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docs: add comprehensive guide on running AI models locally
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eckartal committed Jan 31, 2025
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188 changes: 188 additions & 0 deletions docs/src/pages/post/run-ai-models-locally.mdx
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---
title: "How to run AI models locally: A Complete Guide for Beginners"
description: "A simple guide to running AI models locally on your computer. It's for beginners - no technical knowledge needed."
tags: AI, local models, Jan, GGUF, privacy, local AI
categories: guides
date: 2024-01-31
ogImage: assets/jan-local-ai.jpg
---

import { Callout } from 'nextra/components'
import CTABlog from '@/components/Blog/CTA'

# How to run AI models locally: A Complete Guide for Beginners

Running AI models locally means installing them on your computer instead of using cloud services. This guide shows you how to run open-source AI models like Llama, Mistral, or DeepSeek on your computer - even if you're not technical.

## Quick steps:
1. Download [Jan](https://jan.ai)
2. Pick a recommended model
3. Start chatting

Read [Quickstart](https://jan.ai/docs/quickstart) to get started. For more details, keep reading.

![Run AI models locally with Jan](./_assets/jan-local-ai.jpg)
*Jan is for running AI models locally. Download [Jan](https://jan.ai)*

<Callout type="info">
Benefits of running AI locally:
- **Privacy:** Your data stays on your computer
- **No internet needed:** Use AI even offline
- **No limits:** Chat as much as you want
- **Full control:** Choose which AI models to use
</Callout>

## How to run AI models locally as a beginner

[Jan](https://jan.ai) makes it easy to run AI models. Just download the app and you're ready to go - no complex setup needed.

<Callout type="tip">
What you can do with Jan:
- Download AI models with one click
- Everything is set up automatically
- Find models that work on your computer
</Callout>

## Understanding Local AI models

Think of AI models like apps - some are small and fast, others are bigger but smarter. Let's understand two important terms you'll see often: parameters and quantization.

### What's a "Parameter"?

When looking at AI models, you'll see names like "Llama-2-7B" or "Mistral-7B". Here's what that means:

![AI model parameters explained](./_assets/local-ai-model-parameters.jpg)
*Model sizes: Bigger models = Better results + More resources*

- The "B" means "billion parameters" (like brain cells)
- More parameters = smarter AI but needs a faster computer
- Fewer parameters = simpler AI but works on most computers

<Callout type="info">
Which size to choose?
- **7B models:** Best for most people - works on most computers
- **13B models:** Smarter but needs a good graphics card
- **70B models:** Very smart but needs a powerful computer
</Callout>

### What's Quantization?

Quantization makes AI models smaller so they can run on your computer. Think of it like compressing a video to save space:

![AI model quantization explained](./_assets/open-source-ai-quantization.jpg)
*Quantization: Balance between size and quality*

Simple guide:
- **Q4:** Best choice for most people - runs fast and works well
- **Q6:** Better quality but runs slower
- **Q8:** Best quality but needs a powerful computer

Example: A 7B model with Q4 works well on most computers.

## Hardware Requirements

Before downloading an AI model, let's check if your computer can run it.

<Callout type="info">
The most important thing is VRAM:
- VRAM is your graphics card's memory
- More VRAM = ability to run bigger AI models
- Most computers have between 4GB to 16GB VRAM
</Callout>

### How to check your VRAM:

**On Windows:**
1. Press Windows + R
2. Type "dxdiag" and press Enter
3. Click "Display" tab
4. Look for "Display Memory"

**On Mac:**
1. Click Apple menu
2. Select "About This Mac"
3. Click "More Info"
4. Look under "Graphics/Displays"

### Which models can you run?

Here's a simple guide:

| Your VRAM | What You Can Run | What It Can Do |
|-----------|-----------------|----------------|
| 4GB | Small models (1-3B) | Basic writing and questions |
| 6GB | Medium models (7B) | Good for most tasks |
| 8GB | Larger models (13B) | Better understanding |
| 16GB | Largest models (32B) | Best performance |

<Callout type="tip">
Start with smaller models:
- Try 7B models first - they work well for most people
- Test how they run on your computer
- Try larger models only if you need better results
</Callout>

## Setting Up Your Local AI

### 1. Get Started
Download Jan from [jan.ai](https://jan.ai) - it sets everything up for you.

### 2. Get an AI Model

You can get models two ways:

### 1. Use Jan Hub (Recommended):
- Click "Download Model" in Jan
- Pick a recommended model
- Choose one that fits your computer

![AI model parameters explained](./_assets/jan-model-download.jpg)
*Use Jan Hub to download AI models*

### 2. Use Hugging Face:

<Callout type="warning">
Important: Only GGUF models will work with Jan. Make sure to use models that have "GGUF" in their name.
</Callout>

#### Step 1: Get the model link
Find and copy a GGUF model link from [Hugging Face](https://huggingface.co)

![Finding a GGUF model on Hugging Face](./_assets/hugging-face-jan-model-download.jpg)
*Look for models with "GGUF" in their name*

#### Step 2: Open Jan
Launch Jan and go to the Models tab

![Opening Jan's model section](./_assets/jan-library-deepseek-r1.jpg)
*Navigate to the Models section in Jan*

#### Step 3: Add the model
Paste your Hugging Face link into Jan

![Adding a model from Hugging Face](./_assets/jan-hub-deepseek-r1.jpg)
*Paste your GGUF model link here*

#### Step 4: Download
Select your quantization and start the download

![Downloading the model](./_assets/jan-hf-model-download.jpg)
*Choose your preferred model size and download*

### Common Questions

<Callout type="info">
**"My computer doesn't have a graphics card - can I still use AI?"**
Yes! It will run slower but still work. Start with 7B models.

**"Which model should I start with?"**
Try a 7B model first - it's the best balance of smart and fast.

**"Will it slow down my computer?"**
Only while you're using the AI. Close other big programs for better speed.
</Callout>

## Need help?
<Callout type="info">
Having trouble? We're here to help! [Join our Discord community](https://discord.gg/Exe46xPMbK) for support.
</Callout>

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