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You can run an AI model on your own computer and call it from Python by starting a local model runtime, then sending it a request through that runtime’s Python library or HTTP API. Ollama is a straightforward starting point: it documents an official Python library and local endpoints at http://localhost:11434/api and http://localhost:11434/v1. A local request does not require an API key; Ollama’s hosted cloud API does. Ollama API documentation
What “running AI locally” means
Python usually does not load and execute the model itself in this setup. Instead, a runtime runs the model on your computer and exposes a local service. Your Python program sends a request to that service, which returns the model’s response. This differs from calling a hosted inference service, where the request goes to a provider’s remote endpoint.
The distinction matters in your code: a client can be configured to use a local address or a remote service. Check the base URL you actually use rather than assuming that a Python client is local just because it is installed on your computer. Ollama documents separate local and hosted API options. Ollama API documentation
Run a model with Ollama and call it from Python
Ollama is a practical first choice if you want a local runtime with both an official Python library and an HTTP API. Install Ollama using its current instructions for your operating system, then follow its current model instructions to download and run a model. Model names and commands can vary, so use the exact name and steps shown in the current Ollama documentation rather than copying an old example.
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- Install the runtime. Follow the current installation instructions on the Ollama download page for your operating system.
- Choose and run a model. Use Ollama’s current model documentation to select a model and start it. Confirm the runtime is running before you try to connect from Python.
- Install and use the official Python library. Follow the library’s current installation and request examples in the Ollama Python library documentation. Copy the current package syntax and model name from that documentation.
- Send a request to the local runtime. Ollama documents its local API base URL as
http://localhost:11434/api. It also documents an OpenAI-compatible local endpoint athttp://localhost:11434/v1. Use the interface that matches your Python client and follow its current request format. Ollama API documentation
The API endpoints and the existence of the official library are documented by Ollama, but a specific Python snippet or package release is not established here. The reliable way to avoid stale method names or parameters is to copy a working example from the current library documentation.
Choose a local runtime that fits your workflow
Ollama is not the only way to put a local model behind a Python program. Hugging Face’s guide describes several options; the distinctions below are about documented setup and interfaces, not independent performance tests. Hugging Face: Use AI Models Locally
| Option | Documented workflow | Model/runtime notes | Consider it when |
|---|---|---|---|
| Ollama | Easy-to-install local runtime; offers an official Python library and local HTTP API. | Use models supported by Ollama and follow its current model instructions. | You want a relatively direct route from Python to a local service. |
| llama.cpp | A C/C++ inference engine that can be used from the command line or deployed as a server. | Uses GGUF; the format supports quantized weights and memory mapping. Hugging Face: llama.cpp | You want to work with GGUF models or want more runtime-level control. |
| Jan | GUI workflow with an OpenAI-compatible API server. | Check the current app documentation for the models and API behavior it supports. | You prefer a desktop interface but want an API for Python to call. |
| LM Studio | Desktop app with developer tools and APIs. | Check the current app documentation for supported models and API configuration. | You want to manage local models through a GUI and connect an application through its API. |
Hugging Face describes llama.cpp as “a C/C++ inference engine for deploying large language models locally.” Its guide covers the runtime and GGUF workflow. Hugging Face: llama.cpp
Check model compatibility and your computer before choosing
A runtime’s interface does not tell you whether a particular model will run well on your machine. Check the chosen model’s card and the runtime’s current instructions for compatibility with the model format and your operating system. Hardware affects whether a model can run and how responsive it feels, but there is no universal minimum memory or GPU specification, or reliable speed estimate, established for every model and computer.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute- Confirm the model is supported by the runtime and available in the format that runtime expects.
- Check the model card and current runtime guidance for hardware requirements before downloading.
- Do not infer a speed advantage from the runtime name alone: performance depends on the model, its configuration, and the computer.
Keep the local and hosted endpoints distinct
For Ollama, local requests and cloud requests are separate choices: its documentation says local API requests do not need an API key, while its hosted cloud API does. Verify that your code’s base URL points to the local address if your intention is to run inference on your own computer. If you configure it to use a hosted endpoint instead, the request is no longer being served locally. Ollama API documentation
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