Why Run AI Locally?

Running LLMs on your own hardware means: no API costs, no rate limits, complete privacy, offline access, and unlimited usage. As of 2026, open-source models like Llama 3, Mistral, and Qwen rival GPT-4 quality for most tasks — and you can run them on a $1,400 Mac Mini or a $700 used GPU.

This guide takes you from zero to chatting with a local AI in under 30 minutes.

Hardware Requirements

The hardware you need depends on the model size you want to run:

Picking between the two flagship cards? See our RTX 5090 vs RTX 4090 comparison for the VRAM, speed, and price trade-offs.

The Easiest Path: Ollama

Ollama is the simplest way to run LLMs locally. One install, one command, done.

Install Ollama

Mac:

brew install ollama

Linux:

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

Windows: Download the installer from ollama.ai and run it.

Run Your First Model

# Start the Ollama service (Mac/Linux)
ollama serve

# In a new terminal, run a model
ollama run llama3.1

# Chat with it!
>>> What is the best GPU for running AI locally?

That's it. On first run, Ollama downloads the model (~4.7GB for Llama 3.1 8B). After that, it launches in seconds.

Best Models to Start With

Here are our recommended models for different use cases:

Newer releases like Llama 4 Scout 8B and Qwen 3 are also in the Ollama library and download the same way — just swap the model name.

To run any of these:

# Just replace the model name
ollama run mistral
ollama run qwen2.5:32b
ollama run deepseek-r1:14b

Add a Chat Interface (Open WebUI)

Ollama's terminal interface works, but a web UI makes it much nicer. Open WebUI gives you a ChatGPT-like experience running on your hardware.

# Install Docker first, then:
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. You'll see a familiar chat interface where you can select models, create conversations, and even upload documents for RAG (retrieval-augmented generation).

Running LLMs on Apple Silicon

Apple Silicon Macs are uniquely good at local AI because of unified memory — the CPU and GPU share the same RAM pool, so a Mac with 24GB of unified memory can load a 24GB model without a discrete GPU.

Performance Tips

• Use quantized models: 4-bit quantization (Q4_K_M) reduces VRAM usage by ~4x with only 5–10% quality loss. This is how you run a 70B model on 24GB VRAM.
• Close other GPU apps: Chrome, video playback, and games all use VRAM. Close them before running large models.
• Use the right model size: Bigger isn't always better. A fast 8B model beats a slow 70B model for quick tasks. Match model size to your task complexity.
• Set context length: Lower context length = less VRAM usage. If you don't need long conversations, ollama run llama3.1 --num-ctx 2048 saves VRAM.
• Monitor with nvidia-smi: Run watch -n 1 nvidia-smi (NVIDIA) or check Activity Monitor (Mac) to track memory usage.

Beyond Chat: What Else Can You Run Locally?

• Image generation: Stable Diffusion, Flux, and DALL-E alternatives via ComfyUI
• Code assistants: Tabby or Continue with local models for VS Code/Cursor
• Voice assistants: Whisper (speech-to-text) + local LLM + Piper (text-to-speech)
• Document Q&A: Upload PDFs and chat with them using Open WebUI's RAG feature
• AI agents: Frameworks like CrewAI and AutoGen work with local Ollama models

The Verdict

The barrier to entry for local AI has never been lower. A $1,399 Mac Mini or a $700 used RTX 3090 build gets you a capable local AI setup. Install Ollama, download a model, and start chatting in under 10 minutes.

For the best experience: pair a 24GB GPU with Ollama and Open WebUI. You'll have a private, unlimited, offline-capable AI assistant that rivals cloud services — with zero monthly fees.