Add Privacy-First LLM Smart Home System for Raspberry Pi 5

content/install-guides/llm-jetson-xavier.md

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+---
+additional_search_terms:
+  - cloud
+  - deploy
+
+layout: installtoolsall
+minutes_to_complete: 15
+author: Fidel Makatia
+multi_install: false
+multitool_install_part: false
+official_docs: https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html
+test_images:
+  - ubuntu:latest
+test_maintenance: true
+title: AWS CLI
+tool_install: true
+weight: 1
+---
+
+[AWS CLI](https://docs.aws.amazon.com/cli/index.html) is a cross-platform command-line tool that can be installed on development computers. The AWS Command Line Interface (AWS CLI) is a unified tool that provides a consistent interface for interacting with all parts of Amazon Web Services.
+
+It is available for a variety of operating systems and Linux distributions, supports the Arm architecture and has multiple ways to install it.
+
+## What should I do before installing AWS CLI?
+
+This article provides quick solutions to install the latest version of AWS CLI, version 2, for Ubuntu on Arm.
+
+Confirm you are using an Arm computer with 64-bit Linux by running:
+
+```bash { target="ubuntu:latest" }
+uname -m
+```
+
+The output should be:
+
+```output
+aarch64
+```
+
+If you see a different result, you are not using an Arm computer running 64-bit Linux.
+
+## How do I download and install AWS CLI version 2?
+
+The easiest way to install the latest version of the AWS CLI for Ubuntu on Arm is to download and run the installer from AWS.
+
+Before starting, install `unzip`:
+
+```bash { target="ubuntu:latest" }
+sudo apt update
+sudo apt install unzip -y
+```
+
+Download the zip file with `curl`, extract the installer, and run it.
+
+```bash { target="ubuntu:latest" }
+curl "https://awscli.amazonaws.com/awscli-exe-linux-aarch64.zip" -o "awscliv2.zip"
+unzip awscliv2.zip
+sudo ./aws/install --update
+```
+
+Confirm the CLI version 2 is available by invoking the `aws` command to print the version.
+
+```bash { target="ubuntu:latest" }
+aws --version
+```
+
+Review [AWS CLI v2 is now generally available](https://aws.amazon.com/blogs/developer/aws-cli-v2-is-now-generally-available/) to review the new features in version 2.
+
+You now have the latest version of the AWS CLI installed. Follow [this guide](/install-guides/aws_access_keys/) to generate and configure access keys needed to use the AWS CLI.

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md

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+---
+title: Build a Privacy-First LLM Smart Home on Raspberry Pi 5
+
+minutes_to_complete: 45
+
+who_is_this_for: Anyone who wants a private, cloud-free smart home powered by GenAI on Arm
+
+learning_objectives:
+  - "Deploy a local Large Language Model (LLM) for smart home control"
+  - "Integrate natural language processing with GPIO control"
+  - "Build and run everything on Arm-based single-board computers (no cloud required)"
+  - "Optimize for speed, privacy, and offline operation"
+  - "Create an interactive web dashboard for smart home control"
+prerequisites:
+  - "Basic Python knowledge"
+  - "A text editor (e.g., VS Code, Sublime, Notepad++)"
+  - "An Arm-based single board computer (e.g., Raspberry Pi 5 with at least 8GB RAM)"
+  - "Basic electronic components such as LEDs, sensors, and relays"
+  - "Basic understanding of GPIO pins and electronics"
+
+author: "Fidel Makatia Omusilibwa"
+
+### Tags
+skilllevels: "Introductory"
+subjects: "ML"
+armips:
+  - "Arm Cortex A"
+tools_software_languages:
+  - "Python"
+  - "Ollama"
+  - "gpiozero"
+  - "lgpio"
+  - "FastAPI"
+  - "VS Code or your preferred code editor"
+  - "Raspberry Pi OS (64-bit)"
+operatingsystems:
+  - "Windows , Linux, MacOS"
+
+further_reading:
+  - resource:
+      title: "Raspberry Pi 5 Smart Home Assistant with EdgeAI"
+      link: "https://github.com/fidel-makatia/EdgeAI_Raspi5"
+      type: "source"
+  - resource:
+      title: "Ollama Python/JavaScript Libraries"
+      link: "https://ollama.com/blog/python-javascript-libraries"
+      type: "documentation"
+  - resource:
+      title: "gpiozero Documentation for Raspberry Pi"
+      link: "https://gpiozero.readthedocs.io/en/stable/"
+      type: "documentation"
+  - resource:
+      title: "lgpio Library for Raspberry Pi 5"
+      link: "https://abyz.me.uk/lg/lgpio.html"
+      type: "documentation"
+  - resource:
+      title: "Raspberry Pi 5 Official Documentation"
+      link: "https://www.raspberrypi.org/documentation/computers/raspberry-pi.html"
+      type: "documentation"
+  - resource:
+      title: "Ollama Model Library"
+      link: "https://ollama.com/library"
+      type: "documentation"
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1 # _index.md always has weight of 1 to order correctly
+layout: "learningpathall" # All files under learning paths have this same wrapper
+learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_next-steps.md

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+---
+# ================================================================================
+#       FIXED, DO NOT MODIFY THIS FILE
+# ================================================================================
+weight: 21                  # Set to always be larger than the content in this path to be at the end of the navigation.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/how-to-1.md

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+---
+title: Overview
+weight: 2
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Overview
+
+Control your smart home using natural language with no cloud connection, no third-party servers, and no compromises on privacy. With rapid advances in Generative AI and the power of Arm Cortex-A processors, you can now run large language models (LLMs) directly in your home on the Raspberry Pi 5.
+
+You will create a fully local, privacy-first smart home system that leverages the strengths of Arm Cortex-A architecture. The system achieves 15+ tokens per second inference speeds using optimized models like TinyLlama and Qwen, while maintaining the energy efficiency that makes Arm processors ideal for always-on applications.
+
+## Why Arm Cortex-A for Edge AI?
+
+The Raspberry Pi 5's Arm Cortex-A76 processor excels at high-performance computing tasks like AI inference through:
+
+- Superscalar architecture that executes multiple instructions simultaneously
+- Advanced SIMD with 128-bit NEON units for matrix operations
+- Multi-level cache hierarchy that reduces memory latency
+- Thermal efficiency that maintains performance in compact form factors
+
+Your Arm-powered smart home processes everything locally, providing:
+
+- **Total Privacy**: Conversations and routines never leave your device
+- **Lightning Speed**: Sub-100ms response times with optimized processing
+- **Rock-Solid Reliability**: Operation continues when internet connectivity fails
+- **Unlimited Customization**: Complete control over AI models and automations
+- **Future-Proof Performance**: Continued optimization through Arm's roadmap
+
+## Performance Benchmarks on Raspberry Pi 5
+
+| Model               | Tokens/Sec | Avg Latency (ms) | Performance Rating   |
+| ------------------- | ---------- | ---------------- | -------------------- |
+| qwen:0.5b           | 17.0       | 8,217            | ⭐⭐⭐⭐⭐ Excellent |
+| tinyllama:1.1b      | 12.3       | 9,429            | ⭐⭐⭐⭐⭐ Excellent |
+| deepseek-coder:1.3b | 7.3        | 22,503           | ⭐⭐⭐⭐ Very Good   |
+| gemma2:2b           | 4.1        | 23,758           | ⭐⭐⭐⭐ Very Good   |
+| deepseek-r1:7b      | 1.6        | 64,797           | ⭐⭐⭐ Good          |
+
+Performance insights:
+
+- Qwen 0.5B and TinyLlama 1.1B provide optimal speed for real-time smart home commands
+- DeepSeek-Coder 1.3B and Gemma2 2B handle complex automation tasks effectively
+- DeepSeek-R1 7B offers advanced reasoning capabilities with acceptable latency
+
+## Arm Ecosystem Advantages
+
+The Raspberry Pi 5 benefits from the extensive Arm developer ecosystem:
+
+- Optimized compilers including GCC and Clang with Arm-specific enhancements
+- Native libraries such as gpiozero and lgpio optimized for Raspberry Pi
+- Community support from millions of developers contributing Arm-optimized code
+- Long-term support through Arm's commitment to backward compatibility
+- Industrial adoption with the same architecture powering smartphones, servers, and embedded systems
+
+## Supported Arm-Powered Devices
+
+This learning path focuses on the Raspberry Pi 5, but you can adapt the concepts and code to other Arm-powered devices:
+
+### Recommended Platforms
+
+**Raspberry Pi 5 (Primary Focus)**
+
+- Arm Cortex-A76 quad-core @ 2.4GHz
+- Up to 16GB RAM for larger models
+- Native lgpio support with optimized GPIO performance
+
+**Raspberry Pi 4**
+
+- Arm Cortex-A72 quad-core @ 1.8GHz
+- 8GB RAM maximum, suitable for smaller models
+- Proven compatibility with gpiozero ecosystem
+
+### Compatibility Requirements
+
+Any Arm device can potentially run this project with:
+
+- Arm Cortex-A processor
+- Minimum 4GB RAM (8GB+ recommended)
+- GPIO pins for hardware control
+- Python 3.8+ support
+- Ability to run Ollama
+
+If your Arm device supports Linux, Python, and has GPIO capabilities, you can adapt this learning path to your specific hardware.
+
+## What You Will Build
+
+By completing this learning path, your Raspberry Pi 5 will run:
+
+- Ultra-fast AI processing with 15+ tokens/second performance
+- Complete GPIO control for lights, fans, locks, and sensors via gpiozero + lgpio
+- Modern web dashboard with FastAPI-powered interface optimized for mobile
+- NEON-accelerated performance using custom ARM assembly for critical paths
+- Zero-cloud architecture with everything running locally on your Arm processor
+- Intelligent automation with scene-based control using natural language
+
+You will build a smart home system that demonstrates why Arm processors represent the future of edge computing, combining efficiency, performance, and complete privacy control.