Build Offline AI: Run AI Without Internet (2026)

Last summer I spent two weeks in a cabin with terrible satellite internet. Couldn’t load a webpage to save my life. But I had a full AI assistant running on my laptop—asking questions, debugging code, helping with writing—all completely offline.

That trip changed how I think about AI. Cloud services are convenient, but they’re also dependencies. When the internet goes down (and it WILL go down when you need it most), your AI assistant disappears too.

Building a fully offline AI setup isn’t just for survivalist scenarios. It’s for airplanes, remote locations, secure environments, and anyone who values AI that works reliably and privately.

Let me show you exactly how to build an AI system that works without a single internet connection.

Why Run AI Completely Offline

The mainstream AI experience is cloud-dependent. ChatGPT, Claude, Gemini—all require constant internet. Every query goes to distant servers. That’s fine until it isn’t.

Privacy and Security

When you use cloud AI:

• Your prompts travel over the internet
• They’re processed on servers you don’t control
• They may be logged, reviewed, or used for training
• Even “privacy-focused” services require trusting their policies

With offline AI:

• Nothing leaves your machine. Ever.
• No network traffic to intercept
• No server logs to worry about
• Complete, verifiable privacy

For sensitive work—legal documents, medical information, proprietary code, confidential business data—offline AI means genuine security. It’s the only way to be certain your data stays yours.

Reliability

Cloud services fail. I’ve been mid-conversation with Claude and had the service go down. Lost the context, couldn’t continue.

More commonly:

• Slow/unreliable internet degrades experience
• API rate limits can block you when you need help most
• Service outages happen (remember OpenAI’s big outage last year?)
• Geographic restrictions may block access while traveling

Offline AI doesn’t care about any of this. It runs on your hardware, using your power. Network problems become irrelevant.

Remote Locations

Some places simply don’t have internet:

• Airplanes (even with WiFi, it’s often unusable)
• Remote work sites
• Developing regions
• Wilderness and rural areas
• Underwater installations, ships, remote research stations

If your work takes you off-grid, offline AI lets you maintain AI-enhanced productivity anywhere.

Enterprise and Government Requirements

Many organizations prohibit cloud AI entirely:

• Classified environments (air-gapped networks)
• Healthcare (HIPAA compliance concerns)
• Finance (data residency requirements)
• Defense contractors
• Critical infrastructure

For these users, “just use ChatGPT” isn’t an option. Offline AI is the only AI.

Hardware Requirements

Let’s be realistic about what you need to run AI locally without compromises.

Minimum Specs

CPU-only setup (usable but slow):

• Any modern 64-bit CPU (Intel/AMD, last 5 years)
• 16 GB RAM minimum
• 50 GB free storage
• SSD strongly recommended

This runs 7B parameter models at ~5-10 tokens/second. Usable for simple tasks, a bit slow for interactive work.

Entry-level GPU setup:

• GPU with 8+ GB VRAM (RTX 3060, RTX 4060)
• 16 GB RAM
• 100 GB free storage

This runs 7B-13B models at ~20-40 tokens/second. Comfortable for most uses.

Recommended Specs

Good experience:

• GPU with 12-16 GB VRAM (RTX 4070 Ti, RTX 3080)
• 32 GB RAM
• 200 GB SSD storage

Excellent experience:

• GPU with 24+ GB VRAM (RTX 4090)
• 64 GB RAM
• 500 GB+ NVMe storage

With 24 GB VRAM, you can run 34B models or even 70B with quantization—genuinely impressive capability.

GPU vs CPU

GPU advantages:

• 5-10x faster than CPU
• Essential for larger models
• Better power efficiency per token

CPU advantages:

• Works on more hardware (including laptops without discrete GPUs)
• No driver issues
• Still functional with enough RAM

My recommendation: Use a GPU if you have one. But don’t let lack of GPU stop you—CPU inference works, just slower.

For more on hardware choices, see our GPU guide for AI.

Storage Needs

Models take significant space:

Model Size	Quantized Size	Full Precision
7B	4 GB (Q4)	14 GB
13B	7 GB (Q4)	26 GB
34B	20 GB (Q4)	68 GB
70B	40 GB (Q4)	140 GB

Keep multiple models for different tasks? Budget accordingly. I keep about 150 GB of models on my main machine.

Choosing Your Offline Software Stack

Several tools can run AI locally. Here’s how they compare.

Ollama (Recommended)

Ollama is my top recommendation for most users:

Pros:

• Simplest setup (one command to install, one to run)
• Large model library
• Cross-platform (Mac, Windows, Linux)
• Active community
• OpenAI-compatible API

Cons:

• Less customization than raw llama.cpp
• Slightly more overhead than bare metal

Best for: Most users, especially beginners.

We have a detailed Ollama guide covering installation and usage.

LM Studio

LM Studio is a desktop application with a graphical interface:

Pros:

• Beautiful GUI—no command line needed
• Easy model downloading and management
• Built-in chat interface
• Cross-platform

Cons:

• GUI overhead uses more resources
• Less scriptable
• Larger installation

Best for: Users who prefer graphical interfaces, quick testing of models.

Check out our LM Studio guide for more details.

llama.cpp

The underlying engine that powers many local AI tools:

Pros:

• Maximum performance
• Minimum overhead
• Most customization
• Updated frequently with new optimizations

Cons:

• Requires building from source (or finding binaries)
• Command-line only
• Steeper learning curve

Best for: Power users, embedded/resource-constrained environments, maximum performance seekers.

Comparison Table

Feature	Ollama	LM Studio	llama.cpp
Ease of use	★★★★★	★★★★★	★★★☆☆
Performance	★★★★☆	★★★★☆	★★★★★
GUI	No (can add)	Yes	No
Customization	★★★★☆	★★★☆☆	★★★★★
Model variety	★★★★★	★★★★☆	★★★★☆

For this guide, I’ll use Ollama—the best balance of simplicity and capability.

Best Models for Offline Use

Not all models are equal for offline deployment. Here are my recommendations.

Llama 4 Variants

Meta’s Llama 4 family is the gold standard for open-source AI:

Llama 4 8B:

• Best for: 8-12 GB VRAM or 16+ GB RAM
• Capability: Very good for most tasks
• Speed: Fast, even on modest hardware

ollama pull llama4

Llama 4 70B (quantized):

• Best for: 24+ GB VRAM or 64+ GB RAM
• Capability: Excellent, approaching GPT-4 level
• Speed: Slower but worth it for harder tasks

ollama pull llama4:70b-instruct-q4_K_M

For more on the Llama family, see our Meta Llama 4 guide.

Mistral Models

Mistral from the European lab offers excellent quality:

Mistral 7B:

• Punches above its weight
• Great instruction following
• Excellent for coding

ollama pull mistral

Mistral Large (via API or quantized):

• Top-tier capability
• Larger memory requirements
• Worth it for complex tasks

See our Mistral guide for details.

Phi-4 (Compact Option)

Microsoft’s Phi-4 is surprisingly capable for its size:

Best for: Limited hardware, embedded systems, ARM devices

Capability: Impressive for its size, though not matching larger models

ollama pull phi4

This runs well even on Raspberry Pi 5 or older laptops.

Quantization Explained

Quantization compresses model weights to use less memory:

Format	Bits	Size Reduction	Quality Impact
Full (fp16)	16	Baseline	Perfect
Q8	8	~50%	Minimal
Q6	6	~60%	Very slight
Q4	4	~75%	Slight
Q2	2	~85%	Noticeable

For most uses, Q4 offers the best balance. You fit larger models with minimal quality loss. Q2 is useful for truly constrained environments but quality suffers.

When running with Ollama, models are typically available in Q4 by default:

ollama pull llama4:70b-instruct-q4_K_M  # Q4 quantization

Complete Offline Setup Walkthrough

Let’s build a fully offline AI system from scratch.

Pre-downloading Everything

The key to offline operation: download everything while you have internet.

Step 1: Download Ollama

While online, download the installer for your platform from ollama.ai.

Save the installer file—you’ll use it on your offline machine.

Step 2: Download Models

Pull all models you’ll need:

# Primary general-purpose model
ollama pull llama4

# Code-focused model
ollama pull deepseek-coder-v2

# Fast/light model for quick tasks
ollama pull phi4

# Vision model (if needed)
ollama pull llava

Each download stores the model locally. They’ll work offline forever after.

Step 3: Note Model Locations

Ollama stores models in:

• macOS: ~/.ollama/models
• Linux: ~/.ollama/models
• Windows: %USERPROFILE%\.ollama\models

You can copy this entire folder to another machine for offline use.

Installing Ollama Offline

If your target machine has no internet:

1. Copy the Ollama installer to a USB drive
2. Copy the .ollama/models folder to the USB drive
3. On the offline machine:
   • Install Ollama from the installer
   • Copy the models folder to the appropriate location

That’s it. Run ollama list to verify models are available.

Transferring Models

For air-gapped environments, you need to physically transfer models.

Export from online machine:

# Locate model files
ls ~/.ollama/models/manifests/registry.ollama.ai/library/

# Tar up everything
tar -czvf ollama-models.tar.gz ~/.ollama/models/

Import on offline machine:

# Extract to correct location
tar -xzvf ollama-models.tar.gz -C ~/

# Verify
ollama list

This works across any transport medium: USB, external drive, sneakernet.

Verifying Offline Operation

Test that everything works without internet.

Step 1: Disable network

• Turn off WiFi
• Disconnect Ethernet
• Enable airplane mode

Step 2: Run a model

ollama run llama4 "What year was the Eiffel Tower built?"

If you get a response, congratulations—you have working offline AI.

Step 3: Test various models

# Test each downloaded model
ollama run phi4 "Explain quantum computing briefly"
ollama run deepseek-coder-v2 "Write a Python function to find prime numbers"

All should respond without network access.

Adding Offline GUI

Command line not your preference? Add a graphical interface.

Open WebUI (Offline Setup)

Open WebUI provides a ChatGPT-like interface for Ollama.

Pre-download (while online):

# Pull the Docker image
docker pull ghcr.io/open-webui/open-webui:main

# Save to file
docker save ghcr.io/open-webui/open-webui:main > openwebui.tar

Install offline:

# Load image from file
docker load < openwebui.tar

# Run
docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

Open http://localhost:3000 in your browser. Full ChatGPT-style interface, completely offline.

Desktop Options

For no-Docker setups:

Chatbox - Cross-platform desktop app

• Download the installer while online
• Configure to connect to http://localhost:11434 (Ollama’s API)
• Works offline after initial setup

Msty (macOS) - Native Mac app

• Download from App Store while online
• Connects to local Ollama automatically
• Clean, native interface

Terminal-Based Usage

For minimal setups without GUI overhead:

# Create a simple chat script
cat > chat.sh << 'EOF'
#!/bin/bash
while true; do
    echo -n "You: "
    read input
    if [ "$input" = "quit" ]; then break; fi
    ollama run llama4 "$input"
    echo
done
EOF
chmod +x chat.sh

# Run
./chat.sh

Simple, effective, works on any terminal.

Offline Speech and Vision

For complete offline AI, you might want voice and vision too.

Local Whisper

OpenAI’s Whisper can run locally for speech-to-text:

Install (while online):

pip install openai-whisper

Download models (while online):

import whisper
# This downloads the model
model = whisper.load_model("base")  # or "small", "medium", "large"

Models are cached in ~/.cache/whisper/.

Use offline:

import whisper
model = whisper.load_model("base")
result = model.transcribe("audio.mp3")
print(result["text"])

Works completely offline once models are downloaded. See our audio guide for more context.

Local Image Models

For image understanding offline:

LLaVA via Ollama:

# Download while online
ollama pull llava

# Use offline
ollama run llava "Describe this image: [image path]"

LLaVA can analyze images, describe their contents, and answer questions about them.

Stable Diffusion for image generation:

Requires more setup but works fully offline. Download models while online, then generate images anywhere. See our Stable Diffusion tutorial.

Performance Optimization

Once your offline setup works, optimize it.

Memory Management

Monitor memory usage:

# Linux/Mac
watch -n 1 free -h

# Check Ollama specifically
ollama ps

Reduce memory usage:

• Use more quantized models (Q4 instead of Q8)
• Run one model at a time
• Close other applications during inference

Speed Improvements

Use GPU when available: Ollama automatically uses GPU. Verify with:

ollama run llama4 --verbose
# Look for GPU info in output

Match model to hardware:

• 8 GB VRAM → 7B models
• 12 GB VRAM → 13B models
• 24 GB VRAM → 34B or 70B models

Reduce context length for speed:

# Create Modelfile with shorter context
cat > Modelfile << EOF
FROM llama4
PARAMETER num_ctx 2048  # Default is often 4096+
EOF
ollama create llama4-fast -f Modelfile

Battery Considerations

For laptops:

• Smaller models use less power
• CPU inference is possible but drains faster
• Consider “low-power” models like Phi-4 for battery operation

Heavy AI work can drain a laptop battery in 1-2 hours. Plan accordingly.

Frequently Asked Questions

Can I run all these models on a MacBook?

Yes! Apple Silicon Macs (M1/M2/M3/M4) run local AI exceptionally well. The unified memory architecture means even a 16GB MacBook Air can run 7B models smoothly. See our Mac AI guide.

What’s the quality difference vs ChatGPT?

Honest answer: cloud models still have an edge. GPT-5 and Claude 4 are generally more capable than Llama 4 70B. But the gap has narrowed dramatically. For most practical tasks, local models are “good enough”—and they never fail due to internet issues.

How do I update models when I’m back online?

# Re-pull to get updates
ollama pull llama4

# Then transfer to offline machine as before

Check for updates periodically. Model improvements are released regularly.

Can I run this on a Raspberry Pi?

Pi 5 with 8GB RAM can run small models (Phi, Gemma 2B). It’s slow but works. Great for embedded projects or always-on assistants where speed isn’t critical.

What if I need the latest information?

Offline AI can’t access the internet, so it doesn’t know recent events. For most tasks (coding, writing, analysis), this doesn’t matter. For current events, you’ll need to provide context or wait until you’re back online.

Conclusion

Setting up offline AI takes some initial effort, but the payoff is substantial:

• True privacy: Nothing ever leaves your machine
• Perfect reliability: Works anywhere, anytime
• Zero ongoing cost: Run as much as you want
• Complete independence: No account needed, no service to trust

The capabilities of local models have reached the point where offline AI isn’t a compromise—it’s a legitimate choice that makes sense for many use cases.

Start simple:

1. Install Ollama
2. Pull a few models
3. Test offline operation
4. Add GUI if desired

Once set up, you’ll wonder why you ever depended on cloud AI for everything.

For more local AI resources, explore our guides on Ollama, open source LLMs, and LM Studio.

Your AI, your hardware, your terms.

Last updated: January 11, 2026