How to Build a Voice Assistant with Local LLaMA on a $60 Mini-PC

Imagine having your very own AI voice assistant that runs entirely offline, respects your privacy, and costs less than dinner for two. Sounds like a futuristic dream? Not anymore. With advances in lightweight large language models (LLMs) like LLaMA and budget-friendly mini-PCs, it’s totally possible to build a powerful, private voice assistant for under $60.

This guide will walk you through building your own local voice assistant using Meta’s LLaMA model on a mini-PC like the Orange Pi 5 or an Intel N100-based box. We’ll keep things simple, affordable, and focused on users in the US and UK who value privacy and DIY tech solutions.

Why Local AI Voice Assistants Are Taking Off

Voice assistants like Alexa and Google Assistant are useful, but they come with major trade-offs: cloud dependency, constant listening, and privacy concerns. That’s where local LLMs like LLaMA shine.

Key advantages:

• No internet required for processing
• Full control over your data
• Customizable to your specific needs
• Surprisingly fast, even on budget hardware

What You’ll Need (Hardware & Software)

Budget Mini-PC Options (~$60)

Here’s a quick comparison of devices that can handle small LLaMA models:

Mini-PC Model	Price (USD)	RAM	Performance	Best For
Orange Pi 5 (4GB)	~$60	4GB LPDDR4	Great	Local AI + GPIO tasks
Intel N100 Mini PC	~$90	8GB DDR4	Excellent	LLaMA + multitasking
Raspberry Pi 4 (4GB)	~$70	4GB LPDDR4	Moderate	Barebones setup

Tip: For this guide, we’ll use the Orange Pi 5 as the baseline hardware.

Software Stack

• Linux OS (Armbian or Ubuntu preferred)
• Whisper.cpp (for speech-to-text)
• LLaMA.cpp (for local language inference)
• TTS Engine (like Piper or Coqui TTS)
• Python + Node.js (for orchestration)

Step-by-Step Setup Guide

1. Install the OS

Flash Armbian or Ubuntu to your mini-PC’s SD card or eMMC storage using Balena Etcher. Boot up and complete the initial setup.

sudo apt update && sudo apt upgrade

2. Set Up Voice-to-Text (Speech Recognition)

Install Whisper.cpp, a lightweight open-source voice-to-text engine.

git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
make

Record your voice and transcribe using:

./main -m models/ggml-base.en.bin -f samples/voice.wav

You can hook a USB mic and capture audio in real-time using ffmpeg or arecord.

3. Install LLaMA Locally (llama.cpp)

Use LLaMA.cpp, an optimized C++ version of Meta’s LLaMA for CPUs.

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

Convert and download a small quantized LLaMA model (3B or smaller):

python3 convert.py models/llama-3b/ --outtype q4_0

Run a test prompt:

./main -m models/llama-3b/ggml-model-q4_0.bin -p "How's the weather today?"

Note: On a $60 mini-PC, stick to quantized 3B or smaller models for optimal speed.

4. Add Text-to-Speech (TTS)

Turn responses into voice using Piper TTS.

git clone https://github.com/rhasspy/piper
cd piper
make

Test it:

echo "Hello, I am your private assistant." | ./piper --model en_US-libritts-high

You can route audio to the mini-PC’s 3.5mm jack or a USB speaker.

5. Combine Components into One Workflow

Now that you have:

• Voice-to-text via Whisper
• LLM response via LLaMA.cpp
• Speech output via Piper

You can chain them with a Python or Node.js script.

Sample Workflow in Python:

import os

os.system("arecord -d 5 -f cd voice.wav")
os.system("./whisper.cpp/main -m models/ggml-base.en.bin -f voice.wav > text.txt")

with open("text.txt") as f:
    prompt = f.read()

os.system(f"./llama.cpp/main -m models/llama-3b/ggml-model-q4_0.bin -p \"{prompt}\" > response.txt")

with open("response.txt") as f:
    reply = f.read()

os.system(f"echo '{reply}' | ./piper/piper --model en_US-libritts-high")

You now have an offline AI voice assistant running fully locally!

Real-World Use Case: A Voice Scheduler for Elderly Care

One UK developer built this setup into a voice reminder assistant for his grandmother. It wakes up at specific hours, reminds her to take medicine, and responds to basic questions like “What day is it?”

No cloud. No surveillance. Just a helpful voice that respects privacy.

Performance Tips

• Reduce prompt size to minimize inference delay
• Use quantized models (q4_0 or q5_1) for speed
• Consider using a fan-cooled mini-PC if running long sessions
• Optimize audio I/O with low-latency USB soundcards

Extra Features You Can Add

• Wake Word Detection: Use Porcupine or Snowboy (“Hey Nova”)
• GPIO Integration: Control lights, fans, or coffee machines via GPIO on Orange Pi
• Chat History: Store past prompts and replies for context-aware conversations

Why This Matters in 2025

In an age of constant data collection, offline voice assistants offer:

• A privacy-first alternative to Amazon/Google models
• Lower latency (no internet round-trips)
• Total customization

And with open-source tools and low-cost hardware, it’s more accessible than ever.

You don’t need a $1,000 rig or a cloud server to get LLM-powered voice assistants. With just $60, some open-source magic, and a bit of tinkering, you can run a fully offline AI system tailored to your needs. Whether you’re in Los Angeles or London, this project puts real AI in your hands and keeps Big Tech out.