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Free Guide

Run LLMs Locally

Complete guide to running AI models on your own hardware. Private, fast, and free after setup.

Hardware Requirements

Model SizeMin RAMRecommended RAMGPU VRAMExample Models
7B8GB16GB6GBLlama 3.2, Mistral 7B, Phi-3
13B16GB32GB10GBCodeLlama 13B, Llama 2 13B
30-34B32GB64GB24GBCodeLlama 34B, Mixtral 8x7B
70B+64GB128GB48GB+Llama 3 70B, DeepSeek

Which Model Should You Use?

Coding Assistant

General Chat

Writing & Docs

Image Understanding

Step-by-Step Setup

macOS (Apple Silicon)

1

Install Homebrew

Package manager for macOS

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
2

Install Ollama

The easiest way to run LLMs locally on Mac

brew install ollama
3

Start Ollama Service

Runs in the background, manages models

ollama serve
4

Download a Model

Pulls Llama 3.2 (4.7GB). Other options: mistral, codellama, phi3

ollama pull llama3.2
5

Chat with Your Model

Start an interactive chat session

ollama run llama3.2

💡 Tip: Use 'ollama list' to see downloaded models

Cost Comparison: Local vs API

MethodSetup CostMonthlyPer 1M TokensLatencyPrivacy
Local (7B)$0$0*$020-100ms✓ Full
Local (70B)$1,500+**$50-100$0100-500ms✓ Full
OpenAI GPT-4$0Usage$30-60500-2000ms✗ Cloud
Claude 3.5$0Usage$15-75500-2000ms✗ Cloud
GPT-4 API$0Usage$30+1-3s✗ Cloud

* Electricity cost only (~$5-10/month for heavy use)
** High-end GPU (RTX 4090 or multiple) for 70B models

Pro Tips

Start with 7B models

They run on almost any hardware and are surprisingly capable

Use quantized models

Q4_K_M offers best balance of speed and quality

Monitor GPU memory

Use nvidia-smi or Activity Monitor to check usage

Try different models

Each has strengths - Mistral for reasoning, CodeLlama for code

Use the API

Ollama serves OpenAI-compatible API at localhost:11434

Join the community

r/LocalLLaMA and AGI House Discord for tips

Download the Complete PDF Guide

Includes troubleshooting tips, model comparison charts, and advanced configuration.

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