The Future of Android is Local: How to Run Custom LLMs (Llama, Gemma) On-Device with MediaPipe and Kotlin

The article discusses the shift from cloud-based artificial intelligence to on-device AI processing in Android development, highlighting MediaPipe's AICore as a key tool for this transition. Here are some key points and insights:

1. On-Device AI Advantages:

   • Privacy: Running AI models locally ensures that sensitive data like medical records or legal documents never leave the device.
   • Performance: On-device processing can be faster due to reduced latency compared to cloud-based solutions.

2. MediaPipe AICore:

   • Provides a platform for running large-scale machine learning models on Android devices.
   • Supports various AI tasks including natural language processing (NLP), image recognition, and more.

3. Technical Implementation:

   • Model Management: Models need to be stored in accessible locations like /data/local/tmp/ instead of the compressed assets folder.
   • Threading and ANRs: Use Dispatchers.Default for CPU/GPU-bound tasks to avoid blocking the main thread, which can cause app crashes or poor user experience.
   • Memory Management: Large models require significant memory. Implement mechanisms to release model resources when not in use.

4. **Real-World Example

Read the full article at DEV Community

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