Local LLM - Offline AI with llama.cpp

Run AI inference completely offline on embedded devices. No internet, no API costs, full privacy.

Overview

Recommended Models

By Device RAM

By Use Case

Simple Q&A, Commands:

TinyLlama 1.1B - Fast, basic understanding

Customer Service, FAQ:

Phi-2 or Gemma 2B - Good comprehension, reasonable speed

Complex Reasoning:

Llama 3.2 3B or Mistral 7B - Better accuracy, slower

Installation

Automatic (via deploy script)

./scripts/deploy-embedded.sh pi@device --with-llama

Manual Installation

# SSH to device
ssh pi@raspberrypi.local

# Install dependencies
sudo apt update
sudo apt install -y build-essential cmake git wget

# Clone llama.cpp
cd /opt
sudo git clone https://github.com/ggerganov/llama.cpp
sudo chown -R $(whoami):$(whoami) llama.cpp
cd llama.cpp

# Build for ARM (auto-optimizes)
mkdir build && cd build
cmake .. -DLLAMA_NATIVE=ON -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)

# Download model
mkdir -p /opt/llama.cpp/models
cd /opt/llama.cpp/models
wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf

Start Server

# Test run
/opt/llama.cpp/build/bin/llama-server \
    -m /opt/llama.cpp/models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -c 2048 \
    --threads 4

# Verify
curl http://localhost:8080/v1/models

Systemd Service

Create /etc/systemd/system/llama-server.service:

[Unit]
Description=llama.cpp Server - Local LLM
After=network.target

[Service]
Type=simple
User=root
WorkingDirectory=/opt/llama.cpp
ExecStart=/opt/llama.cpp/build/bin/llama-server \
    -m /opt/llama.cpp/models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -c 2048 \
    -ngl 0 \
    --threads 4
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target

Enable and start:

sudo systemctl daemon-reload
sudo systemctl enable llama-server
sudo systemctl start llama-server

Configuration

botserver .env

# Use local llama.cpp
LLM_PROVIDER=llamacpp
LLM_API_URL=http://127.0.0.1:8080
LLM_MODEL=tinyllama

# Memory limits
MAX_CONTEXT_TOKENS=2048
MAX_RESPONSE_TOKENS=512
STREAMING_ENABLED=true

llama.cpp Parameters

Memory vs Context Size

Context 512:  ~400MB RAM, fast, limited conversation
Context 1024: ~600MB RAM, moderate
Context 2048: ~900MB RAM, good for most uses
Context 4096: ~1.5GB RAM, long conversations

Performance Optimization

CPU Optimization

# Check CPU features
cat /proc/cpuinfo | grep -E "(model name|Features)"

# Build with specific optimizations
cmake .. -DLLAMA_NATIVE=ON \
         -DCMAKE_BUILD_TYPE=Release \
         -DLLAMA_ARM_FMA=ON \
         -DLLAMA_ARM_DOTPROD=ON

Memory Optimization

# For 2GB RAM devices
# Use smaller context
-c 1024

# Use memory mapping (slower but less RAM)
--mmap

# Disable mlock (don't pin to RAM)
# (default is disabled)

Swap Configuration

For devices with limited RAM:

# Create 2GB swap
sudo fallocate -l 2G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

# Make permanent
echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab

# Optimize swap usage
echo 'vm.swappiness=10' | sudo tee -a /etc/sysctl.conf

NPU Acceleration (Orange Pi 5)

Orange Pi 5 has a 6 TOPS NPU that can accelerate inference:

Using rkllm (Rockchip NPU)

# Install rkllm runtime
git clone https://github.com/airockchip/rknn-llm
cd rknn-llm
./install.sh

# Convert model to RKNN format
python3 convert_model.py \
    --model tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
    --output tinyllama.rkllm

# Run with NPU
rkllm-server \
    --model tinyllama.rkllm \
    --port 8080

Expected speedup: 3-5x faster than CPU only.

Model Download URLs

TinyLlama 1.1B (Recommended for 2GB)

wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf

Phi-2 2.7B (Recommended for 4GB)

wget https://huggingface.co/TheBloke/phi-2-GGUF/resolve/main/phi-2.Q4_K_M.gguf

Gemma 2B

wget https://huggingface.co/bartowski/gemma-2-2b-it-GGUF/resolve/main/gemma-2-2b-it-Q4_K_M.gguf

Llama 3.2 3B (Recommended for 8GB)

wget https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/Llama-3.2-3B-Instruct-Q4_K_M.gguf

Mistral 7B

wget https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf

API Usage

llama.cpp exposes an OpenAI-compatible API:

Chat Completion

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "tinyllama",
    "messages": [
      {"role": "user", "content": "What is 2+2?"}
    ],
    "max_tokens": 100
  }'

Streaming

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "tinyllama",
    "messages": [{"role": "user", "content": "Tell me a story"}],
    "stream": true
  }'

Health Check

curl http://localhost:8080/health
curl http://localhost:8080/v1/models

Monitoring

Check Performance

# Watch resource usage
htop

# Check inference speed in logs
sudo journalctl -u llama-server -f | grep "tokens/s"

# Memory usage
free -h

Benchmarking

# Run llama.cpp benchmark
/opt/llama.cpp/build/bin/llama-bench \
    -m /opt/llama.cpp/models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
    -p 512 -n 128 -t 4

Troubleshooting

Model Loading Fails

# Check available RAM
free -h

# Try smaller context
-c 512

# Use memory mapping
--mmap

Slow Inference

# Increase threads (up to CPU cores)
--threads $(nproc)

# Use optimized build
cmake .. -DLLAMA_NATIVE=ON

# Consider smaller model

Out of Memory Killer

# Check if OOM killed the process
dmesg | grep -i "killed process"

# Increase swap
# Use smaller model
# Reduce context size

Best Practices

1. Start small - Begin with TinyLlama, upgrade if needed
2. Monitor memory - Use htop during initial tests
3. Set appropriate context - 1024-2048 for most embedded use
4. Use quantized models - Q4_K_M is a good balance
5. Enable streaming - Better UX on slow inference
6. Test offline - Verify it works without internet before deployment