Ollama

Ollama is an open-source tool with a single goal: make running open large language models on your own computer as easy as installing an app. After installation you type one command in the terminal, and it downloads the model, sets up the runtime and drops you into a chat. No Python environments, no manual model-format wrangling.

Its real value is the local API. While running, Ollama serves an OpenAI-compatible endpoint on your machine, so a long list of AI clients, browser extensions, knowledge-base tools and coding assistants can use it as their model backend. Plenty of people install Ollama not to chat in a terminal, but to give other tools a free, offline model whose data never leaves the computer.

Be realistic about local models. What an ordinary computer runs smoothly is usually a smaller, quantised model, and the gap to cloud flagship models is obvious. It suits privacy-sensitive work, offline use, batch processing and tinkering, not replacing your strongest online assistant.

Key features

• One-command models: download and start a model in one step; the library includes mainstream open models.
• Local API server: an OpenAI-compatible local endpoint for other applications to call.
• Cross-platform: runs on macOS, Windows and Linux, using your GPU or Apple silicon for acceleration.
• Custom models: set a system prompt and parameters in a configuration file and package them as your own model variant.
• Model management: list, update and remove local models, and manage several at once.

How to use

1. Download the installer for your system from the website; after installing, the background service starts automatically.
2. Pick a model from the library that suits your hardware, paying attention to parameter size and memory needs.
3. Run it from the terminal; the first run downloads the model, then you can chat.
4. To use it in another tool, point that tool’s model address at the local Ollama service and enter the model name.
5. Remove models you no longer use; model files take a lot of disk space.

Best for

• Privacy-sensitive data: contracts, medical records and internal documents you do not want in the cloud.
• Powering local tools: a free model backend for knowledge bases, note apps and coding plugins.
• Offline settings: AI on the road or on an isolated network.
• Learning and experimenting: try different open models cheaply and learn the basics of model deployment.

Strengths and limitations

Strengths

• Very easy to install and run; one of the lowest-friction ways to run models locally.
• Strong local API ecosystem with native support in many third-party tools.
• Open source, free, and your data stays on your machine.

Limitations

• Models you can run locally are limited; complex tasks fall well short of cloud flagships.
• Demands memory and GPU; an average computer only runs small models, and slowly.
• Mostly operated from the command line with a basic built-in interface; beginners may prefer the graphical LM Studio.
• Model files are large and slow to download, and speeds from mainland China can be unreliable.