mlsanityData to 14 Oct 2026

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

arXiv 2410.11845 ↗First posted 29 Sep 2024cs.DCcited by 100

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Abstract (arXiv)

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.

Key facts. Cited by 100 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Sep 2024.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 42; 2026: 58.

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  1. 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 46
  2. 2025Balanced Token Pruning: Accelerating Vision Language Models Beyond Local Optimizationcited by 20
  3. 2025Xiaomi MiMo-VL-Miloco Technical Reportcited by 15
  4. 2025A Survey on Collaborative Mechanisms Between Large and Small Language Modelscited by 14
  5. 2025Object Detection with Multimodal Large Vision-Language Models: An In-depth Reviewcited by 14
  6. 2025LLM-Oriented Token-Adaptive Knowledge Distillationcited by 13
  7. 2025LittleBit: Ultra Low-Bit Quantization via Latent Factorizationcited by 11
  8. 2025A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesiscited by 9
  9. 2025Small Models, Big Tasks: An Exploratory Empirical Study on Small Language Models for Function Callingcited by 7
  10. 2025SlimCaching: Edge Caching of Mixture-of-Experts for Distributed Inferencecited by 7
  11. 2025AVA: Towards Agentic Video Analytics with Vision Language Modelscited by 5
  12. 2025Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniquescited by 5
38 more
  1. 2026RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysiscited by 5
  2. 2025PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computingcited by 4
  3. 2025Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devicescited by 4
  4. 2025Token Level Routing Inference System for Edge Devicescited by 4
  5. 2025RedAHD: Reduction-Based End-to-End Automatic Heuristic Design with Large Language Modelscited by 4
  6. 2025DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Modelscited by 4
  7. 2025ECVL-ROUTER: Scenario-Aware Routing for Vision-Language Modelscited by 4
  8. 2025T-SAR: A Full-Stack Co-design for CPU-Only Ternary LLM Inference via In-Place SIMD ALU Reorganizationcited by 4
  9. 2026ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialoguecited by 4
  10. 2026Hierarchical Memory Orchestration for Personalized Persistent Agentscited by 4
  11. 2025FastTTS: Accelerating Test-Time Scaling for Edge LLM Reasoningcited by 3
  12. 2026Federate the Router: Learning Language Model Routers with Sparse and Decentralized Evaluationscited by 3
  13. 2026Multi-Agent Home Energy Management Assistantcited by 3
  14. 2026A Pipelined Collaborative Speculative Decoding Framework for Efficient Edge-Cloud LLM Inferencecited by 3
  15. 2026Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agentscited by 3
  16. 2026OrderMoE: An expert similarity driven distributed edge MoE inferencecited by 3
  17. 2025Distributed Retrieval-Augmented Generationcited by 2
  18. 2025P/D-Device: Disaggregated Large Language Model between Cloud and Devicescited by 2
  19. 2025RoboInspector: Unveiling the Unreliability of Policy Code for LLM-enabled Robotic Manipulationcited by 2
  20. 2025Mnemosyne: An Unsupervised, Human-Inspired Long-Term Memory Architecture for Edge-Based LLMscited by 2
  21. 2025Neural Diversity Regularizes Hallucinations in Language Modelscited by 2
  22. 2026FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deploymentcited by 2
  23. 2026Resource Consumption Threats in Large Language Modelscited by 2
  24. 2026DyMoE: Dynamic Expert Orchestration with Mixed-Precision Quantization for Efficient MoE Inference on Edgecited by 2
  25. 2026DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inferencecited by 2
  26. 2026EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillationcited by 2
  27. 2025Biases in Edge Language Models: Detection, Analysis, and Mitigationcited by 1
  28. 2025AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Resultscited by 1
  29. 2025Mobile-MMLU: A Mobile Intelligence Language Understanding Benchmarkcited by 1
  30. 2025Speculate Deep and Accurate: Lossless and Training-Free Acceleration for Offloaded LLMs via Substitute Speculative Decodingcited by 1
  31. 2025Cerberus: Real-Time Video Anomaly Detection via Cascaded Vision-Language Modelscited by 1
  32. 2025Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networkscited by 1
  33. 2025Post-Training Quantization of OpenPangu Models for Efficient Deployment on Atlas A2cited by 1
  34. 2026Improving User Privacy in Personalized Generation: Client-Side Retrieval-Augmented Modification of Server-Side Generated Speculationscited by 1
  35. 2026Understanding Users' Privacy Reasoning and Behaviors During Chatbot Use to Support Meaningful Agency in Privacycited by 1
  36. 2026SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inferencecited by 1
  37. 2026Agentic Peer-to-Peer Networks: From Content Distribution to Capability and Action Sharingcited by 1
  38. 2026Multilinguality at the Edge: Developing Language Models for the Global Southcited by 1

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