Ollama

Four useful starting projects

• Prototype a private text helper: Use a local model to draft or categorize non-sensitive sample text first. Check whether the full application, not only the model runtime, keeps the intended data local.
• Compare small models: Run the same short task on two models that fit the computer. Compare factual accuracy, formatting and response time using the same inputs, rather than relying on unrelated provider benchmarks.
• Power a local knowledge app: Connect a compatible document application to the local API. Remember that parsing, embeddings, retrieval and generation may use different components, each of which needs its own configuration review.
• Learn API integration: Build a small program that sends a prompt and validates the response. Include a failure case and a long input so the program can handle resource limits instead of assuming every call succeeds.

Platforms, inputs, outputs and setup

Official routes cover Windows, macOS and Linux. GPU support and acceleration vary by platform and device, and a larger context consumes more memory. The FAQ’s running-model inspection can help determine whether work is on CPU, GPU or a combination.

The local service binds to localhost by default. Exposing it to a network changes who may reach it and requires an appropriate access-control design. Do not casually turn a private local experiment into an unauthenticated public model endpoint.

Data, permissions and licensing

Ollama’s FAQ states that local prompts and data are not seen by the company when models run locally. Confirm that you selected a local model and that the surrounding application does not separately upload documents, logs or prompts.

Keep the API restricted and inspect connected tools before granting access to files or command execution. Local inference can improve control over data, but it does not automatically secure the rest of an agent workflow or remove the need for backups and access boundaries.