Awesome Mobile LLMs

A curated list of LLMs and related studies targeted at mobile and embedded hardware

Last update: 3rd September 2026

If your publication/work is not included - and you think it should - please open an issue or reach out directly to @stevelaskaridis.

Let's try to make this list as useful as possible to researchers, engineers and practitioners all around the world.

Contents

• Mobile-First LLMs
• Infrastructure / Deployment of LLMs on Device
• Benchmarking LLMs on Device
• Mobile-Specific Optimisations
• Applications
• Multimodal LLMs
• Surveys on Efficient LLMs
• Training LLMs on Device
• Mobile-Related Use-cases
• Benchmarks
• Leaderboards
• Books and Courses
• Industry Announcements
• Related Organized Workshops
• Related Awesome Repositories

Mobile-First LLMs

The following Table shows sub-3B models designed for on-device deployments, sorted by year.

Infrastructure / Deployment of LLMs on Device

This section showcases frameworks and contributions for supporting LLM inference on mobile and edge devices.

Deployment Frameworks

On-Device Inference Frameworks

These frameworks are primarily used to run models directly on-device, inside mobile apps, edge deployments, or tightly integrated local runtimes.

• llama.cpp: Inference of Meta's LLaMA model (and others) in pure C/C++. Supports various platforms and builds on top of ggml (now gguf format).
  • LLMFarm: iOS frontend for llama.cpp
  • LLM.swift: iOS frontend for llama.cpp
  • Sherpa: Android frontend for llama.cpp
  • iAkashPaul/Portal: Wraps the example android app with tweaked UI, configs & additional model support
  • dusty-nv's llama.cpp: Containers for Jetson deployment of llama.cpp
  • Off Grid: Open-source React Native app for on-device LLM chat, vision models (SmolVLM, LLaVA), and Stable Diffusion image generation on iOS & Android.
  • Airgap: Open-source React Native framework for on-device, offline-first customer support chatbots. Runs Gemma 4 E2B locally via llama.rn. Seven industry templates (telco, retail, healthcare, banking, education, insurance, airlines) ship in the repo.
• MLC-LLM: MLC LLM is a machine learning compiler and high-performance deployment engine for large language models. Supports various platforms and build on top of TVM.
  • Android App: MLC Android app
  • iOS App: MLC iOS app
  • dusty-nv's MLC: Containers for Jetson deployment of MLC
• PyTorch ExecuTorch: Solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers.
  • TorchChat: Codebase showcasing the ability to run large language models (LLMs) seamlessly across iOS and Android
• Google MediaPipe: A suite of libraries and tools for you to quickly apply artificial intelligence (AI) and machine learning (ML) techniques in your applications. Support Android, iOS, Python and Web.
  • GoogleAI-Edge Gallery: Experimental app that puts the power of cutting-edge Generative AI models directly into your hands, running entirely on your Android and iOS devices.
• Apple MLX: MLX is an array framework for machine learning research on Apple silicon, brought to you by Apple machine learning research. Builds upon lazy evaluation and unified memory architecture.
  • MLX Swift: Swift API for MLX.

• Apple Foundation Models SDK: Python bindings for Apple's Foundation Models framework, providing access to the on-device foundation model at the core of Apple Intelligence on macOS.

• HF Swift Transformers: Swift Package to implement a transformers-like API in Swift
• Alibaba MNN: MNN supports inference and training of deep learning models and for inference and training on-device.
• llama2.c (More educational, see here for android port)
• tinygrad: Simple neural network framework from tinycorp and @geohot
• TinyChatEngine: Targeted at Nvidia, Apple M1 and RPi, from Song Han's (MIT) group.
• Llama Stack (swift, kotlin): These libraries are a set of SDKs that provide a simple and effective way to integrate AI capabilities into your iOS/Android app, whether it is local (on-device) or remote inference.
• OLMoE.Swift: Ai2 OLMoE is an AI chatbot powered by the OLMoE model. Unlike cloud-based AI assistants, OLMoE runs entirely on your device, ensuring complete privacy and offline accessibility—even in Flight Mode.
• HuggingSnap: HuggingSnap is an iOS app that lets users quickly learn more about the places and objects around them. HuggingSnap runs SmolVLM2, a compact open multimodal model that accepts arbitrary sequences of image, videos, and text inputs to produce text outputs.
• Flower Intelligence: Flower Intelligence is a cross-platform inference library that lets users seamlessly interact with Large-Language Models both locally and remotely in a secure and private way. The library was created by the Flower Labs team. It supports TypeScript, JavaScript and Swift backends.
• ONNX Runtime: Cross-platform inference and training engine for ONNX models, with a Mobile package and execution providers (NNAPI, Core ML, XNNPACK, QNN) for on-device deployment on Android and iOS. See the Mobile deployment guide.

Local Network Model Serving

These frameworks are primarily used to host models on a laptop, desktop, or workstation and expose them over a local API to other devices on the same LAN.

• LM Studio: Desktop application and local inference server for hosting models on your machine, with an OpenAI-compatible local API.
• Ollama: Local model runner and server for hosting and serving models through a simple CLI and HTTP API.
• Lemonade: Open-source local AI server for text, image, and speech workloads, designed to run privately on local PCs and compatible with OpenAI-style APIs.
• llama.cpp: Can also be used as a lightweight local inference server for hosting GGUF models via CLI and HTTP server modes.
• LocalAI: Self-hosted local inference server and OpenAI-compatible REST API for running LLM, vision, image, and audio workloads on local or on-prem hardware.
• Locally AI: Native Apple-platform app for running AI models fully offline on iPhone, iPad, and Mac, optimized for Apple Silicon and on-device privacy.
• vLLM: High-throughput inference and serving engine that can expose OpenAI-compatible local APIs, better suited to stronger desktops and workstations.
• SGLang: High-performance model serving framework for local and distributed deployments, designed for low-latency and high-throughput inference.

Papers

2026

• [SenSys'26] An Efficient Context Management System for On-Device LLMaaS
  Wangsong Yin et al.
  
• FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
  Yinpeng Wu, Yitong Chen, Lixiang Wang, et al.
  

2025

• Apple Intelligence Foundation Language Models: Tech Report 2025
  Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang, et al.
  
• [ACM Queue] Generative AI at the Edge: Challenges and Opportunities: The next phase in AI deployment
  Vijay Janapa Reddi
  

2024

• PowerInfer-2: Fast Large Language Model Inference on a Smartphone
  Zhenliang Xue, Yixin Song, Zeyu Mi, et al.
  
• [MobiCom'24] Mobile Foundation Model as Firmware
  Jinliang Yuan, Chen Yang, Dongqi Cai, et al.
  
• Merino: Entropy-driven Design for Generative Language Models on IoT Devicess
  Youpeng Zhao, Ming Lin, Huadong Tang, et al.
  
• LLM as a System Service on Mobile Devices
  Wangsong Yin, Mengwei Xu, Yuanchun Li, et al.
  

2023

• LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices
  Junchen Zhao, Yurun Song, Simeng Liu, et al.
  
• LLMCad: Fast and Scalable On-device Large Language Model Inference
  Daliang Xu, Wangsong Yin, Xin Jin, et al.
  
• EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models
  Rongjie Yi, Liwei Guo, Shiyun Wei, et al.
  

2022

• [IEEE Pervasive Computing] The Future of Consumer Edge-AI Computing
  Stefanos Laskaridis, Stylianos I. Venieris, Alexandros Kouris, et al.
  

Benchmarking LLMs on Device

This section focuses on measurements and benchmarking efforts for assessing LLM performance when deployed on device.

Papers

2026

• LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load
  Pranay Tummalapalli, Sahil Arayakandy, Ritam Pal, Kautuk Kundan
  

2025

• Intelligence Per Watt: Measuring Intelligence Efficiency of Local AI
  Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin, et al.
  
• P/D-Device: Disaggregated Large Language Model between Cloud and Devices
  Yibo Jin, Yixu Xu, Yue Chen, et al.
  
• Sometimes Painful but Promising: Feasibility and Trade-Offs of On-Device Language Model Inference
  Maximilian Abstreiter, Sasu Tarkoma, Roberto Morabito
  
• [ICLR'25] PalmBench: A Comprehensive Benchmark of Compressed Large Language Models on Mobile Platforms
  Yilong Li, Jingyu Liu, Hao Zhang, et al.
  
• [SEC'25] lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models
  Haoxin Wang, Xiaolong Tu, Hongyu Ke, et al.
  

2024

• Large Language Model Performance Benchmarking on Mobile Platforms: A Thorough Evaluation
  Jie Xiao, Qianyi Huang, Xu Chen, et al.
  
• [EdgeFM @ MobiSys'24] Large Language Models on Mobile Devices: Measurements, Analysis, and Insights
  Xiang Li, Zhenyan Lu, Dongqi Cai, et al.
  
• MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
  Rithesh Murthy, Liangwei Yang, Juntao Tan, et al.
  
• [MobiCom'24] MELTing point: Mobile Evaluation of Language Transformers
  Stefanos Laskaridis, Kleomenis Katevas, Lorenzo Minto, et al.
  

Mobile-Specific Optimisations

This section focuses on techniques and optimisations that target mobile-specific deployment.

Papers

2026

• MobileMoE: Scaling On-Device Mixture of Experts
  Yanbei Chen et al.
  
• Quant.npu: Enabling Efficient Mobile NPU Inference for on-device LLMs via Fully Static Quantization
  Jinghe Zhang et al.
  
• [MobiSys'26] ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference
  Wangsong Yin, Daliang Xu, Mengwei Xu, et al.
  
• Efficient On-Device Diffusion LLM Inference with Mobile NPU
  Tuowei Wang, Yanfan Sun, Ju Ren
  

2025

• [NeurIPS'25] Nemotron-Flash: Towards Latency-Optimal Hybrid Small Language Models
  Yonggan Fu, Xin Dong, Shizhe Diao, et al.
  
• [MobiCom '25] Elastic On-Device LLM Service
  Wangsong Yin, Rongjie Yi, Daliang Xu, et al.
  
• [MobiCom '25] Confidant: Customizing Transformer-based LLMs via Collaborative Training on Mobile Devices
  Yuhao Chen, Yuxuan Yan, Shuowei Ge, et al.
  
• [MobiCom '25] D2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving
  Haodong Wang, Qihua Zhou, Zicong Hong, et al.
  
• [CVPR'25 EDGE Workshop] Scaling On-Device GPU Inference for Large Generative Models
  Jiuqiang Tang, Raman Sarokin, Ekaterina Ignasheva, et al.
  
• ROMA: a Read-Only-Memory-based Accelerator for QLoRA-based On-Device LLM
  Liang Li, Xingke Yang, Wen Wu, et al.
  
• [ASPLOS'25] Fast On-device LLM Inference with NPUs
  Daliang Xu, Hao Zhang, Liming Yang, et al.
  

2024

• Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference
  Andrii Skliar, Ties van Rozendaal, Romain Lepert, et al.
  
• PhoneLM: An Efficient and Capable Small Language Model Family through Principled Pre-training
  Rongjie Yi, Xiang Li, Weikai Xie, et al.
  
• MobileQuant: Mobile-friendly Quantization for On-device Language Models
  Fuwen Tan, Royson Lee, Łukasz Dudziak, et al.
  
• Gemma 2: Improving Open Language Models at a Practical Size
  Gemma Team, Morgane Riviere, Shreya Pathak, et al.
  
• Apple Intelligence Foundation Language Models
  Tom Gunter, Zirui Wang, Chong Wang, et al.
  
• 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, et al.
  
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
  Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.
  
• Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
  Luchang Li, Sheng Qian, Jie Lu, et al.
  
• Gemma: Open Models Based on Gemini Research and Technology
  Gemma Team, Google DeepMind
  
• MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT
  Omkar Thawakar, Ashmal Vayani, Salman Khan, et al.
  
• [ICML'24] MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
  Zechun Liu, Changsheng Zhao, Forrest Iandola, et al.
  
• [ICML'24] Rethinking Optimization and Architecture for Tiny Language Models
  Yehui Tang, Kai Han, Fangcheng Liu, et al.
  
• TinyLlama: An Open-Source Small Language Model
  Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al.
  

Applications

Papers

2024

• Octopus v3: Technical Report for On-device Sub-billion Multimodal AI Agent
  Wei Chen, Zhiyuan Li
  
• Octopus v2: On-device language model for super agent
  Wei Chen, Zhiyuan Li
  
• Octopus: On-device language model for function calling of software APIs
  Wei Chen, Zhiyuan Li, Mingyuan Ma
  

2023

• Revolutionizing Mobile Interaction: Enabling a 3 Billion Parameter GPT LLM on Mobile
  Samuel Carreira, Tomas Marques, Jose Ribeiro, Carlos Grilo
  
• Towards an On-device Agent for Text Rewriting
  Yun Zhu, Yinxiao Liu, Felix Stahlberg, et al.
  

Multimodal LLMs

This section refers to multimodal LLMs, which integrate vision or other modalities in their tasks.

Papers

2026

• Small Vision-Language Models are Smart Compressors for Long Video Understanding
  Junjie Fei et al.
  

2024

• [CVPR 2024] MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
  Vasu, Pavan Kumar Anasosalu, Pouransari, Hadi, Faghri, Fartash, et al.
  
• TinyLLaVA: A Framework of Small-scale Large Multimodal Models
  Baichuan Zhou, Ying Hu, Xi Weng, et al.
  
• MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
  Xiangxiang Chu, Limeng Qiao, Xinyu Zhang, et al.
  

2023

• MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
  Xiangxiang Chu, Limeng Qiao, Xinyang Lin, et al.
  

Surveys on Efficient LLMs

This section includes survey papers on LLM efficiency, a topic very much related to deploying in constrained devices.

Papers

2025

• GenAI at the Edge: Comprehensive Survey on Empowering Edge Devices
  Mozhgan Navardi, Romina Aalishah, Yuzhe Fu, et al.
  
• Demystifying Small Language Models for Edge Deployment
  Zhenyan Lu, Xiang Li, Dongqi Cai, et al.
  
• Small Language Models (SLMs) Can Still Pack a Punch: A survey
  Shreyas Subramanian, Vikram Elango, Mecit Gungor
  

2024

• A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
  Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.
  
• Small Language Models: Survey, Measurements, and Insights
  Zhenyan Lu, Xiang Li, Dongqi Cai, et al.
  
• On-Device Language Models: A Comprehensive Review
  Jiajun Xu, Zhiyuan Li, Wei Chen, et al.
  
• A Survey of Resource-efficient LLM and Multimodal Foundation Models
  Mengwei Xu, Wangsong Yin, Dongqi Cai, et al.
  

2023

• Efficient Large Language Models: A Survey
  Zhongwei Wan, Xin Wang, Che Liu, et al.
  
• Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
  Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.
  
• A Survey on Model Compression for Large Language Models
  Xunyu Zhu, Jian Li, Yong Liu, et al.
  

Training LLMs on Device

This section refers to papers attempting to train/fine-tune LLMs on device, in a standalone or federated manner.

Papers

2026

• [MobiSys'26] FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs
  Kahou Tam, Wei Niu, Yu Bao, et al.
  
• Online-SDFT: Fine-Tuning Small Language Models for Continual Learning On-Device with Self-Distillation
  I-Ju Lin, Zhang-Wei Hong
  

2025

• Computational Bottlenecks of Training Small-scale Large Language Models
  Saleh Ashkboos, Iman Mirzadeh, Keivan Alizadeh, et al.
  
• [ICML'25] On-device collaborative language modeling via a mixture of generalists and specialists
  Dongyang Fan, Bettina Messmer, Nikita Doikov, et al.
  
• MobiLLM: Enabling LLM Fine-Tuning on the Mobile Device via Server Assisted Side Tuning
  Liang Li, Xingke Yang, Wen Wu, et al.
  

2024

• [Privacy in Natural Language Processing @ ACL'24] PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs
  Dan Peng, Zhihui Fu
  

2023

• [MobiCom'23] Federated Few-Shot Learning for Mobile NLP
  Dongqi Cai, Shangguang Wang, Yaozong Wu, et al.
  
• FwdLLM: Efficient FedLLM using Forward Gradient
  Mengwei Xu, Dongqi Cai, Yaozong Wu, et al.
  
• [Electronics'24] Forward Learning of Large Language Models by Consumer Devices
  Danilo Pietro Pau, Fabrizio Maria Aymone
  
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
  Herbert Woisetschläger, Alexander Isenko, Shiqiang Wang, et al.
  
• Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
  Zhen Qin, Daoyuan Chen, Bingchen Qian, et al.
  

Mobile-Related Use-cases

This section includes paper that are mobile-related, but not necessarily run on device.

Papers

2026

• Xiaomi-GUI-0 Technical Report
  Wanxia Cao, Chengzhen Duan, Pei Fu, et al.
  
• MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management
  Guangyi Liu, Gao Wu, Congxiao Liu, et al.
  
• Beyond the GUI Paradigm: Do Mobile Agents Need the Phone Screen?
  Li Gu, Zihuan Jiang, Linqiang Guo, et al.
  
• CAPED: Context-Aware Privacy Exposure Defense for Mobile GUI Agents
  Siyu Shen, Fenghao Xu, Wenrui Diao, et al.
  
• iOSWorld: A Benchmark for Personally Intelligent Phone Agents
  Lawrence Keunho Jang, Mareks Woodside, Geronimo Carom, et al.
  
• MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents
  Ziyun Zeng, Hang Hua, Bocheng Zou, et al.
  
• How Mobile World Model Guides GUI Agents?
  Weikai Xu, Kun Huang, Yunren Feng, et al.
  
• Mem-W: Latent Memory-Native GUI Agents
  Guibin Zhang, Yaohui Ling, Fanci Meng, et al.
  
• ClawMobile: Rethinking Smartphone-Native Agentic Systems
  Lepeng Zhao, Zhenhua Zou, Shuo Li, et al.
  
• Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents
  Haiyang Xu, Xi Zhang, Haowei Liu, et al.
  
• Anonymization-Enhanced Privacy Protection for Mobile GUI Agents: Available but Invisible
  Hongchao Du, Shangyu Wu, Qiao Li, et al.
  
• MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments
  Guangyi Liu, Pengxiang Zhao, Yaozhen Liang, et al.
  

2025

• Slm-mux: Orchestrating small language models for reasoning
  Chenyu Wang, Zishen Wan, Hao Kang, et al.
  
• Ferret-UI Lite: Lessons from Building Small On-Device GUI Agents
  Zhen Yang, Zi-Yi Dou, Di Feng, et al.
  
• [NeurIPS'25] OmniDraft: A Cross-vocabulary, Online Adaptive Drafter for On-device Speculative Decoding
  Ramchalam Kinattinkara Ramakrishnan, Zhaocong Yuan, Shaojie Zhuo, et al.
  
• Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement
  Xuechen Zhang, Zijian Huang, Chenshun Ni, et al.
  
• Small Language Models are the Future of Agentic AI
  Peter Belcak, Greg Heinrich, Shizhe Diao, et al.
  

2024

• Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
  Junyang Wang, Haiyang Xu, Haitao Jia, et al.
  
• Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs
  Keen You, Haotian Zhang, Eldon Schoop, et al.
  
• Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception
  Junyang Wang, Haiyang Xu, Jiabo Ye, et al.
  
• [MobiCom'24] MobileGPT: Augmenting LLM with Human-like App Memory for Mobile Task Automation
  Sunjae Lee, Junyoung Choi, Jungjae Lee, et al.
  
• [MobiCom'24] AutoDroid: LLM-powered Task Automation in Android
  Hao Wen, Yuanchun Li, Guohong Liu, et al.
  

2023

• [NeurIPS'23] AndroidInTheWild: A Large-Scale Dataset For Android Device Control
  Christopher Rawles, Alice Li, Daniel Rodriguez, et al.
  
• GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation
  An Yan, Zhengyuan Yang, Wanrong Zhu, et al.
  

Older

• [ACL'20] Mapping Natural Language Instructions to Mobile UI Action Sequences
  Yang Li, Jiacong He, Xin Zhou, et al.
  

Benchmarks

• ExoLabs Benchmarks
• Qualcomm AI Hub

Leaderboards

• HF Open LLM Leaderboard
• FlowerTune LLM Leaderboard
• MLPerf Inference: Mobile Benchmark Suite

Books and Courses

• Edge AI Engineering by Marcelo Rovai
• Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems by Vijay Janapa Reddi

Industry Announcements

• WWDC'24 - Apple Foundation Models
• PyTorch Executorch Alpha
• Google - LLMs On-Device with MediaPipe and TFLite
• Qualcomm - The future of AI is Hybrid
• ARM - Generative AI on mobile

Related Organized Workshops

• TTODLer-FM @ ICML'25: Tiny Titans: The next wave of On-Device Learning for Foundational Models (TTODLer-FM)
• ES-FoMO @ ICML'25: Efficient Systems for Foundation Models
• Binary Networks @ ICCV'25: Binary and Extreme Quantization for Computer Vision
• SLLM @ ICLR'25: Workshop on Sparsity in LLMs: Deep Dive into Mixture of Experts, Quantization, Hardware, and Inference
• MCDC @ ICLR'25: Workshop on Modularity for Collaborative, Decentralized, and Continual Deep Learning
• Adaptive Foundation Models @ NeurIPS'24

Related Awesome Repositories

If you want to read more about related topics, here are some tangential awesome repositories to visit:

• NexaAI/Awesome-LLMs-on-device on LLMs on Device
• FairyFali/SLMs-Survey on Small Language Models
• Hannibal046/Awesome-LLM on Large Language Models
• KennethanCeyer/awesome-llm on Large Language Models
• HuangOwen/Awesome-LLM-Compression on Large Language Model Compression
• csarron/awesome-emdl on Embedded and Mobile Deep Learning

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