Ollama turns your own computer into a private AI lab, no subscription, no data leaving your machine, no internet required once a model is downloaded.
It’s built for developers, tinkerers, and anyone who wants to run models like Llama 3 or Mistral without paying per token. If you’re comfortable typing a command or two, it’s absolutely worth installing.
The catch: you’ll need decent RAM, and there’s no polished chat window out of the box, that part’s on you or a companion app.
How to Download & Install
Direct download: Grab the verified installer from the GramFile Ollama page, every file is SHA-256 checked before it goes live, no bundled toolbars, no sponsored installers. Click on the download button at top of this page
Installation in just 2 steps
1. Double click the setup file OllamaSetup.exe
2. Click Install and wait for the installer to finish
System requirements
- RAM: 8 GB minimum, 16 GB recommended for anything past a small model
- Disk space: roughly 200 MB–1.6 GB for the app itself depending on platform, plus 2–40+ GB per model you pull
- GPU: optional. NVIDIA (CUDA), AMD (ROCm, best supported on Linux), and Apple Silicon (Metal) are all accelerated automatically — Ollama runs fine on CPU alone, just slower
- OS: Windows 10/11 (x86_64 and ARM64), macOS 12 or later, most modern Linux distributions
License
Free and open source (MIT license). No account, no activation key, no trial period.
How to update
Windows and Mac installers check for updates automatically and prompt you when one’s ready. On Linux, re-run the install script or pull the latest Docker image. Models update independently — run ollama pull again to fetch the newest version.
Features and Full Review
What is Ollama?
Ollama is a free tool that lets you download and run large language models directly on your own hardware instead of through a cloud API.
Once installed, it works from the command line: you type ollama run llama3, it downloads the model on first use, and you’re chatting with it locally, no internet needed afterward, no per-message billing, and nothing you type ever gets sent to a third-party server.
It’s less a single app and more of a local model server. It handles the messy parts of running an LLM – memory management, GPU detection, model formats – so you don’t have to compile anything or wrestle with Python dependencies just to get a model talking back.
Running Models Locally: How It Actually Works
Every model in Ollama’s library ships in a compressed format that gets unpacked and loaded into RAM (or VRAM, if you’ve got a GPU) the first time you run it. After that first pull, everything happens offline.
That’s the whole appeal, your prompts, your documents, your code snippets, none of it leaves your machine.
For anyone handling sensitive client data or just uncomfortable feeding proprietary code into a cloud chatbot, that alone is worth the install.
Supported Models
Ollama supports a long list of open models – Llama 3, Mistral, Gemma, Phi, Qwen, CodeLlama, and dozens more – each available in multiple sizes and quantization levels.
Smaller quantized versions trade a bit of accuracy for a much smaller memory footprint, which matters a lot if you’re on a laptop rather than a workstation with 64 GB of RAM.
Hardware and GPU Acceleration
You don’t need a graphics card to use Ollama, but you’ll feel the difference if you have one. NVIDIA GPUs get CUDA acceleration out of the box, no separate CUDA toolkit install required, since Ollama bundles what it needs.
AMD cards work through ROCm, though that support is more reliable on Linux than Windows right now.
Apple Silicon Macs benefit from unified memory, meaning a 32 GB M-series machine can comfortably run models that would otherwise demand a dedicated GPU on a PC.
On a CPU-only setup with the 8 GB RAM minimum, expect a 7B model to feel usable but not fast — think a few tokens per second, fine for testing, a bit slow for long conversations.
Command Line Interface
Ollama lives in the terminal by design. A handful of commands cover most of what you’ll do day to day:
- ollama pull — download a model
- ollama run — start chatting with it
- ollama list — see what’s installed
- ollama rm — delete a model to free up disk space
- ollama serve — run the background server so other apps can connect to it
If the terminal isn’t your thing, that’s where the ecosystem picks up the slack — see below.
API and Third-Party Integrations
Ollama runs a local server (default port 11434) that’s compatible with the OpenAI API format, which means most tools built for ChatGPT’s API can point at Ollama instead with a small config change.
This is how it plugs into IDE extensions like Continue and Aider for AI-assisted coding, and into third-party chat front-ends and mobile apps that want a proper interface instead of a terminal window.
If you want a graphical chat UI, you’ll be pairing Ollama with one of these rather than relying on Ollama alone.
Docker and Server Deployment
For anyone running Ollama on a home server or a headless Linux box, there’s an official Docker image, which makes it straightforward to keep the model server isolated and running in the background, restart it automatically, and access it from other devices on the network.
