You can run an AI assistant on your computer by installing a model runner, downloading compatible model weights, loading them into memory, and chatting through an app or web interface. The simplest route is an all-in-one desktop app such as LM Studio; a more modular setup pairs Ollama with an interface such as Open WebUI. Before choosing a model, check its memory and storage needs, your computer’s GPU support, and whether any connected features send data to a hosted service.
What “local AI assistant” means
A model runner manages the language model, while model weights are the files that define what it has learned. To chat, the runner loads those weights and associated parameters into memory so the model can generate responses. That computation can happen on your computer, but the word “local” does not automatically mean every part of an assistant is offline or stays on-device.
For example, Ollama offers local models as well as a separate cloud mode, and Open WebUI can connect to Ollama or hosted providers such as OpenAI and Anthropic. Check which model provider and optional features—such as web search or other integrations—you have enabled. A locally running model does not establish where those other services process prompts or requests. Ollama’s download page and the Open WebUI documentation describe these options.
Choose a setup path
| Route | What you install | Best fit | Trade-off |
|---|---|---|---|
| LM Studio | One desktop application for discovering, downloading, loading, and chatting with models. | Someone who wants a graphical, all-in-one workflow. | Model choice and requirements still vary; check each model and current system requirements. |
| Ollama with Open WebUI | Ollama as the model runner, plus Open WebUI as an optional separate interface. | Someone comfortable combining a runner and a browser-based interface. | There are separate components to install and configure, and Open WebUI can connect to hosted providers as well as local models. |
Both approaches can run local models. Neither makes every available model or connected feature local by default. Compare their current platform requirements, model choices, memory needs, licenses, and data connections before settling on a setup.
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Option 1: Set up LM Studio with its graphical interface
- Install LM Studio from its official site and check its current system requirements for your operating system.
- Open Discover and choose a model to download. LM Studio’s getting-started guide gives Qwen, Mistral, Gemma, and gpt-oss as examples; availability and requirements can change, so review the individual model details rather than treating the examples as recommendations.
- Open the model loader, select the downloaded model, and load it. Loading allocates memory for the model’s weights and other parameters; if the computer cannot accommodate them, choose a smaller or otherwise better-fitting model.
- Go to Chat, start a conversation, and confirm that the selected model is the one you intend to use.
LM Studio notes that models differ in licensing and in how open they are. Review the license for your intended use, particularly if you plan to redistribute or use a model commercially. Follow the current LM Studio getting-started guide for current interface details.
Option 2: Run Ollama and add Open WebUI
Ollama runs the model; Open WebUI is a separate interface you can connect to it. On Windows, Ollama’s documentation says it runs as a native background application and serves a local API at http://localhost:11434. Open WebUI documents installation through Docker, pip, uv, and a desktop app; choose the method that fits your system and follow its current installation instructions.
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- Check the current Ollama platform requirements for your operating system and graphics hardware before installing. Requirements and supported acceleration can change.
- Install Ollama using the current official instructions. The download page lists these commands, which may change: on macOS or Linux,
curl -fsSL https://ollama.com/install.sh | sh; in Windows PowerShell,irm https://ollama.com/install.ps1 | iex. Obtain commands from the official page rather than relying on an old copy. - Download a model using Ollama’s current model instructions, then run it with the runner. Choose a model whose requirements fit your available memory and disk space.
- If you want a browser interface, install Open WebUI using one of its documented methods and configure it to connect to your Ollama instance.
- In Open WebUI, confirm which provider and model are selected. If you want inference to run locally, use the local Ollama connection rather than a hosted provider; review any other enabled connected features separately.
See Ollama’s official download page, its current Windows guide and macOS guide, and the Open WebUI documentation for current installation and connection details.
Check memory, GPU, and storage before downloading
Memory and speed
There is no single minimum specification that fits every model and computer. Loading a model requires memory for its weights and other parameters, and the amount depends on the model and configuration. Compare the specific model’s requirements with available system memory and GPU memory, leaving room for the operating system and other applications.
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GPU support can affect speed, but a dedicated GPU is not a universal prerequisite. Ollama says, “Speed depends on the hardware,” and warns that larger models are slow on computers without a strong GPU. Check current platform and driver guidance for your computer; do not assume performance from another machine will transfer to yours.
Disk space
Ollama’s Windows documentation, accessed October 4, 2026, says the binary installation needs at least 4 GB; that does not include model files. Ollama says models may require tens to hundreds of GB, depending on the models selected. Its macOS documentation gives the same broad model-storage range. Check actual file sizes before downloading rather than treating that range as a fixed requirement.
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Ollama documents how to change the model storage location on both Windows and macOS. If internal storage is tight, an external SSD may be useful, but choose its capacity only after adding up the models you intend to keep. Separately, Open WebUI’s documentation reported, on September 28, 2026, compressed Linux/amd64 container image downloads of 176 MB for :slim and 1.66 GB for :main. Those are image-download sizes, not model sizes or total installed footprint.
What to do if setup is slow or does not fit
- The model will not load: Check its memory needs against available system and GPU memory, including room for other running software. Try a smaller model if the selected one does not fit.
- Responses are too slow: Ollama cautions that large models can be slow without a strong GPU. Check whether your hardware and the runner’s supported acceleration match the model you chose; no universal speed estimate applies across computers.
- There is not enough disk space: Check the model files’ actual sizes and remove downloads you do not need, or change Ollama’s model storage location using the relevant platform guide.
- The interface connects to the wrong service: Verify the selected provider and model in Open WebUI. Its ability to connect to hosted providers means the interface alone does not prove that requests go to a local model.
Confirm licensing and data handling
Before relying on a model, read its license and determine whether its terms permit your intended use. LM Studio specifically cautions that models vary in their licenses and openness. For data handling, distinguish the location of model inference from the location of any provider, search, or integration used alongside it. If you need a particular workflow to remain on-device, verify each enabled component rather than inferring that from the use of a local runner.
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