awesome-ai-edge-computing
github.com/awesomelistsio/awesome-ai-edge-computing ↗A curated list of awesome tools, frameworks, libraries, and resources for running AI models on edge devices, including smartphones, IoT devices, embedded systems, and hardware accelerators. Edge AI focuses on processing data locally on the device, reducing latency and enhancing privacy.
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me community resources from awesome-ai-edge-computing"
Installation instructions →What's inside
Community
- AI on the Edge Forum
A forum for discussing AI applications and deployments on edge devices.
- Edge AI and Vision Alliance
A group dedicated to advancing the use of computer vision and AI at the edge.
- NVIDIA Developer Forums
A community for discussing NVIDIA’s edge AI hardware and software.
- Reddit: r/TinyML
A subreddit for discussing TinyML and edge AI projects.
- TinyML Foundation
A community focused on machine learning for tiny, low-power devices.
Frameworks and Libraries
- Apache TVM
An open-source deep learning compiler stack for running machine learning models on edge devices.
- DeepC
A framework for deploying deep learning models on microcontrollers and edge devices with limited resources.
- Edge Impulse SDK
A toolkit for building, optimizing, and deploying machine learning models on edge devices.
- ONNX Runtime
A cross-platform, high-performance scoring engine for running ONNX models on edge devices.
- TensorFlow Lite
A lightweight version of TensorFlow designed for mobile and embedded devices.
Hardware and Accelerators
- Arduino Nano 33 BLE Sense
An Arduino board designed for AI and machine learning projects at the edge.
- Google Coral
Edge AI hardware by Google, featuring the Edge TPU for fast, efficient inference.
- Intel Movidius Neural Compute Stick
A USB-based neural compute accelerator for running AI models at the edge.
- NVIDIA Jetson
A family of embedded AI computing platforms for edge devices, offering powerful GPU acceleration.
- Raspberry Pi
A popular, low-cost single-board computer that can run AI models locally with the help of libraries like TensorFlow Lite.
- Xilinx Edge AI
AI-enabled FPGAs for real-time processing on edge devices.
Deployment Platforms
- AWS IoT Greengrass
A service for running local compute, messaging, data caching, sync, and ML inference on edge devices.
- Azure IoT Edge
A platform by Microsoft for deploying cloud intelligence on local edge devices.
- Balena
A platform for building, deploying, and managing containerized applications on edge devices.
- EdgeX Foundry
An open-source platform for building interoperable edge computing solutions.
- Google Cloud IoT Edge
A service by Google for running AI inference on edge devices using TensorFlow Lite and Edge TPU.
Learning Resources
- Coursera: Edge AI
Courses on deploying AI models on edge devices.
- Google Coral Tutorials
Tutorials for running AI inference on Google Coral hardware.
- NVIDIA Jetson Tutorials
Getting started guides and tutorials for the NVIDIA Jetson platform.
- PyTorch Mobile Documentation
Official documentation for deploying PyTorch models on mobile devices.
- TinyML Course by HarvardX
A course focused on building machine learning models for microcontrollers and edge devices.
Applications
- DailyVox
On-device AI voice diary app using Apple’s native frameworks for speech recognition and NLP, demonstrating privacy-first edge AI.
Optimization Tools
- NVIDIA TensorRT
A high-performance deep learning inference optimizer and runtime for NVIDIA GPUs, including Jetson devices.
- OctoML
An automated machine learning optimization platform for deploying efficient AI models on edge hardware.
- ONNX Quantization
Tools for optimizing ONNX models through quantization for faster inference on edge hardware.
- TensorFlow Model Optimization Toolkit
Tools for model pruning, quantization, and optimization to run efficiently on edge devices.
Showing a sample of 31 resources. View the full list on GitHub →