Linux Local AI Setup: Ubuntu, Debian & Arch (2025 Complete Guide)

Published on January 30, 2025 • 25 min read

Quick Summary:

• ✅ Install local AI on any Linux distribution
• ✅ GPU acceleration setup (NVIDIA, AMD, Intel)
• ✅ Docker and systemd service configuration
• ✅ Performance optimization and monitoring
• ✅ Troubleshooting for all major distros

Linux is the ultimate platform for local AI development. With superior hardware control, package management flexibility, and native Docker support, Linux offers unmatched performance and customization options for running AI models locally. This comprehensive guide covers everything from basic installation to advanced optimization across all major distributions.

Table of Contents

1. Why Linux for Local AI
2. Distribution-Specific Installation
3. GPU Drivers and Acceleration
4. Docker Setup and Management
5. Systemd Service Configuration
6. Performance Optimization
7. Monitoring and Logging
8. Terminal Customization
9. Security Considerations
10. Troubleshooting Guide

---

Why Linux for Local AI {#why-linux-ai}

Linux Advantages for AI:

1. Hardware Control

• Direct access to GPU memory and drivers
• Fine-grained CPU scheduling and affinity
• Custom kernel parameters for optimization
• Real-time process prioritization

2. Package Management

• Multiple installation methods (package managers, source, containers)
• Dependency resolution and conflict handling
• Easy updates and rollbacks
• Community repositories with latest versions

3. Resource Efficiency

• Minimal overhead compared to Windows/macOS
• No unnecessary background services
• Precise memory and CPU allocation
• Swap and cache optimization

4. Development Environment

• Native Docker and container support
• Advanced shell scripting capabilities
• Comprehensive development tools
• Superior debugging and profiling

---

Distribution-Specific Installation {#distro-installation}

Ubuntu/Debian Installation

Method 1: Official Repository (Recommended)

# Ubuntu 22.04+ / Debian 12+
curl -fsSL https://ollama.com/install.sh | sh

# Verify installation
ollama --version
systemctl status ollama

Method 2: Manual Installation

# Download latest release
curl -L https://github.com/ollama/ollama/releases/latest/download/ollama-linux-amd64.tgz -o ollama.tgz

# Extract
sudo tar -C /usr/local/bin -xzf ollama.tgz

# Make executable
sudo chmod +x /usr/local/bin/ollama

# Create systemd service
sudo tee /etc/systemd/system/ollama.service > /dev/null <<EOF
[Unit]
Description=Ollama Service
After=network-online.target

[Service]
ExecStart=/usr/local/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
Environment="OLLAMA_HOST=0.0.0.0:11434"

[Install]
WantedBy=default.target
EOF

# Create ollama user
sudo useradd -r -s /bin/false -d /usr/share/ollama -m ollama

# Start service
sudo systemctl daemon-reload
sudo systemctl enable ollama
sudo systemctl start ollama

Method 3: APT Repository

# Add Ollama repository (Ubuntu/Debian)
curl -fsSL https://packages.ollama.com/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/ollama.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/ollama.gpg] https://packages.ollama.com/apt stable main" | sudo tee /etc/apt/sources.list.d/ollama.list

# Update and install
sudo apt update
sudo apt install ollama

# Start service
sudo systemctl enable --now ollama

Fedora/CentOS/RHEL Installation

Method 1: DNF Installation

# Fedora 38+
sudo dnf install -y curl
curl -fsSL https://ollama.com/install.sh | sh

# CentOS/RHEL with EPEL
sudo dnf install -y epel-release
sudo dnf install -y curl
curl -fsSL https://ollama.com/install.sh | sh

Method 2: RPM Repository

# Add Ollama repository
sudo tee /etc/yum.repos.d/ollama.repo > /dev/null <<EOF
[ollama]
name=Ollama Repository
baseurl=https://packages.ollama.com/rpm/$basearch/
enabled=1
gpgcheck=1
gpgkey=https://packages.ollama.com/gpg
EOF

# Install
sudo dnf install ollama

# Enable service
sudo systemctl enable --now ollama

Arch Linux Installation

Method 1: AUR (Recommended)

# Using yay
yay -S ollama

# Using paru
paru -S ollama

# Manual AUR installation
git clone https://aur.archlinux.org/ollama.git
cd ollama
makepkg -si

Method 2: Manual Installation

# Install dependencies
sudo pacman -S curl base-devel

# Download and install
curl -L https://github.com/ollama/ollama/releases/latest/download/ollama-linux-amd64.tgz -o ollama.tgz
sudo tar -C /usr/local/bin -xzf ollama.tgz
sudo chmod +x /usr/local/bin/ollama

# Create systemd service (same as Ubuntu method above)

openSUSE Installation

# openSUSE Leap/Tumbleweed
sudo zypper install curl
curl -fsSL https://ollama.com/install.sh | sh

# Or build from source
sudo zypper install go gcc-c++ cmake
git clone https://github.com/ollama/ollama.git
cd ollama
go generate ./...
go build .
sudo mv ollama /usr/local/bin/

---

GPU Drivers and Acceleration {#gpu-acceleration}

NVIDIA GPU Setup

Driver Installation

# Ubuntu/Debian
sudo apt update
sudo apt install nvidia-driver-535 nvidia-utils-535

# Fedora
sudo dnf install akmod-nvidia xorg-x11-drv-nvidia-cuda

# Arch Linux
sudo pacman -S nvidia nvidia-utils

# Verify installation
nvidia-smi

CUDA Toolkit Installation

# Ubuntu 22.04
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt update
sudo apt install cuda-toolkit-12-3

# Fedora
sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
sudo dnf install cuda-toolkit-12-3

# Add to PATH
echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc

Configure Ollama for NVIDIA

# Set environment variables
sudo tee -a /etc/systemd/system/ollama.service.d/override.conf > /dev/null <<EOF
[Service]
Environment="NVIDIA_VISIBLE_DEVICES=all"
Environment="NVIDIA_DRIVER_CAPABILITIES=compute,utility"
Environment="OLLAMA_NUM_GPU=1"
EOF

# Reload and restart
sudo systemctl daemon-reload
sudo systemctl restart ollama

# Verify GPU usage
ollama run llama3.2 "test" &
nvidia-smi

AMD GPU Setup (ROCm)

ROCm Installation

# Ubuntu
wget -q -O - https://repo.radeon.com/rocm/rocm.gpg.key | sudo apt-key add -
echo 'deb [arch=amd64] https://repo.radeon.com/rocm/apt/debian/ ubuntu main' | sudo tee /etc/apt/sources.list.d/rocm.list
sudo apt update
sudo apt install rocm-dev rocm-libs hip-dev

# Add user to render group
sudo usermod -a -G render,video $USER

# Reboot required
sudo reboot

Configure Ollama for AMD

# Set ROCm environment
sudo tee -a /etc/systemd/system/ollama.service.d/override.conf > /dev/null <<EOF
[Service]
Environment="HSA_OVERRIDE_GFX_VERSION=10.3.0"
Environment="OLLAMA_GPU_TYPE=rocm"
Environment="ROCM_PATH=/opt/rocm"
EOF

sudo systemctl daemon-reload
sudo systemctl restart ollama

Intel GPU Setup

# Install Intel GPU tools
sudo apt install intel-gpu-tools mesa-utils

# For Intel Arc GPUs
sudo apt install intel-level-zero-gpu level-zero-dev

# Configure Ollama
sudo tee -a /etc/systemd/system/ollama.service.d/override.conf > /dev/null <<EOF
[Service]
Environment="OLLAMA_GPU_TYPE=intel"
Environment="INTEL_DEVICE=/dev/dri/renderD128"
EOF

sudo systemctl daemon-reload
sudo systemctl restart ollama

---

Docker Setup and Management {#docker-setup}

Docker Installation

# Ubuntu/Debian
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker $USER

# Fedora
sudo dnf install docker docker-compose
sudo systemctl enable --now docker
sudo usermod -aG docker $USER

# Arch Linux
sudo pacman -S docker docker-compose
sudo systemctl enable --now docker
sudo usermod -aG docker $USER

# Logout and login to apply group changes

Ollama Docker Setup

Basic Docker Run

# CPU-only
docker run -d   --name ollama   -p 11434:11434   -v ollama:/root/.ollama   --restart unless-stopped   ollama/ollama:latest

# With NVIDIA GPU
docker run -d   --name ollama   --gpus all   -p 11434:11434   -v ollama:/root/.ollama   --restart unless-stopped   ollama/ollama:latest

# With AMD GPU (ROCm)
docker run -d   --name ollama   --device /dev/kfd --device /dev/dri   --group-add video   -p 11434:11434   -v ollama:/root/.ollama   --restart unless-stopped   ollama/ollama:rocm

Docker Compose Configuration

# docker-compose.yml
version: '3.8'

services:
  ollama:
    image: ollama/ollama:latest
    container_name: ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
      - ./models:/models
    environment:
      - OLLAMA_HOST=0.0.0.0:11434
      - OLLAMA_MODELS=/models
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

  ollama-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: ollama-webui
    ports:
      - "3000:8080"
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
      - WEBUI_SECRET_KEY=your-secret-key
    volumes:
      - webui_data:/app/backend/data
    depends_on:
      - ollama
    restart: unless-stopped

volumes:
  ollama_data:
  webui_data:

Docker Management Scripts

# Create management script
sudo tee /usr/local/bin/ollama-docker > /dev/null <<'EOF'
#!/bin/bash

case "$1" in
  start)
    docker-compose -f /opt/ollama/docker-compose.yml up -d
    ;;
  stop)
    docker-compose -f /opt/ollama/docker-compose.yml down
    ;;
  restart)
    docker-compose -f /opt/ollama/docker-compose.yml restart
    ;;
  logs)
    docker-compose -f /opt/ollama/docker-compose.yml logs -f
    ;;
  update)
    docker-compose -f /opt/ollama/docker-compose.yml pull
    docker-compose -f /opt/ollama/docker-compose.yml up -d
    ;;
  *)
    echo "Usage: $0 {start|stop|restart|logs|update}"
    exit 1
    ;;
esac
EOF

sudo chmod +x /usr/local/bin/ollama-docker

---

Systemd Service Configuration {#systemd-configuration}

Advanced Service Configuration

# Create service override directory
sudo mkdir -p /etc/systemd/system/ollama.service.d/

# Advanced configuration
sudo tee /etc/systemd/system/ollama.service.d/override.conf > /dev/null <<EOF
[Unit]
Description=Ollama Service - Local AI Language Model Server
Documentation=https://ollama.com/docs
After=network-online.target
Wants=network-online.target

[Service]
Type=exec
ExecStart=/usr/local/bin/ollama serve
ExecReload=/bin/kill -HUP $MAINPID
KillMode=mixed
KillSignal=SIGINT
TimeoutStopSec=30
Restart=always
RestartSec=5
User=ollama
Group=ollama

# Security settings
NoNewPrivileges=true
PrivateTmp=true
ProtectSystem=strict
ProtectHome=true
ReadWritePaths=/usr/share/ollama
ProtectKernelTunables=true
ProtectKernelModules=true
ProtectControlGroups=true

# Resource limits
LimitNOFILE=65536
LimitNPROC=4096
MemoryMax=16G
CPUQuota=800%

# Environment
Environment="PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_MODELS=/usr/share/ollama/.ollama/models"
Environment="OLLAMA_NUM_PARALLEL=1"
Environment="OLLAMA_MAX_LOADED_MODELS=3"
Environment="OLLAMA_KEEP_ALIVE=5m"

[Install]
WantedBy=multi-user.target
EOF

# Reload and restart
sudo systemctl daemon-reload
sudo systemctl restart ollama
sudo systemctl status ollama

Service Monitoring Script

# Create monitoring script
sudo tee /usr/local/bin/ollama-monitor > /dev/null <<'EOF'
#!/bin/bash

LOGFILE="/var/log/ollama-monitor.log"
THRESHOLD_CPU=80
THRESHOLD_MEM=90

log_message() {
    echo "$(date '+%Y-%m-%d %H:%M:%S') - $1" >> "$LOGFILE"
}

check_service() {
    if ! systemctl is-active --quiet ollama; then
        log_message "ERROR: Ollama service is down, attempting restart"
        systemctl restart ollama
        sleep 10
        if systemctl is-active --quiet ollama; then
            log_message "INFO: Ollama service restarted successfully"
        else
            log_message "ERROR: Failed to restart Ollama service"
        fi
    fi
}

check_resources() {
    local cpu_usage=$(ps -C ollama -o %cpu --no-headers | awk '{sum+=$1} END {print sum}')
    local mem_usage=$(ps -C ollama -o %mem --no-headers | awk '{sum+=$1} END {print sum}')

    if (( $(echo "$cpu_usage > $THRESHOLD_CPU" | bc -l) )); then
        log_message "WARNING: High CPU usage: ${cpu_usage}%"
    fi

    if (( $(echo "$mem_usage > $THRESHOLD_MEM" | bc -l) )); then
        log_message "WARNING: High memory usage: ${mem_usage}%"
    fi
}

check_service
check_resources
EOF

sudo chmod +x /usr/local/bin/ollama-monitor

# Add to crontab
(crontab -l 2>/dev/null; echo "*/5 * * * * /usr/local/bin/ollama-monitor") | crontab -

---

Performance Optimization {#performance-optimization}

CPU Optimization

# Check CPU info
lscpu
cat /proc/cpuinfo | grep -E "(model name|cpu cores|siblings)"

# Set CPU governor for performance
sudo cpupower frequency-set -g performance

# CPU affinity for Ollama
sudo systemctl edit ollama
# Add:
[Service]
CPUAffinity=0-7  # Use cores 0-7
Nice=-10         # Higher priority
IOSchedulingClass=1
IOSchedulingPriority=4

Memory Optimization

# Check memory info
free -h
cat /proc/meminfo

# Optimize swap
sudo sysctl vm.swappiness=10
sudo sysctl vm.vfs_cache_pressure=50

# Make permanent
echo 'vm.swappiness=10' | sudo tee -a /etc/sysctl.conf
echo 'vm.vfs_cache_pressure=50' | sudo tee -a /etc/sysctl.conf

# Huge pages for large models
sudo sysctl vm.nr_hugepages=1024
echo 'vm.nr_hugepages=1024' | sudo tee -a /etc/sysctl.conf

# Memory limits for Ollama
sudo systemctl edit ollama
# Add:
[Service]
MemoryMax=12G
MemoryHigh=10G

Storage Optimization

# Check disk performance
sudo hdparm -t /dev/sda  # Replace with your disk

# SSD optimization
sudo echo 'noop' > /sys/block/sda/queue/scheduler

# Mount optimizations for model storage
sudo mkdir -p /opt/ollama-models
sudo mount -o noatime,nodiratime /dev/disk/by-label/MODELS /opt/ollama-models

# Add to fstab
echo '/dev/disk/by-label/MODELS /opt/ollama-models ext4 noatime,nodiratime,defaults 0 0' | sudo tee -a /etc/fstab

Network Optimization

# TCP optimization for model downloads
sudo sysctl net.core.rmem_max=67108864
sudo sysctl net.core.wmem_max=67108864
sudo sysctl net.ipv4.tcp_rmem="4096 87380 67108864"
sudo sysctl net.ipv4.tcp_wmem="4096 65536 67108864"

# Make permanent
echo 'net.core.rmem_max=67108864' | sudo tee -a /etc/sysctl.conf
echo 'net.core.wmem_max=67108864' | sudo tee -a /etc/sysctl.conf
echo 'net.ipv4.tcp_rmem=4096 87380 67108864' | sudo tee -a /etc/sysctl.conf
echo 'net.ipv4.tcp_wmem=4096 65536 67108864' | sudo tee -a /etc/sysctl.conf

---

Monitoring and Logging {#monitoring-logging}

System Monitoring

# Install monitoring tools
# Ubuntu/Debian
sudo apt install htop iotop nethogs nvtop

# Fedora
sudo dnf install htop iotop nethogs nvtop

# Arch
sudo pacman -S htop iotop nethogs nvtop

# Create monitoring dashboard script
cat > ~/ollama-status.sh << 'EOF'
#!/bin/bash
clear
echo "=== Ollama System Status ==="
echo "Service Status:"
systemctl status ollama --no-pager -l

echo -e "
CPU and Memory:"
ps aux | grep ollama | grep -v grep

echo -e "
GPU Status:"
if command -v nvidia-smi &> /dev/null; then
    nvidia-smi
elif command -v rocm-smi &> /dev/null; then
    rocm-smi
fi

echo -e "
Disk Usage:"
df -h ~/.ollama

echo -e "
Network Connections:"
sudo netstat -tlnp | grep 11434

echo -e "
Recent Logs:"
journalctl -u ollama --no-pager -n 10
EOF

chmod +x ~/ollama-status.sh

Advanced Logging

# Configure structured logging
sudo mkdir -p /var/log/ollama
sudo chown ollama:ollama /var/log/ollama

# Create logrotate configuration
sudo tee /etc/logrotate.d/ollama > /dev/null <<EOF
/var/log/ollama/*.log {
    daily
    rotate 7
    compress
    delaycompress
    missingok
    notifempty
    create 0644 ollama ollama
    postrotate
        systemctl reload ollama > /dev/null 2>&1 || true
    endscript
}
EOF

# Update systemd service for logging
sudo systemctl edit ollama
# Add:
[Service]
StandardOutput=append:/var/log/ollama/ollama.log
StandardError=append:/var/log/ollama/ollama-error.log

Performance Metrics Collection

# Create metrics collection script
sudo tee /usr/local/bin/ollama-metrics > /dev/null <<'EOF'
#!/bin/bash

METRICS_FILE="/var/log/ollama/metrics.log"
DATE=$(date '+%Y-%m-%d %H:%M:%S')

# CPU usage
CPU_USAGE=$(top -bn1 | grep "Cpu(s)" | awk '{print $2}' | sed 's/%us,//')

# Memory usage
MEM_USAGE=$(free | grep Mem | awk '{printf("%.1f"), $3/$2 * 100.0}')

# GPU usage (if NVIDIA)
if command -v nvidia-smi &> /dev/null; then
    GPU_USAGE=$(nvidia-smi --query-gpu=utilization.gpu --format=csv,noheader,nounits)
    GPU_MEMORY=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits)
else
    GPU_USAGE="N/A"
    GPU_MEMORY="N/A"
fi

# Disk I/O
DISK_READ=$(iostat -d 1 2 | tail -n +4 | awk '{sum+=$3} END {print sum}')
DISK_WRITE=$(iostat -d 1 2 | tail -n +4 | awk '{sum+=$4} END {print sum}')

# Network I/O
NET_RX=$(cat /proc/net/dev | grep eth0 | awk '{print $2}')
NET_TX=$(cat /proc/net/dev | grep eth0 | awk '{print $10}')

echo "$DATE,CPU:$CPU_USAGE,MEM:$MEM_USAGE,GPU:$GPU_USAGE,GPU_MEM:$GPU_MEMORY,DISK_R:$DISK_READ,DISK_W:$DISK_WRITE,NET_RX:$NET_RX,NET_TX:$NET_TX" >> "$METRICS_FILE"
EOF

sudo chmod +x /usr/local/bin/ollama-metrics

# Run every minute
(crontab -l 2>/dev/null; echo "* * * * * /usr/local/bin/ollama-metrics") | crontab -

---

Terminal Customization {#terminal-customization}

Bash Configuration

# Add to ~/.bashrc
cat >> ~/.bashrc << 'EOF'

# Ollama aliases
alias ai="ollama run llama3.2"
alias ai-code="ollama run codellama"
alias ai-list="ollama list"
alias ai-ps="ollama ps"
alias ai-rm="ollama rm"
alias ai-pull="ollama pull"
alias ai-status="systemctl status ollama"
alias ai-logs="journalctl -u ollama -f"
alias ai-monitor="watch -n 1 'ollama ps && echo && free -h && echo && nvidia-smi'"

# Functions
function ai-ask() {
    if [ -z "$1" ]; then
        echo "Usage: ai-ask 'your question'"
        return 1
    fi
    echo "$1" | ollama run llama3.2
}

function ai-explain() {
    if [ -z "$1" ]; then
        echo "Usage: ai-explain <file>"
        return 1
    fi
    cat "$1" | ollama run codellama "Explain this code:"
}

function ai-review() {
    if [ -z "$1" ]; then
        echo "Usage: ai-review <file>"
        return 1
    fi
    cat "$1" | ollama run codellama "Review this code for bugs and improvements:"
}

function ai-translate() {
    if [ -z "$1" ] || [ -z "$2" ]; then
        echo "Usage: ai-translate 'text' 'target language'"
        return 1
    fi
    echo "$1" | ollama run llama3.2 "Translate to $2:"
}

function ai-model-info() {
    if [ -z "$1" ]; then
        echo "Available models:"
        ollama list
        return 0
    fi
    ollama show "$1"
}

# Model size checker
function ai-model-size() {
    echo "Model storage usage:"
    du -h ~/.ollama/models/* 2>/dev/null | sort -hr
    echo
    echo "Total:"
    du -sh ~/.ollama/models/ 2>/dev/null
}

# Quick setup for new models
function ai-setup() {
    echo "🤖 Setting up Local AI environment..."
    echo "Available popular models:"
    echo "1. llama3.2:3b    - Fast, good for general use"
    echo "2. llama3.2:7b    - Balanced performance and quality"
    echo "3. codellama:7b   - Best for programming"
    echo "4. mistral:7b     - Alternative to Llama"
    echo "5. phi3:mini      - Smallest, fastest"

    read -p "Enter model name or number (1-5): " choice
    case $choice in
        1) ollama pull llama3.2:3b ;;
        2) ollama pull llama3.2:7b ;;
        3) ollama pull codellama:7b ;;
        4) ollama pull mistral:7b ;;
        5) ollama pull phi3:mini ;;
        *) ollama pull "$choice" ;;
    esac
}

EOF

source ~/.bashrc

Zsh Configuration

# For Zsh users (oh-my-zsh)
cat >> ~/.zshrc << 'EOF'

# Ollama plugin
plugins=(... ollama)

# Custom prompt with AI status
function ai_status() {
    if systemctl is-active --quiet ollama; then
        echo "🤖"
    else
        echo "💤"
    fi
}

# Add to prompt
RPROMPT='$(ai_status)'

# Same aliases and functions as bash
# (copy from bash section above)

EOF

Fish Shell Configuration

# Fish shell configuration
mkdir -p ~/.config/fish/functions

# Create AI functions
cat > ~/.config/fish/functions/ai.fish << 'EOF'
function ai
    ollama run llama3.2 $argv
end
EOF

cat > ~/.config/fish/functions/ai-status.fish << 'EOF'
function ai-status
    if systemctl is-active --quiet ollama
        echo "🤖 Ollama is running"
        ollama ps
    else
        echo "💤 Ollama is stopped"
    end
end
EOF

---

Security Considerations {#security}

Firewall Configuration

# UFW (Ubuntu/Debian)
sudo ufw enable
sudo ufw allow 22/tcp                    # SSH
sudo ufw allow from 192.168.1.0/24 to any port 11434  # Local network only

# FirewallD (Fedora/CentOS)
sudo firewall-cmd --permanent --add-service=ssh
sudo firewall-cmd --permanent --add-rich-rule='rule family="ipv4" source address="192.168.1.0/24" port protocol="tcp" port="11434" accept'
sudo firewall-cmd --reload

# iptables (manual)
sudo iptables -A INPUT -p tcp --dport 22 -j ACCEPT
sudo iptables -A INPUT -s 192.168.1.0/24 -p tcp --dport 11434 -j ACCEPT
sudo iptables -A INPUT -p tcp --dport 11434 -j DROP

SSL/TLS Configuration

# Generate self-signed certificate
sudo mkdir -p /etc/ollama/ssl
sudo openssl req -x509 -nodes -days 365 -newkey rsa:2048     -keyout /etc/ollama/ssl/ollama.key     -out /etc/ollama/ssl/ollama.crt     -subj "/C=US/ST=State/L=City/O=Organization/CN=localhost"

# Configure Ollama with TLS
sudo systemctl edit ollama
# Add:
[Service]
Environment="OLLAMA_HOST=https://0.0.0.0:11434"
Environment="OLLAMA_TLS_CERT=/etc/ollama/ssl/ollama.crt"
Environment="OLLAMA_TLS_KEY=/etc/ollama/ssl/ollama.key"

Access Control

# Create API key authentication script
sudo tee /usr/local/bin/ollama-auth-proxy > /dev/null <<'EOF'
#!/bin/bash

# Simple API key authentication proxy
VALID_KEY="your-secure-api-key-here"
OLLAMA_URL="http://localhost:11434"

while IFS= read -r line; do
    if [[ "$line" == *"Authorization: Bearer $VALID_KEY"* ]]; then
        # Forward request to Ollama
        curl -s -X POST "$OLLAMA_URL/api/generate"             -H "Content-Type: application/json"             -d "$REQUEST_BODY"
        exit 0
    fi
done

echo "Unauthorized" | nc -l -p 11435
EOF

sudo chmod +x /usr/local/bin/ollama-auth-proxy

---

Troubleshooting Guide {#troubleshooting}

Service Issues

Issue: Service won't start

# Check service status
sudo systemctl status ollama -l

# Check for port conflicts
sudo netstat -tlnp | grep 11434
sudo lsof -i :11434

# Check permissions
ls -la /usr/local/bin/ollama
sudo -u ollama /usr/local/bin/ollama serve  # Test as service user

# Reset service
sudo systemctl stop ollama
sudo systemctl reset-failed ollama
sudo systemctl start ollama

Issue: Permission denied errors

# Fix ownership
sudo chown -R ollama:ollama /usr/share/ollama
sudo chmod 755 /usr/local/bin/ollama

# Check SELinux (CentOS/RHEL/Fedora)
sestatus
sudo setsebool -P httpd_can_network_connect 1
sudo semanage port -a -t http_port_t -p tcp 11434

GPU Issues

Issue: GPU not detected

# Check GPU
lspci | grep -i vga
lspci | grep -i nvidia

# NVIDIA troubleshooting
nvidia-smi
sudo dmesg | grep nvidia

# Check driver loading
lsmod | grep nvidia

# Reinstall drivers
sudo apt purge nvidia-*
sudo apt install nvidia-driver-535

# AMD GPU troubleshooting
rocm-smi
clinfo

Issue: Out of memory errors

# Check memory usage
free -h
sudo dmesg | grep -i "killed process"

# Reduce model size
ollama pull llama3.2:3b-q4_0  # Quantized version

# Increase swap
sudo fallocate -l 8G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile
echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab

Network Issues

Issue: Can't download models

# Check connectivity
curl -I https://ollama.com
nslookup ollama.com

# Check proxy settings
echo $http_proxy $https_proxy

# Use different registry
export OLLAMA_REGISTRY=https://registry.npmmirror.com
ollama pull llama3.2

# Manual download
wget https://huggingface.co/microsoft/DialoGPT-medium/resolve/main/pytorch_model.bin

Performance Issues

Issue: Slow inference

# Check CPU governor
cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sudo cpupower frequency-set -g performance

# Check thermal throttling
sudo dmesg | grep -i thermal
sensors  # Install lm-sensors

# Monitor during inference
htop &
ollama run llama3.2 "test"

# Optimize model loading
export OLLAMA_NUM_PARALLEL=1
export OLLAMA_MAX_LOADED_MODELS=1

---

Distribution-Specific Tips

Ubuntu/Debian Specific

# Use backports for newer versions
echo 'deb http://deb.debian.org/debian bullseye-backports main' | sudo tee -a /etc/apt/sources.list
sudo apt update
sudo apt install -t bullseye-backports ollama

# Snap installation (Ubuntu)
sudo snap install ollama --classic

# Multiple CUDA versions
sudo update-alternatives --install /usr/local/cuda cuda /usr/local/cuda-12.3 123
sudo update-alternatives --config cuda

Fedora/RHEL Specific

# Enable additional repositories
sudo dnf install fedora-workstation-repositories
sudo dnf config-manager --set-enabled google-chrome

# SELinux policies
sudo setsebool -P allow_execmem 1
sudo semanage fcontext -a -t bin_t "/usr/local/bin/ollama"
sudo restorecon -v /usr/local/bin/ollama

Arch Linux Specific

# Use different AUR helpers
yay -S ollama-git  # Development version
paru -S ollama-bin # Binary version

# Kernel parameters
sudo tee -a /etc/default/grub << 'EOF'
GRUB_CMDLINE_LINUX="nvidia-drm.modeset=1"
EOF
sudo grub-mkconfig -o /boot/grub/grub.cfg

---

Frequently Asked Questions

Q: Which Linux distribution is best for local AI?

A: Ubuntu LTS offers the best hardware support and community resources. Arch Linux provides bleeding-edge packages but requires more maintenance. Fedora is excellent for development with good balance of stability and modern packages.

Q: How much RAM do I need for Linux local AI?

A: Minimum 8GB for small models (3B parameters), 16GB for medium models (7B), 32GB+ for large models (13B+). Linux uses less overhead than Windows, so you can run larger models with the same hardware.

Q: Can I run multiple AI models simultaneously?

A: Yes, with sufficient RAM. Use OLLAMA_MAX_LOADED_MODELS to control how many models stay in memory. Each model consumes its full size in RAM.

Q: Should I use Docker or native installation?

A: Native installation offers better performance and easier GPU access. Docker is better for isolation, easy updates, and running multiple versions. Use Docker in production environments.

Q: How do I optimize for specific hardware?

A: Enable CPU governor (performance), configure GPU drivers properly, use appropriate swap settings, and tune kernel parameters. Monitor with htop, nvidia-smi, and iotop to identify bottlenecks.

---

Conclusion

Linux provides the ultimate platform for local AI with unmatched flexibility, performance, and control. From Ubuntu's user-friendly approach to Arch's cutting-edge packages, you can optimize every aspect of your AI setup for maximum performance.

The combination of proper GPU drivers, systemd service management, and shell customization creates a powerful local AI environment that can rival cloud-based solutions while maintaining complete privacy and control.

Remember to regularly update your installation (sudo apt update && sudo apt upgrade or equivalent) and monitor system resources to ensure optimal performance.

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