Hardware
Single-Board Edge Self-Hosting
Run small models and lightweight agents on Raspberry Pi, Orange Pi, and other edge boards for offline, low-power inference.
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Verified 29 days ago
Single-Board Edge Self-Hosting
Why run agents on the edge
Edge boards are cheap, quiet, and always on. They are not fast enough for frontier models, but they are perfect for narrow agents: sensor summarization, local voice commands, privacy-first transcription, and offline classification.
What fits on edge hardware
- 1B–4B parameter language models.
- Image classification with quantized vision models.
- Wake-word and keyword spotting.
- Rule-based or small-model agents that orchestrate local sensors and actuators.
Hardware options
| Board | Strength | Notes |
|---|---|---|
| Raspberry Pi 5 | Ecosystem and community | 8GB RAM; good for 1B–3B models |
| Orange Pi 5 Plus | More RAM and cores | Up to 16GB; better for 4B models |
| NVIDIA Jetson Orin Nano | GPU-accelerated inference | Best for vision and small transformer models |
| Apple TV / Mac mini (idle) | Reuse existing hardware | Apple Silicon is efficient for small models |
Software stack
- Ollama or llama.cpp for model inference.
- MQTT or Node-RED for sensor integration.
- SQLite or Chroma for local memory.
- OpenClaw or lightweight Python agents for orchestration.
Tradeoffs
- Limited context and capability. Edge models are narrow by design.
- Slow inference. Quantization and small chips mean patience is required.
- Storage constraints. Model weights fill SD cards and eMMC quickly.
- Cooling and power. Some boards throttle under sustained load.
Best fit
Home automation, offline privacy-sensitive tasks, sensor summarization, and prototypes that need to run without cloud dependency.