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Intel® Edge Developer Kit Use Cases

Transform your Intel® hardware into powerful AI and edge computing solutions with these ready-to-deploy use cases.

🎯 Choose by Experience Level

🟢 Beginner-Friendly (New to AI/Edge Computing)

Use Case What You'll Build Time Learn
🤖 Chat with Local LLMs Private ChatGPT* alternative ⏱️ 10 min LLM deployment, web interfaces
🎥 AI Video Analytics Smart video content analysis ⏱️ 15 min Computer vision, semantic search
💬 Edge AI Demo Studio (Digital Avatar) AI microservices ⏱️ 20 min Multi-modal AI, speech processing

🟡 Intermediate (Some AI/Development Experience)

Use Case What You'll Build Time Learn
📚 RAG Knowledge Base Enterprise AI knowledge system ⏱️ 30 min RAG patterns, vector databases
🚗 Smart Parking IoT-powered parking management ⏱️ 45 min IoT integration, real-time analytics
📹 Video Summarization AI-powered video insights ⏱️ 60 min Multi-modal AI, content analysis
📄 Visual & Textual Query-driven Document Reasoning Engine Document Search & Retrieval Engine ⏱️ 30 min Document embedding, VLM
🎬 VLM Video Summarization and Interactive Chat AI video analysis with interactive chat ⏱️ 30 min VLM, vector embeddings, semantic search
🤖 Manufacturing HMI with LLM & GenAI Manufacturing Defect Detection Agent ⏱️ 90 min Robotics, Object Detection, LLM, RAG

🔴 Advanced (Experienced Developers)

Use Case What You'll Build Time Learn
🏎️ Real-Time Computing (TCC) Ultra-low latency applications ⏱️ 60+ min Real-time systems, hardware timing
📷 GMSL Camera Integration High-speed camera systems ⏱️ 90+ min Camera drivers, embedded systems
🔍 Intel® Distribution of OpenVINO™ Toolkit Advanced Custom AI model deployment ⏱️ 120+ min Model optimization, performance tuning

🎯 Choose by Application Area

🤖 Artificial Intelligence

📹 Media & Content

🏢 Enterprise & IoT

🔧 Platform & Framework

🚀 Quick Start Recommendations

👨‍🎓 For Students

  1. Start with OpenWebUI + Ollama - Learn LLM deployment
  2. Try AI Video Analytics - Understand computer vision
  3. Explore Edge AI Demo Studio (Digital Avatar) - Multi-modal AI concepts

👨‍💼 For Professionals

  1. Deploy RAG Toolkit - Enterprise AI patterns
  2. Explore Visual & Textual Query-driven Document Reasoning Engine - Document embedding models & VLMs
  3. Try VLM Video Summarization and Interactive Chat - Advanced video analysis with VLM
  4. Implement Smart Parking - IoT + AI integration
  5. Master Real-Time Computing - Performance optimization
  6. Explore Manufacturing HMI with LLM & GenAI - Agentic AI

🔬 For Researchers

  1. Optimize with OpenVINO - Custom model deployment
  2. Integrate GMSL Cameras - Advanced imaging
  3. Develop Video Summarization - Multi-modal research

📋 Prerequisites

System Requirements

  • ✅ Completed platform setup
  • ✅ Docker installed and running
  • ✅ Sufficient disk space (varies by use case)

Hardware Recommendations

Use Case Category Minimum Hardware Recommended
LLM/Chat Applications 8GB GPU memory 12GB+ GPU memory
Computer Vision Integrated graphics Dedicated GPU + NPU
Real-Time Systems Intel® Core™ Ultra processor Latest generation CPU
Camera Integration Platform-specific Validated hardware only

🆘 Getting Help

Before You Start

During Development

Ready to start building? Pick a use case above and follow its README for step-by-step instructions!

Use Cases

  1. Intel® Distribution of OpenVINO™ Toolkit
  2. Open WebUI with Ollama
  3. LLM RAG Toolkit
  4. AI Video Analytics
  5. Edge AI Demo Studio (Digital Avatar)
    • A tool that simplifies deploying and managing AI microservices like TTS and STT in the edge.
  6. Time Coordinated Computing (TCC)
  7. Smart Parking
  8. Video Summarization & Visual RAG
  9. GMSL Camera Enablement
  10. MIPI Camera Enablement
  11. Visual & Textual Query-driven Document Reasoning Engine
  12. LLM Database Query using Intel AI Assistant Builder
  13. Manufacturing HMI with LLM & GenAI