Install Ollama in 5 Minutes — Step by Step (Windows, Mac, Linux)

5 minutes. Then qwen3.5:4b runs on your own hardware.

No cloud account, no API costs, no data being uploaded anywhere. Tested on Windows 11, macOS Sonoma, and Ubuntu 24.04.

What Is Ollama?

Ollama is a local LLM runner — a program that executes language models on your own hardware and exposes an OpenAI-compatible API. That means: tools built for ChatGPT work with Ollama without modification. We run 3 Ollama instances on different hardware in 24/7 operation. This is the setup that works.

Installation

Windows 11

Option 1: winget (recommended)

winget install Ollama.Ollama

Option 2: Direct download Download the installer from ollama.com/download and run it. Ollama then runs as a Windows service in the background.

GPU support (NVIDIA) is detected automatically if CUDA drivers are installed — no additional configuration needed. AMD GPUs are supported via ROCm — details in the Ollama GitHub.

macOS (Sonoma, Ventura, Monterey)

brew install ollama

Without Homebrew: direct download at ollama.com/download/mac. Apple Silicon (M1/M2/M3/M4) is fully supported — the integrated GPU is used automatically.

Linux (Ubuntu 24.04, Debian, Fedora)

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

The installer sets up Ollama as a systemd service. After installation, Ollama starts automatically at boot. GPU support for NVIDIA and AMD is detected if drivers are present.

First Test: Load and Run a Model

ollama run qwen3.5:4b

On the first run, the model downloads (~2.5 GB). Then an interactive chat session starts directly in the terminal:

>>> Explain Docker in one sentence.
Docker is a platform that packages applications in isolated containers so they
run the same everywhere — regardless of the host system.

>>> /bye

/bye ends the session. The model stays stored locally and is immediately available again.

The API runs in parallel at http://localhost:11434. Test it:

curl http://localhost:11434/api/tags

This returns all locally available models as JSON.

Which Model for Which VRAM?

VRAM is the limiting factor — not RAM. If your GPU doesn't have enough VRAM, the model continues running on the CPU (noticeably slower, but functional).

We use qwen3.5:27b on an RTX 3090 (24 GB) as our primary model — Ollama Model Library lists all available models with size information.

No dedicated GPU VRAM? No problem. qwen3.5:4b runs on CPU too — slower, but perfectly fine for first tests. On a modern laptop processor that's roughly 3-8 tokens per second.

Model Management

# Show all locally available models
ollama list

# Download a model without immediately starting it
ollama pull llama3.2:3b

# Remove a model
ollama rm llama3.2:3b

Models are stored at ~/.ollama/models (Linux/macOS) or C:\Users\<name>\.ollama\models (Windows). On an SSD with at least 20 GB free space we recommend qwen3.5:4b plus a second model for comparison.

What's Next?

Ollama is running. The API responds. That's the foundation. What's still missing is a browser interface so you can chat without a terminal — and a clean configuration so Ollama reliably starts after a reboot.

Continue to Step 4: Set Up a Browser Interface with Open WebUI →

Or go straight to the complete setup — the Local AI Playbook P1 includes pre-configured Docker Compose files for Ollama + Open WebUI + monitoring, detailed instructions for all operating systems, and the complete stack we run in production ourselves. The contents of this playbook are being reworked and will appear, tested, in the Hub. Continue the learning path

Sources: ollama.com — official documentation. github.com/ollama/ollama — source code and GPU support details. ollama.com/library — complete model library with size information and benchmarks.