Local LLM Setup Guide

What's Inside

A complete, hands-on guide to running your own AI language models locally. Covers four major inference engines (Ollama, llama.cpp, vLLM, LM Studio), hardware selection, model quantization, performance optimization, secure networking, and cost analysis. Whether you want privacy, zero API costs, or offline capability — this guide gets you running.

Sections Covered

• Why Run Your Own LLM? (privacy, cost, control, offline, customization)
• Hardware Requirements (minimum, recommended, maximum configurations)
• Software Stack Overview (architecture diagram)
• Method 1: Ollama — Easiest path (install, run, API, custom Modelfiles)
• Method 2: llama.cpp — Maximum performance (build, download, run, key parameters)
• Method 3: vLLM — Production serving (tensor parallelism, continuous batching, OpenAI API)
• Method 4: LM Studio — GUI users (no command line needed)
• Model Selection Guide (by use case, with VRAM requirements)
• Quantization Guide (Q2 through Q8, quality/size tradeoffs)
• Performance Optimization (GPU, CPU, memory bandwidth, benchmarking)
• Networking & API Access (WireGuard VPN, Caddy reverse proxy, Open WebUI)
• Security Hardening (6-point checklist)
• Cost Analysis: Local vs Cloud (break-even calculations)

Sample: Hardware Requirements

Sample: Ollama Quick Start

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Run your first model (4.7GB download)
ollama run llama3.1:8b

# Start the API server
ollama serve

# Use the API
curl http://localhost:11434/api/generate -d '{
  "model": "llama3.1:8b",
  "prompt": "Explain quantum computing in simple terms",
  "stream": false
}'

Sample: Quantization Guide

Sample: Cost Break-Even Analysis