How it works
Ollama runs models through a local service and terminal commands. You pick a compatible model, download the required files, and start a conversation or connect an app via the API. The official documentation shows the paths to get started with Ollama and to integrate the API.
When the model runs on your own computer, inference happens on your machine. That lets you experiment without sending every prompt to an external provider, but performance depends on the available memory, CPU and GPU. Larger models can be slow or may not fit on the machine at all. Ollama also documents a cloud models option, so using Ollama does not necessarily mean all processing is local.
The typical setup separates three things: the Ollama program, the model weights, and the app that sends requests. Weights are the parameters learned during training. The app talks to the service, receives the output, and decides how to display or use it. This separation makes it easy to test different models without rewriting the whole integration.
Practical example
Imagine a small team that wants to build an internal assistant to summarize project documents. First, the team checks whether the documents can be processed on the available computer and chooses a model that fits the task. Then they use Ollama to pull that model and try questions with test files that contain no confidential data.
If the results are useful, an internal script or service can send requests to the Ollama API. The program still needs to control which documents enter the context, validate the answers, and log errors. Running the model locally does not remove the need to protect files, review permissions, or check answer quality.
To compare models, keep the same set of questions and evaluate criteria like accuracy, latency and memory usage. A fluent response does not prove the model was correct. For tasks with real impact, check the output against the original source or ask for human validation.
What it is for
Ollama is for prototyping applications with language models, testing open models, and integrating inference into development tools. It can also support experiments without relying on a chat interface hosted by another company. For local use, the team keeps more control over the environment, but takes on installation, updates, machine capacity and service security.
It is not, by itself, an artificial intelligence model and it does not guarantee privacy or quality. It is an execution layer: behavior depends on the chosen model, the instructions, the data provided, and how the application handles the response. Before exposing the API on a network, set up authentication and restrict access as documented.
For developers, a common case is to test a local integration before deciding where to host it. The same project can use a local model during development and switch to another provider later, as long as the application handles differences in API, limits and response formats correctly. That flexibility is useful, but it should be confirmed with tests, not assumed.