# EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting

arXiv: https://arxiv.org/abs/2406.15758 · HTML: https://mlsanity.com/paper/2406.15758/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 22 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot](https://mlsanity.com/paper/2301.00774/) (2023)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU](https://mlsanity.com/paper/2303.06865/) (2023)
- [LLM-QAT: Data-Free Quantization Aware Training for Large Language Models](https://mlsanity.com/paper/2305.17888/) (2023)
- [LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning](https://mlsanity.com/paper/2206.06522/) (2022)
- [Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization](https://mlsanity.com/paper/2305.14152/) (2023)
- [Understanding Softmax Confidence and Uncertainty](https://mlsanity.com/paper/2106.04972/) (2021)
- [Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning](https://mlsanity.com/paper/2306.15686/) (2023)
- [2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency](https://mlsanity.com/paper/2109.05223/) (2021)

## Built on by (most cited first, 27 of 27)

- [Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency](https://mlsanity.com/paper/2504.03360/) (2025)
- [Vision-Language Models for Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/2502.07855/) (2025)
- [SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving](https://mlsanity.com/paper/2506.09397/) (2025)
- [WDMoE: Wireless Distributed Mixture of Experts for Large Language Models](https://mlsanity.com/paper/2411.06681/) (2024)
- [When IoT Meet LLMs: Applications and Challenges](https://mlsanity.com/paper/2411.17722/) (2024)
- [Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits](https://mlsanity.com/paper/2505.21594/) (2025)
- [Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions](https://mlsanity.com/paper/2407.04581/) (2024)
- [LightVLM: Acceleraing Large Multimodal Models with Pyramid Token Merging and KV Cache Compression](https://mlsanity.com/paper/2509.00419/) (2025)
- [ActionFlow: A Pipelined Action Acceleration for Vision Language Models on Edge](https://mlsanity.com/paper/2512.20276/) (2025)
- [Accelerating Mobile Language Model via Speculative Decoding and NPU-Coordinated Execution](https://mlsanity.com/paper/2510.15312/) (2025)
- [Kelle: Co-design KV Caching and eDRAM for Efficient LLM Serving in Edge Computing](https://mlsanity.com/paper/2510.16040/) (2025)
- [PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing](https://mlsanity.com/paper/2503.12167/) (2025)
- [Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices](https://mlsanity.com/paper/2504.00002/) (2025)
- [UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces](https://mlsanity.com/paper/2505.00472/) (2025)
- [Towards Pervasive Distributed Agentic Generative AI -- A State of The Art](https://mlsanity.com/paper/2506.13324/) (2025)
- [Towards Edge General Intelligence via Large Language Models: Opportunities and Challenges](https://mlsanity.com/paper/2410.18125/) (2024)
- [Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges](https://mlsanity.com/paper/2508.09561/) (2025)
- [FastTTS: Accelerating Test-Time Scaling for Edge LLM Reasoning](https://mlsanity.com/paper/2509.00195/) (2025)
- [One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models](https://mlsanity.com/paper/2505.12216/) (2025)
- [Scaling behavior of large language models in emotional safety classification across sizes and tasks](https://mlsanity.com/paper/2509.04512/) (2025)
- [PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration](https://mlsanity.com/paper/2512.08809/) (2025)
- [WISP: Waste- and Interference-Suppressed Distributed Speculative LLM Serving at the Edge via Dynamic Drafting and SLO-Aware Batching](https://mlsanity.com/paper/2601.11652/) (2026)
- [ConfigSpec: Profiling-Based Configuration Selection for Distributed Edge--Cloud Speculative LLM Serving](https://mlsanity.com/paper/2604.09722/) (2026)
- [Adaptive Pruning for Large Language Models with Structural Importance Awareness](https://mlsanity.com/paper/2412.15127/) (2024)
- [SSFO: Self-Supervised Faithfulness Optimization for Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.17225/) (2025)
- [AQLoRA: A Zero-Search Recipe for Fast Quantized LoRA Fine-Tuning](https://mlsanity.com/paper/2608.23816/) (2026)
- [Scaling Down the Scaling Laws: Parameter Efficiency and Compute-Optimal Training in Resource-Constrained Large Language Models](https://mlsanity.com/paper/2610.06387/) (2026)

## Abstract (arXiv)

Efficient adaption of large language models (LLMs) on edge devices is essential for applications requiring continuous and privacy-preserving adaptation and inference. However, existing tuning techniques fall short because of the high computation and memory overheads. To this end, we introduce a computation- and memory-efficient LLM tuning framework, called Edge-LLM, to facilitate affordable and effective LLM adaptation on edge devices. Specifically, Edge-LLM features three core components: (1) a layer-wise unified compression (LUC) technique to reduce the computation overhead by generating layer-wise pruning sparsity and quantization bit-width policies, (2) an adaptive layer tuning and voting scheme to reduce the memory overhead by reducing the backpropagation depth, and (3) a complementary hardware scheduling strategy to handle the irregular computation patterns introduced by LUC and adaptive layer tuning, thereby achieving efficient computation and data movements. Extensive experiments demonstrate that Edge-LLM achieves a 2.92x speed up and a 4x memory overhead reduction as compared to vanilla tuning methods with comparable task accuracy. Our code is available at https://github.com/GATECH-EIC/Edge-LLM