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Understand the three parts of a local setup
- Model weights: the files for the model you want to run. Check the specific release’s model card and license before relying on claims about commercial use or redistribution.
- Runtime: software that loads the model and processes prompts on your machine. Ollama and llama.cpp are two options.
- Chat interface: an optional way to interact with the runtime. Open WebUI can connect to local runtimes, but it can also connect to hosted providers.
These layers are separate: installing a chat interface does not determine where inference happens. The selected provider or connection does.
Choose a runtime
| Option | Setup and model handling | How you interact | Connection to verify |
|---|---|---|---|
| Ollama | Install Ollama and use it to run a model locally; see the Ollama API introduction and current installation documentation. | Use the local runtime directly or connect an application through its API. | Ollama documents local API access at http://localhost:11434/api and an OpenAI-compatible local endpoint at http://localhost:11434/v1. Local requests do not require the API key used for cloud requests. |
| llama.cpp | Run compatible GGUF model files using the project’s runtime; this option offers command-line and server settings. See the llama.cpp documentation. | Chat in the terminal or run an OpenAI-compatible server. | Inference can run on your own laptop, desktop, or server; verify the server or client is configured for that local instance. |
Neither route is universally best or fastest based on the available evidence: there are no comparable benchmarks or a current model-to-hardware matrix here. Choose based on your comfort with the setup and how you want to manage models and connect applications.
Set up and test a local model
- Check storage before downloading. Ollama’s current Windows documentation says model files may occupy tens to hundreds of GB. This is a qualitative range, not a universal minimum or an estimate for every model. An external SSD can provide storage if your internal drive is short on space, but it does not replace RAM or accelerator memory. See Ollama’s Windows documentation.
- Install one runtime. Follow the current installation instructions for Ollama or the llama.cpp project. The llama.cpp route requires working with GGUF model files; Ollama documents its own model-running workflow and local API.
- Choose a compatible model. Confirm that the model format works with your chosen runtime, then read the model card and license for that particular release. The evidence here does not establish license terms for any specific model family.
- Load the model and try a representative prompt. Use a prompt similar to the work you actually expect to do. Check whether response quality and speed are acceptable on your machine before building a workflow around it.
- Add an interface only if you need one. Open WebUI can connect to local Ollama and llama.cpp servers. Select the local provider for local inference; if you select a hosted provider instead, the request is no longer being processed solely by your computer. See Open WebUI documentation.
Check hardware and performance on your own computer
There is no universal hardware requirement established here. Whether a model runs acceptably depends on its size, quantization, context, runtime, and machine configuration. Check current documentation for the particular model and test it on the target computer. The storage range in Ollama’s Windows documentation concerns disk space for model files; it should not be read as a RAM or GPU-memory requirement.
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What local inference does—and does not—mean for privacy
When a local runtime processes a prompt on your own hardware, you can avoid sending that inference request to a hosted model endpoint. Ollama’s privacy policy says it does not collect, store, transmit, or access prompts and responses processed locally. That is the vendor’s statement about local Ollama processing, not an independent audit or a guarantee covering other applications, hosted providers, or network-dependent features.
Before sending sensitive content, check the active provider, any extensions, and whether the feature you are using depends on a network service. Open WebUI can connect to hosted services as well as local runtimes, so confirm the selected connection for each workflow.
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