Running AI Models Locally: Complete Setup Guide for 2026

Running AI models locally gives you privacy, offline access, and zero API costs. This guide covers everything you need to get started in 2026.

Why Run AI Locally?

Benefits

• Privacy: Your data never leaves your computer
• No costs: After setup, usage is free
• Offline access: Works without internet
• No rate limits: Generate as much as you want
• Customization: Fine-tune models for your needs

Trade-offs

• Requires decent hardware (GPU recommended)
• Smaller models than cloud services
• Initial setup effort
• You manage updates

Hardware Requirements

Minimum (7B models)

• RAM: 16GB
• Storage: 20GB free
• GPU: Optional but recommended
• CPU: Modern quad-core

Recommended (13B-70B models)

• RAM: 32GB+
• Storage: 100GB+ SSD
• GPU: NVIDIA RTX 3080+ or M1/M2/M3 Mac
• VRAM: 8GB+ for GPU acceleration

Option 1: Ollama (Recommended for Beginners)

Ollama is the easiest way to run local models.

Installation

macOS:

brew install ollama

Windows: Download from ollama.ai

Linux:

curl -fsSL https://ollama.ai/install.sh | sh

Running Your First Model

# Start Ollama service
ollama serve

# In another terminal, run a model
ollama run llama3.2

# Or try other models
ollama run mistral
ollama run codellama
ollama run deepseek-coder

Available Models

Model	Size	Best For
llama3.2	3B-70B	General purpose
mistral	7B	Fast, capable
codellama	7B-34B	Coding tasks
deepseek-coder	6.7B-33B	Code generation
phi-3	3.8B	Small but capable
qwen2.5	7B-72B	Multilingual

Using Ollama with Applications

# API endpoint (OpenAI compatible)
curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.2",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Option 2: LM Studio (Best GUI)

LM Studio provides a polished desktop app for running local models.

Setup

1. Download from lmstudio.ai
2. Install and launch
3. Browse the model catalog
4. Download a model (one-click)
5. Start chatting

Features

• Visual model browser
• Built-in chat interface
• OpenAI-compatible API server
• Model comparison tools
• Hardware monitoring

Option 3: Text Generation WebUI (Most Features)

For power users who want maximum control.

Installation

# Clone the repository
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui

# Run the installer
./start_linux.sh  # or start_windows.bat

Features

• Multiple model format support
• Advanced generation parameters
• Extensions ecosystem
• Training/fine-tuning tools
• Multi-user support

Choosing the Right Model

For General Chat

• Best quality: Llama 3.2 70B (needs 48GB+ VRAM)
• Good balance: Llama 3.2 8B or Mistral 7B
• Limited hardware: Phi-3 3.8B

For Coding

• Best: DeepSeek Coder 33B
• Good balance: CodeLlama 13B
• Fast: DeepSeek Coder 6.7B

For Writing

• Creative: Llama 3.2 with higher temperature
• Factual: Mistral 7B Instruct
• Fast drafts: Phi-3

Performance Tips

GPU Acceleration

Ollama automatically uses GPU when available. Check with:

ollama run llama3.2 --verbose

Quantization

Smaller quantized models run faster with minimal quality loss:

• Q4_K_M: Good balance of speed/quality
• Q5_K_M: Better quality, slightly slower
• Q8_0: Near full quality

Context Length

Longer context uses more memory:

# Set context length
ollama run llama3.2 --ctx-size 4096

Integrating with Your Workflow

VS Code Integration

Install “Continue” extension, configure for Ollama:

{
  "models": [{
    "title": "Ollama",
    "provider": "ollama",
    "model": "codellama"
  }]
}

Python Integration

import requests

response = requests.post(
    "http://localhost:11434/api/generate",
    json={
        "model": "llama3.2",
        "prompt": "Explain quantum computing",
        "stream": False
    }
)
print(response.json()["response"])

Using with LangChain

from langchain_community.llms import Ollama

llm = Ollama(model="llama3.2")
response = llm.invoke("What is machine learning?")

Troubleshooting

Model Won’t Load

• Check available RAM/VRAM
• Try a smaller model
• Use quantized version

Slow Generation

• Enable GPU acceleration
• Use smaller context length
• Try a smaller model
• Close other applications

Out of Memory

• Use quantized models (Q4_K_M)
• Reduce context length
• Try CPU-only mode (slower but works)

Next Steps

1. Start simple: Install Ollama, run Mistral 7B
2. Experiment: Try different models for different tasks
3. Integrate: Connect to your development tools
4. Optimize: Fine-tune settings for your hardware
5. Explore: Try fine-tuning on your own data

Local AI is increasingly capable. What was cloud-only last year now runs on a laptop. The gap will continue to close.

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What local models are you running? Share your setup in the comments!