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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11You can run an AI model locally by installing a model runner, downloading the model’s weights, and loading them into your computer’s memory. For a simple command-line start, try Ollama; for a graphical download-and-chat workflow, use LM Studio; and for direct model-file or local-server control, use llama.cpp. Check hardware requirements and the model’s license before you download.
What “running an AI model locally” means
A local runner is the software that loads and runs a model; it is not the model itself. You also need the model weights—the files that contain the model—which must be downloaded and loaded into memory. LM Studio documents model files such as .gguf and .safetensors. Weights may be accessible without the model being fully open-source, and licenses differ, so check the exact model’s terms before commercial use or redistribution. LM Studio’s getting-started documentation explains its model workflow and openness caveat.
Choose a local model runner
| Option | Best fit | How you use it |
|---|---|---|
| Ollama | A short command-line workflow or a simple desktop start | Install Ollama, then run a model by name from the terminal. The quickstart example is ollama run gemma4:e2b. Ollama quickstart |
| LM Studio | A graphical app for finding, downloading, loading, and chatting with models | Choose a model in Discover, download its weights, load it, and start a chat. LM Studio getting started |
| llama.cpp | Direct control over a model file or a local server | Run a command with a local model file; its server can provide a web frontend and endpoints at 127.0.0.1:8080 by default. llama.cpp server documentation |
These options document different setup workflows, not a comparable performance test. Choose based on whether you prefer a graphical interface, a short command, or lower-level configuration.
Run your first local model
- Check compatibility and available space. Review the runner’s operating-system and hardware requirements, along with the model’s download size. GPU support can depend on the exact card, driver, platform, and backend; check the current Ollama GPU support list and LM Studio system requirements.
- Install a runner. For Ollama, choose the installer for macOS, Windows, or Linux from the official download page, then open the app or start from a terminal and follow the setup prompts.
- Choose a model and review its license. Check that the model is available to download and that its license permits your intended use. A model described as open-weight does not necessarily grant the same permissions as another model.
- Download and load the weights. In LM Studio, open Discover to download a model, then select it in the model loader. Loading allocates memory for the weights and other parameters. With Ollama, the first run downloads the selected model automatically.
- Start chatting. In an Ollama terminal, run
ollama run gemma4:e2b. Ollama’s current quickstart says this downloads the model and starts a chat on your computer. In LM Studio, start a chat after the model has loaded. - Set up an API or server only if you need one. Basic chat does not require this. For a local server, follow the llama.cpp server instructions; Ollama also documents a local API in its quickstart.
How much memory and storage do you need?
There is no single RAM figure that guarantees a model will run well. Memory use depends on the weights, context length, runtime, and whether the model fits in GPU or unified memory. Longer context windows need more memory; falling back to system RAM may be slower.
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| Guidance | Scope |
|---|---|
| About 7.2 GB download; 8 GB of available VRAM or Mac unified memory recommended | Ollama’s Gemma 4 E2B example in its current quickstart, accessed 2026-10-04. This is a model-specific example, not a universal minimum; larger context windows need more memory. Ollama quickstart |
| 16 GB or more RAM recommended; 8 GB Macs may work with smaller models and modest context sizes | LM Studio’s macOS guidance, accessed 2026-10-04. LM Studio system requirements |
| 16 GB RAM and 4 GB dedicated VRAM recommended; x64 systems require AVX2 | LM Studio’s Windows guidance, accessed 2026-10-04. LM Studio system requirements |
| Tens to hundreds of GB may be needed for model storage | Ollama’s Windows documentation, accessed 2026-10-04; this is qualitative guidance, not a fixed requirement for every user. Ollama for Windows |
Check the model’s file size and your free disk space before downloading. If internal storage is limited, an external SSD is an optional way to store model files; it is not required for every setup. Ollama’s Windows documentation also explains how to change the model storage location.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Will a local model work offline, and is it private?
Once the software and model files are on your computer, LM Studio says its core functions—including chatting with downloaded models, chatting with documents, and running a local server—do not require internet connectivity: “LM Studio can operate entirely offline, just make sure to get some model files first.” LM Studio offline operation
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You need connectivity to download the runner and model files. Local inference means prompts for that local workflow are processed by the model on your computer; it does not establish that an entire installation never communicates over a network. Treat integrations, remote API settings, and exposing a server to other devices separately. Ollama also distinguishes its local model workflow from its cloud option, and notes that local speed depends on hardware. Ollama download page
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What to check when setup is slow or fails
- The model will not load: Check available memory, model size, and context length. Try a smaller model or a more modest context configuration if the current choice exceeds the machine’s resources.
- GPU acceleration is unavailable: Confirm the exact GPU, operating system, and driver against the runner’s current compatibility documentation. Do not assume that a product family name alone guarantees support. Ollama GPU support
- The download cannot complete: Verify internet access and available disk space. On Windows, Ollama documents model storage location settings if you need to move downloads to another drive. Ollama for Windows
- Responses are too slow: Hardware affects local speed, and large models can be slow without a strong GPU. Consider a smaller model that better fits the computer rather than assuming the runner alone will improve performance. Ollama download page
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