensman		Paper
SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators Rasoul Shafipour, David Harrison, Maxwell Horton, Jeffrey Marker, Houman Bedayat, Sachin Mehta, Mohammad Rastegari, Mahyar Najibi, Saman Naderiparizi		Paper
FlatQuant: Flatness Matters for LLM Quantization Yuxuan Sun, Ruikang Liu, Haoli Bai, Han Bao, Kang Zhao, Yuening Li, Jiaxin Hu, Xianzhi Yu, Lu Hou, Chun Yuan, Xin Jiang, Wulong Liu, Jun Yao		Github Paper
SLiM: One-shot Quantized Sparse Plus Low-rank Approximation of LLMs Mohammad Mozaffari, Maryam Mehri Dehnavi		Github Paper
Scaling laws for post-training quantized large language models Zifei Xu, Alexander Lan, Wanzin Yazar, Tristan Webb, Sayeh Sharify, Xin Wang		Paper
Continuous Approximations for Improving Quantization Aware Training of LLMs He Li, Jianhang Hong, Yuanzhuo Wu, Snehal Adbol, Zonglin Li		Paper
DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs Yingsong Luo, Ling Chen		Github Paper
Quamba: A Post-Training Quantization Recipe for Selective State Space Models Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin, Kai-Chiang Wu, Diana Marculescu		Github Paper
AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations Qian Tao, Wenyuan Yu, Jingren Zhou		Paper
Channel-Wise Mixed-Precision Quantization for Large Language Models Zihan Chen, Bike Xie, Jundong Li, Cong Shen		Paper
Progressive Mixed-Precision Decoding for Efficient LLM Inference Hao Mark Chen, Fuwen Tan, Alexandros Kouris, Royson Lee, Hongxiang Fan, Stylianos I. Venieris		Paper
EXAQ: Exponent Aware Quantization For LLMs Acceleration Moran Shkolnik, Maxim Fishman, Brian Chmiel, Hilla Ben-Yaacov, Ron Banner, Kfir Yehuda Levy		Github Paper
PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs Mengzhao Chen, Yi Liu, Jiahao Wang, Yi Bin, Wenqi Shao, Ping Luo		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
Scaling Laws for Mixed quantization in Large Language Models Zeyu Cao, Cheng Zhang, Pedro Gimenes, Jianqiao Lu, Jianyi Cheng, Yiren Zhao		Paper
PalmBench: A Comprehensive Benchmark of Compressed Large Language Models on Mobile Platforms Yilong Li, Jingyu Liu, Hao Zhang, M Badri Narayanan, Utkarsh Sharma, Shuai Zhang, Pan Hu, Yijing Zeng, Jayaram Raghuram, Suman Banerjee		Paper
CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression Wenyuan Liu, Xindian Ma, Peng Zhang, Yan Wang		Paper
SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration Jintao Zhang, Jia wei, Pengle Zhang, Jun Zhu, Jianfei Chen		Paper
Addition is All You Need for Energy-efficient Language Models Hongyin Luo, Wei Sun		Paper
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models Yifei Liu, Jicheng Wen, Yang Wang, Shengyu Ye, Li Lyna Zhang, Ting Cao, Cheng Li, Mao Yang		Github Paper
INT-FlashAttention: Enabling Flash Attention for INT8 Quantization Shimao Chen, Zirui Liu, Zhiying Wu, Ce Zheng, Peizhuang Cong, Zihan Jiang, Yuhan Wu, Lei Su, Tong Yang		Github Paper
Accumulator-Aware Post-Training Quantization Ian Colbert, Fabian Grob, Giuseppe Franco, Jinjie Zhang, Rayan Saab		Paper
DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs Haokun Lin, Haobo Xu, Yichen Wu, Jingzhi Cui, Yingtao Zhang, Linzhan Mou, Linqi Song, Zhenan Sun, Ying Wei		Github Paper
A Comprehensive Evaluation of Quantized Instruction-Tuned Large Language Models: An Experimental Analysis up to 405B Jemin Lee, Sihyeong Park, Jinse Kwon, Jihun Oh, Yongin Kwon		Paper
The Uniqueness of LLaMA3-70B with Per-Channel Quantization: An Empirical Study Minghai Qin		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
Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration Zhuofan Wen, Shangtong Gui, Yang Feng		Paper
PLD+: Accelerating LLM inference by leveraging Language Model Artifacts Shwetha Somasundaram, Anirudh Phukan, Apoorv Saxena		Paper
FastDraft: How to Train Your Draft Ofir Zafrir, Igor Margulis, Dorin Shteyman, Guy Boudoukh		Paper
SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents Dawei Li, Zhen Tan, Peijia Qian, Yifan Li, Kumar Satvik Chaudhary, Lijie Hu, Jiayi Shen		Github Paper
The N-Grammys: Accelerating Autoregressive Inference with Learning-Free Batched Speculation Lawrence Stewart, Matthew Trager, Sujan Kumar Gonugondla, Stefano Soatto		Paper
Accelerated AI Inference via Dynamic Execution Methods Haim Barad, Jascha Achterberg, Tien Pei Chou, Jean Yu		Paper
SuffixDecoding: A Model-Free Approach to Speeding Up Large Language Model Inference Gabriele Oliaro, Zhihao Jia, Daniel Campos, Aurick Qiao		Paper
Dynamic Strategy Planning for Efficient Question Answering with Large Language Models Tanmay Parekh, Pradyot Prakash, Alexander Radovic, Akshay Shekher, Denis Savenkov		Paper
MagicPIG: LSH Sampling for Efficient LLM Generation Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, Yang Zhou, Jianyu Zhang, Niklas Nolte, Yuandong Tian, Matthijs Douze, Leon Bottou, Zhihao Jia, Beidi Chen		Github Paper
Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition Artem Basharin, Andrei Chertkov, Ivan Oseledets		Paper
Efficient Inference for Augmented Large Language Models Rana Shahout, Cong Liang, Shiji Xin, Qianru Lao, Yong Cui, Minlan Yu, Michael Mitzenmacher		Paper
Dynamic Vocabulary Pruning in Early-Exit LLMs Jort Vincenti, Karim Abdel Sadek, Joan Velja, Matteo Nulli, Metod Jazbec		Github Paper
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation Qinsi Wang, Saeed Vahidian, Hancheng Ye, Jianyang Gu, Jianyi Zhang, Yiran Chen		Github Paper
DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, Junxian Guo, Shang Yang, Haotian Tang, Yao Fu, Song Han		Github Paper
DySpec: Faster Speculative Decoding with Dynamic Token Tree Structure Yunfan Xiong, Ruoyu Zhang, Yanzeng Li, Tianhao Wu, Lei Zou		Paper
QSpec: Speculative Decoding with Complementary Quantization Schemes Juntao Zhao, Wenhao Lu, Sheng Wang, Lingpeng Kong, Chuan Wu		Paper
TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention Lijie Yang, Zhihao Zhang, Zhuofu Chen, Zikun Li, Zhihao Jia		Paper
ParallelSpec: Parallel Drafter for Efficient Speculative Decoding Zilin Xiao, Hongming Zhang, Tao Ge, Siru Ouyang, Vicente Ordonez, Dong Yu		Paper
SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration Heming Xia, Yongqi Li, Jun Zhang, Cunxiao Du, Wenjie Li		Github Paper
TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text Songshuo Lu, Hua Wang, Yutian Rong, Zhi Chen, Yaohua Tang		Github Paper
A Little Goes a Long Way: Efficient Long Context Training and Inference with Partial Contexts Suyu Ge, Xihui Lin, Yunan Zhang, Jiawei Han, Hao Peng		Paper
Mnemosyne: Parallelization Strategies for Efficiently Serving Multi-Million Context Length LLM Inference Requests Without Approximations Amey Agrawal, Junda Chen, Íñigo Goiri, Ramachandran Ramjee, Chaojie Zhang, Alexey Tumanov, Esha Choukse		Paper
Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction Zhenmei Shi, Yifei Ming, Xuan-Phi Nguyen, Yingyu Liang, Shafiq Joty		Github Paper
Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference Zongyue Qin, Zifan He, Neha Prakriya, Jason Cong, Yizhou Sun		Paper
CritiPrefill: A Segment-wise Criticality-based Approach for Prefilling Acceleration in LLMs Junlin Lv, Yuan Feng, Xike Xie, Xin Jia, Qirong Peng, Guiming Xie		Github Paper
RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval Di Liu, Meng Chen, Baotong Lu, Huiqiang Jiang, Zhenhua Han, Qianxi Zhang, Qi Chen, Chengruidong Zhang, Bailu Ding, Kai Zhang, Chen Chen, Fan Yang, Yuqing Yang, Lili Qiu		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

Efficient MOE

Title & Authors	Introduction	Links
Fast Inference of Mixture-of-Experts Language Models with Offloading Artyom Eliseev, Denis Mazur		Github Paper
Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning Mingyu Cao, Gen Li, Jie Ji, Jiaqi Zhang, Xiaolong Ma, Shiwei Liu, Lu Yin		Github Paper
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference Andrii Skliar, Ties van Rozendaal, Romain Lepert, Todor Boinovski, Mart van Baalen, Markus Nagel, Paul Whatmough, Babak Ehteshami Bejnordi		Paper
MoNTA: Accelerating Mixture-of-Experts Training with Network-Traffc-Aware Parallel Optimization Jingming Guo, Yan Liu, Yu Meng, Zhiwei Tao, Banglan Liu, Gang Chen, Xiang Li		Github Paper
MoE-I2: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition Cheng Yang, Yang Sui, Jinqi Xiao, Lingyi Huang, Yu Gong, Yuanlin Duan, Wenqi Jia, Miao Yin, Yu Cheng, Bo Yuan		Github Paper
HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference Peng Tang, Jiacheng Liu, Xiaofeng Hou, Yifei Pu, Jing Wang, Pheng-Ann Heng, Chao Li, Minyi Guo		Paper
ProMoE: Fast MoE-based LLM Serving using Proactive Caching Xiaoniu Song, Zihang Zhong, Rong Chen		Paper
ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference Xin He, Shunkang Zhang, Yuxin Wang, Haiyan Yin, Zihao Zeng, Shaohuai Shi, Zhenheng Tang, Xiaowen Chu, Ivor Tsang, Ong Yew Soon		Paper
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference Yulei Qian, Fengcun Li, Xiangyang Ji, Xiaoyu Zhao, Jianchao Tan, Kefeng Zhang, Xunliang Cai		Paper
MC-MoE: Mixture Compressor for Mixture-of-Experts LLMs Gains More Wei Huang, Yue Liao, Jianhui Liu, Ruifei He, Haoru Tan, Shiming Zhang, Hongsheng Li, Si Liu, Xiaojuan Qi		Github 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
Taipan: Efficient and Expressive State Space Language Models with Selective Attention Chien Van Nguyen, Huy Huu Nguyen, Thang M. Pham, Ruiyi Zhang, Hanieh Deilamsalehy, Puneet Mathur, Ryan A. Rossi, Trung Bui, Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen		Paper
SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs Yizhao Gao, Zhichen Zeng, Dayou Du, Shijie Cao, Hayden Kwok-Hay So, Ting Cao, Fan Yang, Mao Yang		Github Paper
Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression Jingcun Wang, Yu-Guang Chen, Ing-Chao Lin, Bing Li, Grace Li Zhang		Github Paper
Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions Zhihao He, Hang Yu, Zi Gong, Shizhan Liu, Jianguo Li, Weiyao Lin		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
ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression Guangda Liu, Chengwei Li, Jieru Zhao, Chenqi Zhang, Minyi Guo		Paper
Unifying KV Cache Compression for Large Language Models with LeanKV Yanqi Zhang, Yuwei Hu, Runyuan Zhao, John C.S. Lui, Haibo Chen		Paper
Compressing KV Cache for Long-Context LLM Inference with Inter-Layer Attention Similarity Da Ma, Lu Chen, Situo Zhang, Yuxun Miao, Su Zhu, Zhi Chen, Hongshen Xu, Hanqi Li, Shuai Fan, Lei Pan, Kai Yu		Paper
MiniKV: Pushing the Limits of LLM Inference via 2-Bit Layer-Discriminative KV Cache Akshat Sharma, Hangliang Ding, Jianping Li, Neel Dani, Minjia Zhang		Paper
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection Wei Wu, Zhuoshi Pan, Chao Wang, Liyi Chen, Yunchu Bai, Kun Fu, Zheng Wang, Hui Xiong		Paper
Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning Yu Fu, Zefan Cai, Abedelkadir Asi, Wayne Xiong, Yue Dong, Wen Xiao		Github Paper
BUZZ: Beehive-structured Sparse KV Cache with Segmented Heavy Hitters for Efficient LLM Inference Junqi Zhao, Zhijin Fang, Shu Li, Shaohui Yang, Shichao He		Github Paper
A Systematic Study of Cross-Layer KV Sharing for Efficient LLM Inference You Wu, Haoyi Wu, Kewei Tu		Github Paper
Lossless KV Cache Compression to 2% Zhen Yang, J.N.Han, Kan Wu, Ruobing Xie, An Wang, Xingwu Sun, Zhanhui Kang		Paper
MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection Bokai Lin, Zihao Zeng, Zipeng Xiao, Siqi Kou, Tianqi Hou, Xiaofeng Gao, Hao Zhang, Zhijie Deng		Paper
Residual vector quantization for KV cache compression in large language model Ankur Kumar		Github Paper
KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing Yifei Yang, Zouying Cao, Qiguang Chen, Libo Qin, Dongjie Yang, Hai Zhao, Zhi Chen		Github Paper
LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy Rongzhi Zhang, Kuang Wang, Liyuan Liu, Shuohang Wang, Hao Cheng, Chao Zhang, Yelong Shen		Paper
SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation Aurick Qiao, Zhewei Yao, Samyam Rajbhandari, Yuxiong He		Paper
Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference Piotr Nawrot, Adrian Łańcucki, Marcin Chochowski, David Tarjan, Edoardo M. Ponti		Paper
KV-Compress: Paged KV-Cache Compression with Variable Compression Rates per Attention Head Isaac Rehg		Paper
Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference Yuan Feng, Junlin Lv, Yukun Cao, Xike Xie, S. Kevin Zhou		Github Paper
AlignedKV: Reducing Memory Access of KV-Cache with Precision-Aligned Quantization Yifan Tan, Haoze Wang, Chao Yan, Yangdong Deng		Github Paper
CSKV: Training-Efficient Channel Shrinking for KV Cache in Long-Context Scenarios Luning Wang, Shiyao Li, Xuefei Ning, Zhihang Yuan, Shengen Yan, Guohao Dai, Yu Wang		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

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
JPPO: Joint Power and Prompt Optimization for Accelerated Large Language Model Services Feiran You, Hongyang Du, Kaibin Huang, Abbas Jamalipour		Paper
Generative Context Distillation Haebin Shin, Lei Ji, Yeyun Gong, Sungdong Kim, Eunbi Choi, Minjoon Seo		Github Paper
MultiTok: Variable-Length Tokenization for Efficient LLMs Adapted from LZW Compression Noel Elias, Homa Esfahanizadeh, Kaan Kale, Sriram Vishwanath, Muriel Medard		Github Paper
Selection-p: Self-Supervised Task-Agnostic Prompt Compression for Faithfulness and Transferability Tsz Ting Chung, Leyang Cui, Lemao Liu, Xinting Huang, Shuming Shi, Dit-Yan Yeung		Paper
From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression Eunseong Choi, Sunkyung Lee, Minjin Choi, June Park, Jongwuk Lee		Paper
Perception Compressor:A training-free prompt compression method in long context scenarios Jiwei Tang, Jin Xu, Tingwei Lu, Hai Lin, Yiming Zhao, Hai-Tao Zheng		Paper
FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text Compression Fazal Mittu, Yihuan Bu, Akshat Gupta, Ashok Devireddy, Alp Eren Ozdarendeli, Anant Singh, Gopala Anumanchipalli		Github Paper
Parse Trees Guided LLM Prompt Compression Wenhao Mao, Chengbin Hou, Tianyu Zhang, Xinyu Lin, Ke Tang, Hairong Lv		Github Paper
AlphaZip: Neural Network-Enhanced Lossless Text Compression Swathi Shree Narashiman, Nitin Chandrachoodan		Github Paper
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning Shivam Shandilya, Menglin Xia, Supriyo Ghosh, Huiqiang Jiang, Jue Zhang, Qianhui Wu, Victor Rühle		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

Low-Rank Decomposition

Title & Authors	Introduction	Links
Natural GaLore: Accelerating GaLore for memory-efficient LLM Training and Fine-tuning Arijit Das		Github Paper
CompAct: Compressed Activations for Memory-Efficient LLM Training Yara Shamshoum, Nitzan Hodos, Yuval Sieradzki, Assaf Schuster		Paper
ESPACE: Dimensionality Reduction of Activations for Model Compression Charbel Sakr, Brucek Khailany		Paper

Hardware/System/Serving

Title & Authors	Introduction	Links
FastSwitch: Optimizing Context Switching Efficiency in Fairness-aware Large Language Model Serving Ao Shen, Zhiyao Li, Mingyu Gao		Paper
CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration Hongpeng Jin, Yanzhao Wu		Paper
Ripple: Accelerating LLM Inference on Smartphones with Correlation-Aware Neuron Management Tuowei Wang, Ruwen Fan, Minxing Huang, Zixu Hao, Kun Li, Ting Cao, Youyou Lu, Yaoxue Zhang, Ju Ren		Paper
ALISE: Accelerating Large Language Model Serving with Speculative Scheduling Youpeng Zhao, Jun Wang		Paper
EPIC: Efficient Position-Independent Context Caching for Serving Large Language Models Junhao Hu, Wenrui Huang, Haoyi Wang, Weidong Wang, Tiancheng Hu, Qin Zhang, Hao Feng, Xusheng Chen, Yizhou Shan, Tao Xie		Paper
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training Jinda Jia, Cong Xie, Hanlin Lu, Daoce Wang, Hao Feng, Chengming Zhang, Baixi Sun, Haibin Lin, Zhi Zhang, Xin Liu, Dingwen Tao		Paper
FastAttention: Extend FlashAttention2 to NPUs and Low-resource GPUs Haoran Lin, Xianzhi Yu, Kang Zhao, Lu Hou, Zongyuan Zhan et al		Paper
POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference Aditya K Kamath, Ramya Prabhu, Jayashree Mohan, Simon Peter, Ramachandran Ramjee, Ashish Panwar		Paper
TPI-LLM: Serving 70B-scale LLMs Efficiently on Low-resource Edge Devices Zonghang Li, Wenjiao Feng, Mohsen Guizani, Hongfang Yu		Github Paper
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores Shaobo Ma, Chao Fang, Haikuo Shao, Zhongfeng Wang		Paper
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

Tuning

Title & Authors	Introduction	Links
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization Huaqin Zhao, Jiaxi Li, Yi Pan, Shizhe Liang, Xiaofeng Yang, Wei Liu, Xiang Li, Fei Dou, Tianming Liu, Jin Lu		Paper
Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation Ayan Sengupta, Vaibhav Seth, Arinjay Pathak, Natraj Raman, Sriram Gopalakrishnan, Tanmoy Chakraborty		Github Paper
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning Jingfan Zhang, Yi Zhao, Dan Chen, Xing Tian, Huanran Zheng, Wei Zhu		Paper
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates Md Kowsher, Tara Esmaeilbeig, Chun-Nam Yu, Mojtaba Soltanalian, Niloofar Yousefi		Github Paper
Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models Kai Yao, Penlei Gao, Lichun Li, Yuan Zhao, Xiaofeng Wang, Wei Wang, Jianke Zhu		Github Paper
Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning Nusrat Jahan Prottasha, Asif Mahmud, Md. Shohanur Islam Sobuj, Prakash Bhat, Md Kowsher, Niloofar Yousefi, Ozlem Ozmen Garibay		Paper
QEFT: Quantization for Efficient Fine-Tuning of LLMs Changhun Lee, Jun-gyu Jin, Younghyun Cho, Eunhyeok Park		Github Paper
BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models Aofei Chang, Jiaqi Wang, Han Liu, Parminder Bhatia, Cao Xiao, Ting Wang, Fenglong Ma		Github Paper
SparseGrad: A Selective Method for Efficient Fine-tuning of MLP Layers Viktoriia Chekalina, Anna Rudenko, Gleb Mezentsev, Alexander Mikhalev, Alexander Panchenko, Ivan Oseledets		Github Paper
SpaLLM: Unified Compressive Adaptation of Large Language Models with Sketching Tianyi Zhang, Junda Su, Oscar Wu, Zhaozhuo Xu, Anshumali Shrivastava		Paper
Bone: Block Affine Transformation as Parameter Efficient Fine-tuning Methods for Large Language Models Jiale Kang		Github Paper
Enabling Resource-Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines Lei Gao, Amir Ziashahabi, Yue Niu, Salman Avestimehr, Murali Annavaram		Paper

Efficient Training

Title & Authors	Introduction	Links
AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning Changhai Zhou, Shiyang Zhang, Yuhua Zhou, Zekai Liu, Shichao Weng		Paper
Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention Xingtai Lv, Ning Ding, Kaiyan Zhang, Ermo Hua, Ganqu Cui, Bowen Zhou		Github Paper
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs Yifei Zhang, Hao Zhu, Aiwei Liu, Han Yu, Piotr Koniusz, Irwin King		Paper
COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training Haocheng Xi, Han Cai, Ligeng Zhu, Yao Lu, Kurt Keutzer, Jianfei Chen, Song Han		Github Paper
BitPipe: Bidirectional Interleaved Pipeline Parallelism for Accelerating Large Models Training Houming Wu, Ling Chen, Wenjie Yu		Github Paper

Survey (or Benchmark)

Title & Authors	Introduction	Links
Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding Hyun Ryu, Eric Kim		Paper
LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale, Farah Ferdaus et al		Github Paper
Prompt Compression for Large Language Models: A Survey Zongqian Li, Yinhong Liu, Yixuan Su, Nigel Collier		Github Paper
Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective Jinhao Li, Jiaming Xu, Shan Huang, Yonghua Chen, Wen Li, Jun Liu, Yaoxiu Lian, Jiayi Pan, Li Ding, Hao Zhou, Guohao Dai		Paper
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms Ruihao Gong, Yifu Ding, Zining Wang, Chengtao Lv, Xingyu Zheng, Jinyang Du, Haotong Qin, Jinyang Guo, Michele Magno, Xianglong Liu		Paper
Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey Sourav Verma		Github Paper
Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview Yanshu Wang, Tong Yang, Xiyan Liang, Guoan Wang, Hanning Lu, Xu Zhe, Yaoming Li, Li Weitao		Paper
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