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To run an AI model on your computer, install a local runner, download model weights that fit your hardware, load them into memory, and start a chat. Ollama offers a short command-line route, LM Studio provides a graphical workflow, and llama.cpp is suited to people who want to work directly with model files or run a local server. A local workflow keeps inference on your machine, but the software and model downloads initially require internet access.
What “running a model locally” means
The runner is the software that loads and executes a model; it is not the model itself. You also need the model’s weights—the files that contain the learned parameters—downloaded to your computer and loaded into memory. LM Studio notes that weights may be distributed in formats such as .gguf or .safetensors, and that models differ in license and openness. Check the license for the exact model you choose, especially before commercial use or redistribution. LM Studio’s getting-started guide explains its model workflow and terminology.
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Choose a local runner
| Runner | Best fit | What the documented workflow looks like |
|---|---|---|
| Ollama | A simple terminal workflow or desktop start | Install for macOS, Windows, or Linux, then select and run a model. Its current quickstart example is ollama run gemma4:e2b. Ollama Quickstart |
| LM Studio | People who prefer a graphical app | Find a model in Discover, download its weights, select it in the model loader, then chat. LM Studio: Get started |
| llama.cpp | People comfortable with commands and local model files, or who need a local server | Run a server with a local model file; the documented server listens by default at 127.0.0.1:8080. llama.cpp server README |
These setup guides do not provide a comparable performance benchmark, so they do not establish which runner is fastest or produces the best answers. For basic chat, a server or API is optional: choose that route only if you need another app or workflow to connect to the local model.
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Option A: Start with Ollama
- Check that your operating system and hardware are supported. Ollama provides installers for macOS, Windows, and Linux on its download page; its current hardware support page lists GPU and driver guidance.
- Install Ollama from the official download page and follow the setup prompts. Open the app or start from a terminal as described in the quickstart.
- In a terminal, run
ollama run gemma4:e2b. Ollama’s quickstart documents this as an example: it downloads the model and starts a chat on your computer. The first run needs internet access to fetch the model. - When the download completes, enter a prompt in the chat. Your hardware and model choice affect how quickly responses are generated.
Option B: Use LM Studio’s graphical workflow
- Check LM Studio’s current system requirements for your operating system and computer.
- Install LM Studio, open Discover, and find a downloadable model. Review that model’s license before relying on it for a particular use.
- Download the model weights. Check the file size and ensure you have enough disk space before starting.
- Select the downloaded model in the model loader. Loading allocates memory for the weights and other parameters.
- Open a chat and send a prompt. The chat runs locally once the model files are on the computer.
Option C: Run a local server with llama.cpp
If you want an app or script to connect to a model running on your computer, llama.cpp documents a server command that loads a local model file and serves a web frontend and endpoints. Follow the server README for the exact invocation and options for your installation. Its documented default address is 127.0.0.1:8080. This is a more hands-on path than starting a chat in a graphical app.
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Choose a model your computer can handle
There is no single RAM requirement that applies to every local AI model. Memory use depends on the model weights, context length, runtime, and whether the model fits in GPU or unified memory. Larger context windows need more memory. Start with a model whose download size and memory needs fit your actual machine, and avoid treating a vendor’s example as a universal minimum.
| Documented guidance | Scope |
|---|---|
| About 7.2 GB to download; 8 GB of available VRAM, or unified memory on a Mac, recommended | Gemma 4 E2B example in Ollama’s Quickstart, 2026; not a general minimum. Ollama also notes that larger context windows need more memory and that falling back to system RAM may be slower. Source |
| 16 GB or more RAM recommended; 8 GB Macs may work with smaller models and modest context sizes | LM Studio guidance for macOS, 2026. Source |
| At least 16 GB RAM and 4 GB dedicated VRAM recommended; x64 systems require AVX2 | LM Studio guidance for Windows, 2026. Source |
GPU compatibility is specific to hardware, operating system, drivers, and backend. Ollama lists supported NVIDIA cards and driver requirements, AMD ROCm paths, Apple Metal support, and additional Vulkan support. Check its current GPU support documentation against your exact GPU and driver combination; a product-family name alone is not enough to confirm compatibility.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check storage before downloading
Model weights can take up substantial disk space. Ollama’s Windows documentation says downloaded model files may require tens to hundreds of GB, depending on what you download, and explains how to change the model storage location. Check a model’s size and your available storage first. An external SSD for local AI model storage is an optional way to add capacity if your internal drive is short on space; no specific capacity is required for every setup. See Ollama’s Windows documentation for storage-location details.
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What to expect about offline use and privacy
Downloading the runner and model files requires an internet connection. Once the files are present, LM Studio says its core features—including chatting with downloaded models, chatting with documents, and running a local server—do not require connectivity: “LM Studio can operate entirely offline, just make sure to get some model files first.” LM Studio Offline Operation.
That describes local inference, not a guarantee that every installation or workflow never communicates over a network. Treat integrations, remote API settings, and exposing a server to other devices as separate choices. Ollama distinguishes running models locally from its cloud option; its download page also notes that local speed depends on hardware. Ollama Download.
Quick Recap
Common setup problems to check
- The model will not load: Check available memory, model size, and context settings. A model that downloads successfully may still exceed the memory available for loading.
- Responses are slow: Confirm that your hardware and drivers are supported and check whether the model is using GPU or unified memory as expected. Ollama warns that system RAM fallback can be slower. Review GPU support.
- The download fails or storage fills up: Check your internet connection during setup, the model’s download size, and free disk space. On Windows, consult Ollama’s instructions for changing where model files are stored. Windows storage guidance.
- You expected a fully offline first run: Download the app and model files while connected; offline chatting becomes possible after the files are present in a supported workflow. LM Studio Offline Operation.
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