A Review on Edge Large Language Models: Design, Execution, and Applications

Zheng, Yue; Chen, Yuhao; Qian, Bin; Shi, Xiufang; Shu, Yuanchao; Chen, Jiming

doi:10.1145/3719664

Cited by 171IoT and Edge/Fog ComputingAdvanced Neural Network ApplicationsTopic Modeling

A Review on Edge Large Language Models: Design, Execution, and Applications

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Zheng, Yue, Chen, Yuhao, Qian, Bin, Shi, Xiufang, Shu, Yuanchao, Chen, Jiming·2025·ACM Computing Surveys

DOI 10.1145/3719664

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Abstract

Large language models (LLMs) have revolutionized natural language processing with their exceptional understanding, synthesizing, and reasoning capabilities. However, deploying LLMs on resource-constrained edge devices presents significant challenges due to computational limitations, memory constraints, and edge hardware heterogeneity. This survey provides a comprehensive overview of recent advancements in edge LLMs, covering the entire lifecycle—from resource-efficient model design and pre-deployment strategies to runtime inference optimizations. It also explores on-device applications across various domains. By synthesizing state-of-the-art techniques and identifying future research directions, this survey bridges the gap between the immense potential of LLMs and the constraints of edge computing.

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Even 1-bit models (e.g., approximately 15.4 GB for 70B parameters) may exceed the capacity of highly resource-constrained devices.
“Nevertheless, even 1-bit models ( e . g ., approximately 15.4 GB for 70B parameters [7]) may exceed the capacity of highly resource-constrained devices [15].”
From LittleBit: Ultra Low-Bit Quantization via Latent Factorization (2025) · cites this paper as [15]

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Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning
Frantar, E., & Alistarh, D. (2022) · neural information processing systems · doi:10.52202/068431-0323 · Cited by 79 · see citing works
LLM-Pruner: On the Structural Pruning of Large Language Models
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SliceGPT: Compress Large Language Models by Deleting Rows and ColumnsPreprint
Ashkboos, S., Croci, M. L., do Nascimento, M. G., Hoefler, T., & Hensman, J. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.15024 · Cited by 72 · see citing works
Tabi: An Efficient Multi-Level Inference System for Large Language Models
Wang, Y., Chen, K., Tan, H., & Guo, K. (2023) · European Conference on Computer Systems · doi:10.1145/3552326.3587438 · Cited by 71 · see citing works
Fast Inference from Transformers via Speculative Decoding
Leviathan, Y., Kalman, M., & Matias, Y. (2022) · arXiv · doi:10.48550/arxiv.2211.17192 · Cited by 71 · see citing works
A Survey on Model Compression and Acceleration for Pretrained Language Models
Xu, C., & McAuley, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26255 · Cited by 70 · see citing works
X-Former: In-Memory Acceleration of Transformers
Sridharan, S., Stevens, J. R., Roy, K., & Raghunathan, A. (2023) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2023.3282046 · Cited by 69 · see citing works
Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference
Tambe, T., Yang, E.-Y., Wan, Z., Deng, Y., Reddi, V. J., Rush, A. M., Brooks, D. M., & Wei, G.-Y. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218516 · Cited by 66 · see citing works
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly DetectionPreprint
Zhou, Q., Pang, G., Tian, Y., He, S., & Chen, J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.18961 · Cited by 66 · see citing works
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The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
Kurtic, E., Campos, D., Nguyen, T., Frantar, E., Kurtz, M., Fineran, B., Goin, M., & Alistarh, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.279 · Cited by 65 · see citing works
22.9 A 12nm 18.1TFLOPs/W Sparse Transformer Processor with Entropy-Based Early Exit, Mixed-Precision Predication and Fine-Grained Power Management
Tambe, T., Zhang, J. J., Hooper, C. R. C., Jia, T., Whatmough, P. N., Zuckerman, J. D., dos Santos, M. C., Loscalzo, E. J., Giri, D., Shepard, K. L., et al. (2023) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc42615.2023.10067817 · Cited by 64 · see citing works
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
Zhang, M., Chen, H., Shen, C., Yang, Z., Ou, L., Yu, X., & Zhuang, B. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.178 · Cited by 64 · see citing works
Fluctuation-Based Adaptive Structured Pruning for Large Language Models
An, Y., Xu, Z., Yu, C., Tang, M., & Wang, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28960 · Cited by 60 · see citing works
Adapting Language Models to Compress Contexts
Chevalier, A., Wettig, A., Ajith, A., & Chen, D. (2023) · arXiv · Cited by 59 · cited 2× · see citing works
“…Co-Design , which involves co-optimized solutions that leverage speciﬁc hardware features by adapting techniques such as sparsity and quantization to be hardware-friendly; and third, Hardware-Level Optimizations , which highlights innovations in hardware designed to improve the performance of LLMs.”
Adapting Language Models to Compress Contexts
Chevalier, A., Wettig, A., Ajith, A., & Chen, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.232 · Cited by 59 · see citing works
AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference With Transformers
Tuli, S., & Jha, N. K. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2023.3273992 · Cited by 56 · see citing works
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Chan, C.-M., Chen, W., Su, Y., Yu, J., Xue, W., Zhang, S., Fu, J., & Liu, Z. (2023) · arXiv · doi:10.48550/arxiv.2308.07201 · Cited by 55 · cited 2× · see citing works
“On-device LLMs boost enterprise privacy and cost-efﬁciency, improving tasks like message completion, meeting summaries, and computer operations.”
Lion: Adversarial Distillation of Proprietary Large Language Models
Jiang, Y., Chan, C., Chen, M., & Wang, W. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.189 · Cited by 55 · see citing works
EdgeLLM: Fast On-Device LLM Inference With Speculative Decoding
Xu, D., Yin, W., Zhang, H., Jin, X., Zhang, Y., Wei, S., Xu, M., & Liu, X. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3513457 · Cited by 52 · see citing works
Mobile Foundation Model as Firmware
Yuan, J., Yang, C., Cai, D., Wang, S., Yuan, X., Zhang, Z., Li, X., Zhang, D., Mei, H., Jia, X., et al. (2024) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3636534.3649361 · Cited by 51 · see citing works
End-to-End Multitarget Flexible Job Shop Scheduling With Deep Reinforcement Learning
Wang, R., Jing, Y., Gu, C., He, S., & Chen, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3485748 · Cited by 51 · see citing works
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FastBERT: a Self-distilling BERT with Adaptive Inference TimePreprint
Liu, W., Zhou, P., Zhao, Z., Wang, Z., Deng, H., & Ju, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.02178 · Cited by 49 · see citing works
16.1 MuITCIM: A 28nm $2.24 \mu\mathrm{J}$/Token Attention-Token-Bit Hybrid Sparse Digital CIM-Based Accelerator for Multimodal Transformers
Tu, F., Wu, Z., Wang, Y., Wu, W., Liu, L., Hu, Y., Wei, S., & Yin, S. (2023) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc42615.2023.10067842 · Cited by 41 · see citing works
Efficient Large Language Models: A SurveyPreprint
Wan, Z., Wang, X., Che, L., Alam, S., Zheng, Y., Liu, J., Qu, Z., Yan, S., Zhu, Y., Zhang, Q., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.03863 · Cited by 41 · see citing works
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Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
Li, B., Kong, Z., Zhang, T., Li, J., Li, Z., Liu, H., & Ding, C. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.286 · Cited by 39 · see citing works
LeeBERT: Learned Early Exit for BERT with cross-level optimization
Zhu, W. (2021) · doi:10.18653/v1/2021.acl-long.231 · Cited by 38 · see citing works
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
Shen, X., Dong, P., Lu, L., Kong, Z., Li, Z., Lin, M., Wu, C., & Wang, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29860 · Cited by 38 · see citing works
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
Zhu, J., Cai, S., Deng, F. Y., Ooi, B. C., & Wu, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681190 · Cited by 38 · see citing works
Mokey: enabling narrow fixed-point inference for out-of-the-box floating-point transformer models
Zadeh, A. H., Mahmoud, M., Abdelhadi, A., & Moshovos, A. (2022) · Proceedings of the 49th Annual International Symposium on Computer Architecture · doi:10.1145/3470496.3527438 · Cited by 37 · see citing works
Large-Scale Deterministic Networks: Architecture, Enabling Technologies, Case Study, and Future Directions
Tian, W., Gu, C., Guo, M., He, S., Kang, J., Niyato, D. T., & Chen, J. (2024) · IEEE Network · doi:10.1109/mnet.2024.3355116 · Cited by 37 · see citing works
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MELTing Point: Mobile Evaluation of Language Transformers
Laskaridis, S., Katevas, K., Minto, L., & Haddadi, H. (2024) · Proceedings of the 30th Annual International Conference on Mobile Computing and Networking · doi:10.1145/3636534.3690668 · Cited by 37 · see citing works
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language ModelsPreprint
Shao, W., Chen, M., Zhang, Z., Xu, P., Zhao, L., Li, Z., Zhang, K., Gao, P., Qiao, Y., & Luo, P. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.13137 · Cited by 36 · see citing works
Distributed Inference and Fine-tuning of Large Language Models Over The Internet
Borzunov, A., Ryabinin, M., Chumachenko, A., Baranchuk, D., Dettmers, T., Belkada, Y., Samygin, P., & Raffel, C. (2023) · arXiv · doi:10.48550/arxiv.2312.08361 · Cited by 35 · see citing works
DISCO: Distilling Counterfactuals with Large Language Models
Chen, Z., Gao, Q., Bosselut, A., Sabharwal, A., & Richardson, K. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.302 · Cited by 34 · see citing works
Exploring the Feasibility of Remote Cardiac Auscultation Using Earphones
Chen, T., Yang, Y., Fan, X., Guo, X., Xiong, J., & Shangguan, L. (2024) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3636534.3649366 · Cited by 34 · see citing works
Sheared LLaMA: Accelerating Language Model Pre-training via Structured PruningPreprint
Xia, M., Gao, T., Zeng, Z., & Chen, D. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.06694 · Cited by 34 · see citing works
Language Models Meet World Models: Embodied Experiences Enhance Language Models
Xiang, J., Tao, T., Gu, Y., Shu, T., Wang, Z., Yang, Z., & Hu, Z. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-3295 · Cited by 34 · see citing works
A Survey on Automatic Generation of Figurative Language: From Rule-based Systems to Large Language Models
Lai, H., & Nissim, M. (2024) · ACM Computing Surveys · doi:10.1145/3654795 · Cited by 30 · see citing works
When the Edge Meets Transformers: Distributed Inference with Transformer Models
Hu, C., & Li, B. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00017 · Cited by 30 · see citing works
Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding
Bae, S., Ko, J., Song, H., & Yun, S.-Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.362 · Cited by 28 · see citing works
STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining
Guo, L., Choe, W., & Lin, F. X. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3575698 · Cited by 27 · see citing works
GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the EdgePreprint
Padmanabhan, A., Agarwal, N., Iyer, A. P., Ananthanarayanan, G., Shu, Y., Karianakis, N., Xu, G. H., & Netravali, R. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.07705 · Cited by 27 · see citing works
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LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsPreprint
Wu, M., Waheed, A., Zhang, C., Abdul-Mageed, M., & Aji, A. F. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.14402 · Cited by 27 · see citing works
Plug-and-Play: An Efficient Post-training Pruning Method for Large Language ModelsPreprint
Zhang, Y., Bai, H., Lin, H., Zhao, J., Hou, L., & Cannistraci, C. V. (2024) · Preprints.org · doi:10.20944/preprints202310.1487.v2 · Cited by 27 · see citing works
Language model compression with weighted low-rank factorizationPreprint
Hsu, Y.-C., Hua, T., Chang, S., Lou, Q., Shen, Y., & Jin, H. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.00112 · Cited by 24 · see citing works
Less is More: Task-aware Layer-wise Distillation for Language Model CompressionPreprint
Chen, L., Zuo, S., Zhang, Q., He, P., Chen, W., & Zhao, T. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.01351 · Cited by 24 · see citing works
DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
Zhong, Y., Liu, S., Chen, J., Hu, J., Zhu, Y., Liu, X., Jin, X., & Zhang, H. (2024) · arXiv · doi:10.48550/arxiv.2401.09670 · Cited by 23 · see citing works
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GraphWiz: An Instruction-Following Language Model for Graph Computational Problems
Chen, N., Li, Y., Tang, J., & Li, J. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3672010 · Cited by 22 · see citing works
Distilling Reasoning Capabilities into Smaller Language ModelsPreprint
Shridhar, K., Stolfo, A., & Sachan, M. (2022) · arXiv (Cornell University) · doi:10.3929/ethz-b-000653476 · Cited by 21 · see citing works
MCC-KD: Multi-CoT Consistent Knowledge Distillation
Chen, H., Wu, S., Quan, X., Wang, R., Yan, M., & Zhang, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.454 · Cited by 21 · see citing works
Interactive Continual Learning: Fast and Slow Thinking
Qi, B., Chen, X., Gao, J., Dong, L., Liu, J., Wu, L., & Zhou, B. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01224 · Cited by 21 · see citing works
DRONE: Data-aware Low-rank Compression for Large NLP Models
Chen, P. M., Yu, H., Dhillon, I. S., & Hsieh, C. (2021) · neural information processing systems · Cited by 19 · see citing works
SpecTr: Fast Speculative Decoding via Optimal Transport
Sun, Z., Suresh, A. T., Ro, J. H., Beirami, A., Jain, H., & Yu, F. (2023) · neural information processing systems · doi:10.52202/075280-1314 · Cited by 18 · see citing works
GesturePrint: Enabling User Identification for mmWave-Based Gesture Recognition Systems
Xu, L., Wang, K. R., Gu, C., Guo, X., He, S., & Chen, J. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00103 · Cited by 17 · see citing works
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QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language ModelsPreprint
Liu, J., Gong, R., Wei, X., Dong, Z., Cai, J., & Zhuang, B. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.08041 · Cited by 17 · see citing works
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
Gur, I., Furuta, H., Huang, A., Safdari, M., Matsuo, Y., Eck, D., & Faust, A. (2023) · arXiv · doi:10.48550/arxiv.2307.12856 · Cited by 17 · see citing works
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers
Ji, T., Jain, S., Ferdman, M., Milder, P. A., Schwartz, H. A., & Balasubramanian, N. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.363 · Cited by 16 · see citing works
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use CasesPreprint
Liu, Z., Zhao, C., Iandola, F., Lai, C., Tian, Y., Fedorov, I., Xiong, Y., Chang, E., Shi, Y., Krishnamoorthi, R., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.14905 · Cited by 16 · see citing works
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., & Pfister, T. (2023) · arXiv · doi:10.48550/arxiv.2305.02301 · Cited by 16 · see citing works
TaskFusion: An Efficient Transfer Learning Architecture with Dual Delta Sparsity for Multi-Task Natural Language Processing
Fan, Z., Zhang, Q., Abillama, P., Shoouri, S., Lee, C. S. G., Blaauw, D. T., Kim, H.-S., & Sylvester, D. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589040 · Cited by 16 · see citing works
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels
Xu, L., Gu, C., Tan, R., He, S., & Chen, J. (2023) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3625687.3625782 · Cited by 16 · see citing works
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MAF: Exploring Mobile Acoustic Field for Hand-to-Face Gesture Interactions
Yang, Y., Chen, T., Huang, Y., Guo, X., & Shangguan, L. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642437 · Cited by 15 · see citing works
EASTER: Learning to Split Transformers at the Edge Robustly
Guo, X., Jiang, Q., Shen, Y., Pimentel, A. D., & Stefanov, T. P. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3438995 · Cited by 15 · see citing works
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
Li, H., Su, J., Chen, Y., Li, Q., & Zhang, Z.-X. (2023) · neural information processing systems · doi:10.52202/075280-0220 · Cited by 15 · see citing works
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LlamaTouch: A Faithful and Scalable Testbed for Mobile UI Task Automation
Zhang, L. L., Wang, S., Jia, X., Zheng, Z., Yan, Y., Gao, L., Li, Y., & Xu, M. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676382 · Cited by 15 · see citing works
Dialogue Summarization with Mixture of Experts based on Large Language Models
Tian, Y., Xia, F., & Song, Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.385 · Cited by 14 · see citing works
ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Models Inference
Zeng, Z., Hong, Y., Dai, H., Zhuang, H., & Chen, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29922 · Cited by 14 · see citing works
An Autonomous Parallelization of Transformer Model Inference on Heterogeneous Edge Devices
Lee, J., Bahk, I., Kim, H., Jeong, S., Lee, S., & Min, D. (2024) · International Conference on Supercomputing · doi:10.1145/3650200.3656628 · Cited by 13 · see citing works
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse ApproximationPreprint
Li, Y., Yu, Y., Zhang, Q., Liang, C., He, P., Chen, W., & Zhao, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.11222 · Cited by 13 · see citing works
LLM in a flash: Efficient Large Language Model Inference with Limited MemoryPreprint
Alizadeh, K., Mirzadeh, I., Belenko, D., Khatamifard, K., Cho, M., Mundo, C. C. D., Rastegari, M., & Farajtabar, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.11514 · Cited by 13 · see citing works
Matrix Compression via Randomized Low Rank and Low Precision Factorization
Saha, R., Srivastava, V., & Pilancı, M. (2023) · neural information processing systems · doi:10.52202/075280-0826 · Cited by 12 · see citing works
Efficient Methods for Natural Language Processing: A SurveyPreprint
Treviso, M., Lee, J.-U., Ji, T., van Aken, B., Cao, Q., Ciosici, M. R., Hassid, M., Heafield, K., Hooker, S., Raffel, C., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.00099 · Cited by 11 · see citing works
SmartMem: Layout Transformation Elimination and Adaptation for Efficient DNN Execution on Mobile
Niu, W., Sanim, M. M. R., Shu, Z., Guan, J., Shen, X., Yin, M., Agrawal, G., & Ren, B. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620666.3651384 · Cited by 11 · see citing works
HPipe: Large Language Model Pipeline Parallelism for Long Context on Heterogeneous Cost-effective Devices
Ma, R., Yang, X., Wang, J., Qi, Q., Sun, H., Wang, J., Zhuang, Z., & Liao, J. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-industry.1 · Cited by 11 · see citing works
CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation
Ma, X., Zhang, Z., & Zhao, H. Y. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.539 · Cited by 11 · see citing works
Fed-DFA: Federated Distillation for Heterogeneous Model Fusion Through the Adversarial Lens
Wang, Z., Yan, F., Wang, T., Wang, C., Shu, Y., Cheng, P., & Chen, J. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i20.35444 · Cited by 11 · see citing works
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GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference
Zadeh, A. H., Edo, I., Awad, O. M., & Moshovos, A. (2020) · 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro50266.2020.00071 · Cited by 10 · see citing works
Knowledge Fusion of Large Language Models
Wan, F., Huang, X., Cai, D., Quan, X., Bi, W., & Shi, S. (2024) · arXiv · doi:10.48550/arxiv.2401.10491 · Cited by 10 · see citing works
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with SearchPreprint
Kim, G., & Cho, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07003 · Cited by 9 · see citing works
Extreme Compression of Large Language Models via Additive QuantizationPreprint
Egiazarian, V., Panferov, A., Kuznedelev, D., Frantar, E., Babenko, A., & Alistarh, D. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.06118 · Cited by 8 · see citing works
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
Tseng, A., Chee, J., Sun, Q., Kuleshov, V., & De, C. (2024) · PubMed · doi:10.48550/arxiv.2402.04396 · Cited by 8 · see citing works
Boost Transformer-based Language Models with GPU-Friendly Sparsity and Quantization
Yu, C. H., Chen, T., & Gan, Z. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.15 · Cited by 7 · see citing works
LinguaLinked: Distributed Large Language Model Inference on Mobile Devices
Zhao, J., Song, Y., Harris, I. G., & Jyothi, S. A. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-demos.16 · Cited by 7 · see citing works
Petals: Collaborative Inference and Fine-tuning of Large Models
Borzunov, A., Baranchuk, D., Dettmers, T., Ryabinin, M., Belkada, Y., Chumachenko, A., Samygin, P., & Raffel, C. (2022) · arXiv · doi:10.48550/arxiv.2209.01188 · Cited by 6 · see citing works
Propagating Knowledge Updates to LMs Through Distillation
Padmanabhan, S., Onoe, Y., Zhang, M., Durrett, G., & Choi, E. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2041 · Cited by 6 · see citing works
Towards an On-device Agent for Text Rewriting
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Research works printed but not linked: 40

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Self-citation
Efficient Inference for Edge Large Language Models: A Survey
Cai, G., Tian, R., Yang, L., Jia, Y., Li, L., & Wang, J. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010166 · Cited by 19 · see citing works

Authors: Zheng, Y., Chen, Y., Qian, B., Shi, X., Shu, Y., & Chen, J.
Affiliations: Zhejiang University of Technology (CN)
Zhejiang University (CN)
ORCID: Zheng, Yue
Chen, Yuhao
Qian, Bin
Shi, Xiufang
Shu, Yuanchao
Chen, Jiming
Venue: ACM Computing Surveys
Publisher: Association for Computing Machinery (ACM)
Published: 2025-03-23
Volume: 57
Issue: 8
Pages: 1-35
ISSN: 0360-0300 (ISSN-L)
1557-7341
DOI: 10.1145/3719664
OpenAlex: W4407881031
Cited by: 169
References: 171
Citations by year: 2026: 114 · 2025: 55
FWCI: 70.94
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: ACM Computing Surveys (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion, public-domain)
arXiv (Cornell University) (public-domain)
Funders: National Natural Science Foundation of China (92467301, U23A20326, 62293511, and 62372414)
Key Research and Development Program of Zhejiang Province (2025C01061 and 2025C01012)
ZJUCSE-Enflame cloud and edge intelligence joint laboratory
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: edge computing, resource-constrained devices, model compression, inference optimization, on-device inference, model design, heterogeneous hardware, memory constraints

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A Review on Edge Large Language Models: Design, Execution, and Applications

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