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

Component	Details	Repo/docs
EdgeAI_Raspi5	Main repo for local LLM inference and smart home integration on Pi 5	https://github.com/fidel-makatia/EdgeAI_Raspi5
Supported devices	Raspberry Pi 5 (Arm Cortex-A76)
LLM backend	Ollama (DeepSeek, Gemma, Tinyllama, Qwen)
OS	Raspberry Pi OS or Ubuntu Arm64
NLP interface	Ollama API (runs locally)
Integration	GPIO, REST API, MQTT
Frontend	Flask, HTML/CSS/JS Web Dasbboard
Metrics	Token/sec, latency, device state, power draw

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

Metric	Local LLM on Pi 5 (Ollama)	Cloud-Based AI	Notes
Inference Latency	~1–9 sec (Tinyllama 1.1B)	0.5–2.5 sec (+network jitter)	Local is consistent, private, and predictable.
Command Execution	Instant after inference	Delayed by network/server	The Arm-powered Pi 5 eliminates the cloud as a point of failure.
Tokens/Second	8–20 tokens/sec	20–80+	Local models are rapidly improving in speed on Arm hardware.
Reliability	Works offline; no external dependency	Needs Internet	The Pi 5 provides an always-on hub immune to ISP outages.
Privacy	100% on-device; nothing leaves	Data sent to provider	Absolute data privacy is guaranteed.
Cost (Ongoing)	$0 after hardware	$5–$25/mo (API fees)	No recurring costs.
Model Customization	Run any quantized model locally	Fixed by provider	GGUF and ONNX formats are supported for full flexibility.
Security	Local network only	Exposed to remote breaches	The attack surface is dramatically reduced.

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