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Edge Device Deployment

Deploy GPUX models on edge devices with ARM and embedded GPU support.

🎯 Overview

Complete guide for deploying GPUX on edge devices including NVIDIA Jetson, Raspberry Pi, and ARM-based systems.

In Development

Detailed deployment guide for edge devices is being developed. Basic functionality is available.

📚 What Will Be Covered

  • 🔄 NVIDIA Jetson: TX2, Xavier, Orin series
  • 🔄 Raspberry Pi: Pi 4, Pi 5 with GPU acceleration
  • 🔄 ARM Devices: Apple Silicon, ARM-based servers
  • 🔄 Docker ARM: Multi-architecture container support
  • 🔄 Model Optimization: Quantization and pruning for edge
  • 🔄 Power Management: Battery optimization strategies
  • 🔄 Offline Operation: Local model serving without internet

🚀 Quick Start

NVIDIA Jetson

# Install GPUX on Jetson
sudo apt update
sudo apt install -y python3 python3-pip
pip3 install gpux

# Pull and serve model
gpux pull microsoft/DialoGPT-medium
gpux serve microsoft/DialoGPT-medium --port 8080

Raspberry Pi

# Install GPUX on Raspberry Pi
sudo apt update
sudo apt install -y python3 python3-pip
pip3 install gpux

# Pull and serve model
gpux pull microsoft/DialoGPT-medium
gpux serve microsoft/DialoGPT-medium --port 8080

Docker ARM Support

FROM python:3.11-slim

WORKDIR /app
RUN pip install gpux

# Pull model from Hugging Face Hub
RUN gpux pull microsoft/DialoGPT-medium

EXPOSE 8080
CMD ["gpux", "serve", "microsoft/DialoGPT-medium", "--port", "8080"]

# Build for ARM architecture
docker buildx build --platform linux/arm64 -t gpux-model-arm .

# Run on ARM device
docker run -p 8080:8080 gpux-model-arm

💡 Key Takeaways

Success

✅ ARM Support: Raspberry Pi, Apple Silicon, ARM servers ✅ Edge Optimization: Quantized models for low-power devices ✅ Offline Operation: Local model serving ✅ Docker ARM: Multi-architecture container support ✅ Power Efficiency: Optimized for battery-powered devices

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