Transforming smart home privacy and latency with local LLM inference on Arm devices

The problem: Smart homes rely on the cloud. At a cost.

Most smart home assistants rely on cloud-based AI. They use it even for simple tasks, such as turning on a light, setting a thermostat, or checking energy usage.This introduces privacy risks and lag. It also makes your home vulnerable to network outages.

In a world moving toward privacy and autonomy, the challenge is clear. Can we bring true intelligence to smart homes—locally, efficiently, and privately—using affordable hardware like Raspberry Pi 5?

Figure 1: UI when running Qwen.

Figure 2: UI when running DeepSeek.

Why it matters: Privacy, reliability, and Edge AI for everyone

For millions of users, especially those with unreliable or costly internet a cloud-dependent smart home is only “smart” when the network works. Even in well-connected homes, privacy remains a growing concern. The Raspberry Pi 5 delivers a major performance leap with its 64-bit Arm processor. Paired with efficient LLMs, it can now run advanced AI locally, offering full privacy and real-time response. This project shows that powerful, private AI can now run on affordable, accessible hardware.

The solution: Private, conversational smart home AI on Raspberry Pi 5

This open-source, privacy-first smart home assistant shows that large language models can now run entirely locally on Arm-based devices. It uses Ollama and LLMs for natural language commands and home automation.. There is no cloud. There is no compromise.

Key implementation steps

• Ollama LLM Backend: All Natural Language Processing (NLP) runs on-device using Ollama, supporting models like Deepseek, Tinyllama, Qwen, and Gemma. Models are pulled and run locally, requiring no external API.
• Optimized for Arm: The system is tuned for the Pi 5 quad-core 64-bit Arm Cortex-A76 processor. The architecture performance-per-watt efficiency and powerful NEON engine are essential for accelerating the complex mathematics of LLM inference, enabling the Pi 5 to run larger models with lower latency than previous generations.
• Direct Device Control: Supports GPIO, MQTT, and Zigbee for direct hardware integration with lights, fans, plugs, sensors, and more.
• Web Dashboard & API: Includes a clean UI and REST API for control and monitoring, plus a Command Line Interface (CLI) for advanced users.
• Real-Time Metrics: Monitors LLM speed (tokens/sec), command latency, power consumption, and cache hits—ensuring full transparency and performance tuning.

Hardware setup

Figure 3: Hardware setup

• Raspberry Pi 5 (8GB or 16GB recommended). This single-board computer features a quad-core Arm Cortex-A76 CPU, each core operating at up to 2.4 GHz. The Arm cores support NEON SIMD (Single Instruction, Multiple Data) extensions, enabling efficient parallel processing for optimized performance in compute-intensive applications.
• Raspberry Pi OS 64-bit (or Ubuntu 22.04 ARM64).
• MicroSD or NVMe Storage.
• Internet for initial setup (operates offline once deployed).
• GPIO devices: lights, sensors, etc. connected via GPIO/MQTT/Zigbee.
• Full setup & instructions on GitHub: https://github.com/fidel-makatia/EdgeAI_Raspi5/tree/main.

System architecture

The system employs a fully local workflow from command to action.

Figure 4: System architecture

Metrics visualization

Figure 5: System initialization sequence highlighting key optimization steps and hardware/software initializations.

Figure 6: benchmark summary

Technical details

Challenges and solutions

• Deploying LLMs on the Pi 5: Ollama’s quantized models make LLM inference practical. Quantization reduces the memory and computational footprint of models, a technique highly effective on modern Arm CPUs. This allows the Pi 5 to support models up to 7B parameters (e.g., Deepseek 7B) in under 16GB RAM.
• Maintaining Real-Time Responsiveness: All requests and automation remain local, resulting in sub-second response times for most tasks after inference.
• Universal Protocol Support: The code is modular to support GPIO, MQTT, and Zigbee, ensuring compatibility with a wide range of home hardware.

Performance metrics

Results & impact

• Truly Local, Private AI: All processing and automation occur on-device—no data ever leaves your local network.
• Low Latency, High Reliability: Near-instant command execution—even with the internet unplugged.
• Hardware Flexibility: Runs on any Arm Cortex A architecture that supports Python and Ollama.

Technology stack

• Backend: Python 3, Flask
• Web: HTML, CSS, JS
• LLM/NLP: Ollama, DeepSeek (others: Gemma, Qwen, Tinyllama, Mistral)
• Hardware: gpiozero, Adafruit DHT (optional)
• API: REST, Web dashboard, CLI

Get started

• Repo & Docs: https://github.com/fidel-makatia/EdgeAI_Raspi5
• Quickstart:
  1. Flash Raspberry Pi OS or Ubuntu to an SD card/NVMe drive.
  2. Clone the repo & follow the installation instructions.
  3. Download supported LLMs using Ollama (see repo for tips).
  4. Connect home devices and automate.

What is next

• Fork the repo and try it on your own Pi 5.
• Extend for more models, protocols, or sensors.
• Use the dashboard for local automation and monitoring.
• Watch the demo below.

Watch demo

Arm developer tools used

• Arm Deepseek learning path
• Llama 3 on Raspberry Pi 5 learning path

Resources

• EdgeAI_Raspi5 GitHub: https://github.com/fidel-makatia/EdgeAI_Raspi5
• Ollama: https://ollama.com/
• Raspberry Pi 5 Documentation
• Whisper.cpp (for optional voice integration)

This project shows how local LLM inference on Raspberry Pi 5 transforms smart home privacy, latency, and reliability. It puts control where it belongs, at the edge.

For a step-by-step guide on creating a privacy-first smart home assistant, explore the Arm Learning Path for Raspberry Pi Smart Home.

Find Fidel on GitHub