Building Your First Home Lab: A Complete Guide

Why Every Infrastructure Engineer Needs a Home Lab

A home lab is your personal playground where you can:

  • Break things without breaking production
  • Learn new technologies hands-on
  • Practice for certifications (CCNA, RHCSA, etc.)
  • Test infrastructure ideas before implementing at work
  • Build AI-ready systems for local model hosting

This guide covers everything from choosing hardware to running AI assistants locally.

Understanding Your Needs

Before buying anything, figure out what you want to learn:

Network-Focused Lab

  • Goal: Learn routing, switching, VLANs, firewalls
  • Hardware: Managed switches, pfSense router, Raspberry Pi
  • Software: GNS3, EVE-NG for network simulation
  • Budget: $200-500

Virtualization Lab

  • Goal: Master hypervisors, VMs, containers
  • Hardware: Server with decent RAM (32GB+)
  • Software: Proxmox, ESXi, Docker
  • Budget: $500-1500

AI-Ready Lab

  • Goal: Run local LLMs, AI assistants, ML workloads
  • Hardware: GPU-equipped server or workstation
  • Software: Ollama, OpenClaw, Jupyter
  • Budget: $800-2000+

Hardware Options

Option 1: Start Small (Under $100)

Raspberry Pi 4 (8GB)

  • Cost: ~$80
  • Power: 5-15W
  • Good for: DNS (Pi-hole), VPN, web servers, K3s
  • Limitations: ARM CPU, limited compute

Old Laptop/Desktop

  • Cost: Free (use what you have)
  • Power: 50-150W
  • Good for: Learning Linux, Docker, basic VMs
  • Limitations: Age, noise, limited expandability

Option 2: Entry-Level Server ($300-700)

Used Dell PowerEdge R720 / HP DL380 Gen8

  • Cost: $300-500 on eBay
  • Specs: Dual CPUs (12-32 cores), 64-128GB RAM
  • Power: 200-400W idle
  • Pros: Enterprise hardware, tons of RAM, hot-swap drives
  • Cons: Loud, power-hungry, older tech

HP ProDesk / Dell OptiPlex (SFF)

  • Cost: $200-400 used
  • Specs: i5/i7, 32-64GB RAM, quiet
  • Power: 65-150W
  • Pros: Silent, power-efficient, small
  • Cons: Limited expandability

Option 3: AI-Ready Build ($1000-2000+)

For running local LLMs (Ollama, LM Studio, etc.):

Minimum Specs:

  • CPU: AMD Ryzen 9 / Intel i9 (12+ cores)
  • RAM: 64GB minimum (128GB better)
  • GPU: NVIDIA RTX 3090/4090 or AMD RX 7900 XT (16GB+ VRAM)
  • Storage: 1TB NVMe + 4TB HDD

Why GPU Matters:

  • CPU-only inference is slow (10-30 tokens/sec)
  • GPU inference is fast (50-200+ tokens/sec)
  • NVIDIA has better software support (CUDA)
  • AMD works but requires ROCm (trickier setup)

My Build (Real Example):

  • AMD Ryzen 9 5900X (24 threads)
  • 62GB RAM
  • AMD RX 6600 XT (8GB VRAM - not ideal for AI!)
  • Result: CPU inference too slow, GPU not supported by ROCm 😅

Lesson: If you want AI, invest in a supported GPU upfront.

Virtualization Platforms

Why I love it:

  • Free and open source
  • Debian-based (familiar for Linux users)
  • Web UI for everything
  • Supports VMs (KVM) + Containers (LXC)
  • Built-in backup, HA clustering

Perfect for:

  • Running multiple VMs (Windows, Linux, BSD)
  • Containers for lightweight services
  • Learning enterprise virtualization

Getting Started:

  1. Download Proxmox ISO
  2. Boot from USB, install to bare metal
  3. Access web UI: https://your-ip:8006
  4. Create VMs, containers, storage pools

VMware ESXi (Enterprise Standard)

Pros:

  • Industry standard (skills transfer to work)
  • vCenter for management
  • Mature ecosystem

Cons:

  • Free version limitations
  • Licensing costs for features
  • Hardware compatibility pickier

Use if: You want enterprise skills for your resume

GNS3 (Network Simulation)

Not a hypervisor - but essential for network labs:

  • Simulates routers, switches, firewalls
  • Runs real Cisco IOS, Juniper, Palo Alto images
  • Connect VMs to simulated networks
  • Practice CCNA/CCNP without physical gear

Setup:

  • Install GNS3 on Windows/Mac/Linux
  • Add router images (legally obtained!)
  • Build topologies, test configs
  • Break stuff without consequences

Essential Services for Your Lab

Once you have hardware and a hypervisor, run these:

Core Infrastructure

  1. DNS Server (Pi-hole, Unbound) - Block ads, learn DNS
  2. DHCP Server - Understand IP management
  3. pfSense/OPNsense - Firewall, routing, VPN
  4. Reverse Proxy (Nginx, Traefik) - Secure service access

Monitoring & Observability

  1. Grafana + Prometheus - Visualize everything
  2. Uptime Kuma - Service monitoring
  3. Netdata - Real-time system metrics

Learning Platforms

  1. Docker Host - Container fundamentals
  2. Kubernetes (K3s on Raspberry Pi!) - Orchestration
  3. GitLab/Gitea - Version control, CI/CD pipelines

AI Lab: Running Local Models

Want to run AI assistants like OpenClaw locally?

Software Stack

Ollama (Easiest)

curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.3
ollama run llama3.3

LM Studio (GUI)

  • Download models with one click
  • Visual interface
  • OpenAI-compatible API

OpenClaw (AI Assistant Framework)

  • Integrates with local models (Ollama, LM Studio)
  • Telegram, Discord, email integration
  • File access, web search, automation
  • Can use GPU for inference

Model Recommendations by Hardware

CPU-Only (64GB+ RAM):

  • Llama 3.2 3B - Fast, decent quality
  • Phi-3 Mini - Microsoft’s small model
  • Qwen 2.5 7B - Good for chat

GPU (8GB VRAM):

  • Llama 3.3 8B - Solid all-rounder
  • Mistral 7B - Fast, good reasoning
  • DeepSeek R1 8B - Reasoning model (slower)

GPU (16GB+ VRAM):

  • Llama 3.3 70B - Near-GPT-4 quality
  • Mixtral 8x7B - MoE architecture, fast
  • DeepSeek R1 - Full reasoning capabilities

OpenClaw + Local AI

Imagine this workflow:

  1. Run Ollama with Llama 3.3 locally
  2. Connect OpenClaw to it
  3. Chat via Telegram/Discord
  4. AI has access to your files, calendar, email
  5. Zero API costs, fully private

Setup:

# Install OpenClaw
npm install -g openclaw

# Configure local model
openclaw configure
# Select: Local LLM (Ollama/LM Studio)
# Point to: http://localhost:11434

# Run
openclaw gateway start

Now you have a personal AI assistant running entirely on your hardware. No cloud dependency, no API bills, complete privacy.

Common Mistakes to Avoid

❌ Buying too much too soon - Start small, expand as you learn ❌ Ignoring power costs - Old servers can cost $50-100/month in electricity ❌ Skipping documentation - Write down what you did or you’ll forget ❌ No backups - Labs fail. Back up configs and VMs ❌ Ignoring security - Practice good habits even in a lab ❌ Wrong GPU for AI - Check ROCm/CUDA support before buying

My Home Lab Journey

I’ve broken everything at least twice. Here’s what I learned:

What worked:

  • Starting with a Raspberry Pi
  • Using Proxmox over ESXi (easier learning curve)
  • Running local AI models (when hardware cooperated!)
  • Documenting failures and solutions

What didn’t:

  • Buying a GPU without checking AI framework support
  • Running DeepSeek R1 on CPU (too slow for real-time chat)
  • Not checking kernel compatibility before installing drivers
  • Assuming “it’ll just work”

Resources

Next Steps

  1. Define your goal - Network? Virtualization? AI?
  2. Choose hardware - Start small, expand later
  3. Pick a hypervisor - Proxmox is my recommendation
  4. Install core services - DNS, monitoring, reverse proxy
  5. Document everything - Future you will thank present you
  6. Break things safely - That’s what labs are for!

Coming Soon

Future posts will dive deeper into:

  • Setting up Proxmox step-by-step
  • Building a pfSense router from scratch
  • Running OpenClaw with local AI models
  • Monitoring your entire lab with Grafana
  • Network simulation with GNS3

Got a home lab? Share your setup in the comments! What worked? What didn’t? Let’s learn together. 🖥️

Update: I learned the hard way that AMD RX 6600 XT doesn’t work with ROCm on kernel 6.14. Check compatibility before buying! đŸ˜