Ollama

What it is

Ollama is a tool for running open-weight language models on your own computer. You install it, pull a model by name, and it handles downloading the weights, loading them onto your GPU or CPU and serving them. It runs on macOS, Windows and Linux, and there is an official Docker image.

Under the hood it builds on llama.cpp, the open-source inference project, and wraps it in a simple command-line interface and a local HTTP API. The model library on ollama.com lists many open model families; the README mentions Gemma, Qwen, DeepSeek, gpt-oss, Kimi, GLM and MiniMax among others.

The software is MIT licensed and free to use locally. Ollama has also added an optional cloud service: you can sign in and run larger models on Ollama’s servers through the same interface, with a free tier that includes starter credits and paid plans with more usage. This page is mostly about the local side, which is what most people mean by “Ollama”.

One thing to keep in mind: Ollama’s licence covers Ollama itself. Each model you download has its own licence, and some are more restrictive than others. Check the model’s page before using it in a commercial product.

Where it fits in AI-driven worlds

Local models are interesting for games and simulations for reasons that have little to do with matching frontier models on quality:

• No per-call cost. If you want hundreds of simulated agents each making decisions every few seconds, paying per token for a hosted model gets expensive quickly. Locally, the only cost is your hardware and electricity.
• Offline and private. Nothing leaves the machine, which matters for some players, some data and some jurisdictions.
• Reproducible experiments. A specific model version sits on your disk and does not change underneath you, which is useful for research-style simulation work.
• Prototyping runtime AI. Before deciding whether non-player characters need a large hosted model, you can test the design with a small local one.

Because Ollama exposes a REST API on localhost:11434, and also an OpenAI-compatible endpoint under /v1, connecting it to a game server, a Godot project or a Python simulation is straightforward. Ollama also integrates with coding agents such as Claude Code and Codex, so you can point some AI coding tools at a local or Ollama-hosted model instead of a commercial API.

Strengths

• Very easy setup. One installer, one command to run a model. It removes most of the friction that used to come with local inference.
• Consistent API. The same HTTP interface regardless of which model you load, and OpenAI compatibility for existing client libraries.
• Good model library. Popular open models are packaged and named consistently, in several sizes.
• Customisable. A Modelfile lets you set a system prompt and parameters and save the result as your own named model, which is handy for giving a character a fixed persona.
• Free and open source. MIT licence for the tool itself.

Limits

• Hardware decides everything. Model size, speed and quality are bounded by your RAM and GPU memory. Small models run on ordinary laptops; larger ones need serious hardware.
• Smaller models are weaker. Local models that fit on consumer hardware are generally less capable than frontier hosted models at complex reasoning and long coding tasks. For development work, a hosted model is still the better choice.
• Shipping to players is harder. Running a model on a player’s machine means a large download and wildly varying performance. Most games that use local models today are experiments or PC-only projects that accept that trade-off.
• Model licences vary. As above, the tool is MIT licensed, but individual models are not necessarily.

Getting started

Install it on macOS or Linux with:

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

On Windows, download the installer from ollama.com or run the PowerShell command given in the README. Then pull and chat with a model:

ollama run gemma4

The local server listens on port 11434. To call it from your own code:

curl http://localhost:11434/api/chat -d '{
  "model": "gemma4",
  "messages": [{ "role": "user", "content": "Describe a market town in two sentences." }],
  "stream": false
}'

Swap gemma4 for any model from the library that suits your hardware. If you already use an OpenAI client library, point its base URL at http://localhost:11434/v1/; it requires an API key value, but Ollama ignores it.

Verdict

Ollama is the simplest way to run open models locally, and that makes it the obvious starting point for simulations with many agents, offline games and privacy-sensitive experiments. Do not expect frontier-level quality from models that fit on a laptop, and check each model’s licence. For experimenting with runtime AI without a running bill, it is excellent.