You can run a chatbot on your own computer by installing a local model runner, downloading compatible model files, and loading the model to chat. For a beginner, Ollama offers a short terminal setup; LM Studio provides a graphical workflow; and llama.cpp gives experienced users more control over command-line and server configuration.
What “running a chatbot locally” means
A local chatbot usually consists of two separate parts: a model runner, which loads and executes the model, and the model weights, which are the files containing the model. Installing a runner does not necessarily install a model. You typically download model files separately, and the chosen runner must support their format.
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Local operation means the chat can be generated on your computer rather than sent to a hosted chatbot service. It does not by itself establish that every part of an app is offline or that a particular setup has been independently assessed for privacy or security. Check the app’s documentation and settings for the behavior you need.
Choose a setup route
| Your priority | Starting point | What to expect |
|---|---|---|
| Short setup from a terminal | Ollama | Install the app, run a model command, and chat in the terminal. |
| Graphical model discovery and chat | LM Studio | Choose a model in Discover, load it in Chat, and start a conversation. |
| More manual control or a local server | llama.cpp | Work with supported GGUF models through command-line tools or a server. |
Option 1: Start chatting with Ollama
Ollama’s current quickstart supports macOS, Windows, and Linux. Download and install it using the official Ollama Quickstart, then open a terminal and run its example command:
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ollama run gemma4:e2bOllama downloads the model and starts a chat on your computer. Type a message to begin; enter /bye to exit. The same model can also be used through Ollama’s local API, if you want to connect compatible software rather than chat in the terminal.
The Gemma 4 E2B example download is about 7.2 GB, and Ollama recommends 8 GB of available VRAM—or unified memory on a Mac—for that model. Those are vendor figures for this specific example, not minimum requirements for every local chatbot. More context or a different model can change memory needs, and a system with less VRAM may use system RAM, which can make responses slower. Check the current model and platform guidance before downloading.
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Option 2: Use LM Studio’s graphical interface
LM Studio’s workflow is designed around selecting and loading models in the app. Install the latest version, then follow these steps:
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- Open the Discover tab and select a model. Confirm that its file format is supported and that its size is appropriate for your computer.
- Download the model files.
- Open the Chat tab and load the model. Loading allocates memory for the model’s weights and other parameters.
- Start a conversation in the chat interface.
Model weights are often distributed in formats such as GGUF or Safetensors. LM Studio’s getting-started documentation explains the app’s workflow and advises users to confirm they can access the weights and understand the model’s terms. See Get started with LM Studio.
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Check LM Studio’s system requirements first
These recommendations are specific to LM Studio; they should not be treated as universal requirements for other runners or models. Consult the current LM Studio system requirements before installing.
- macOS: Apple Silicon M1, M2, M3, or M4 with macOS 14 or newer is listed. The documentation recommends 16 GB or more of RAM. Macs with 8 GB may work with smaller models and modest context sizes; Intel Macs are not currently supported.
- Windows: x64 and Snapdragon X Elite ARM are supported. The x64 route requires AVX2. LM Studio recommends 16 GB of RAM and 4 GB of dedicated VRAM.
- Linux: x64 and ARM64 are supported. The documentation lists an AppImage distribution and Ubuntu 20.04 or newer; newer Ubuntu versions are described as not well tested.
Option 3: Use llama.cpp for more control
llama.cpp is a more technical route for people comfortable with command-line configuration. The project documents installation through package managers, Docker, prebuilt binaries, or a source build. Its tools can run a local GGUF model, download a supported model, or serve a model on your machine.
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llama-cli -m my_model.ggufFor a supported model hosted by Hugging Face, the project also documents using the -hf option. To launch a server, use llama-server; it provides a basic browser interface on localhost and an OpenAI-compatible endpoint. Follow the project’s documentation for the exact model syntax and backend setup. It supports CPU and GPU backends, quantization, and hybrid CPU/GPU inference, but compatibility and configuration require more hands-on work than the app-first options.
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Check whether your computer can handle the model
There is no single hardware minimum that applies to every local chatbot. The model, its file size, quantization, context size, and available hardware acceleration all affect memory use and responsiveness. A runner’s system requirements are only part of the picture: also check the requirements or guidance for the specific model you plan to load.
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- Check the model’s download size and make sure you have enough storage for its files.
- Check the runner’s current requirements for your operating system and processor.
- Compare the model’s memory guidance with your available RAM, VRAM, or unified memory. A longer context can increase memory needs.
- Check whether your exact GPU and operating-system combination is supported. Ollama documents different conditions for NVIDIA, AMD, Apple Metal, and Vulkan acceleration; support depends on platform and hardware details. See Ollama hardware support.
- Review the model’s license and terms for your intended use. Model availability, openness, and permitted uses vary.
Can you use a local chatbot without internet?
Yes, once the application and model files are on your computer, offline use may be possible. LM Studio explicitly documents offline chat, document chat, and local-server operation after the required files are present. You still need an internet connection to obtain the app and model files in the first place. See LM Studio Offline Operation.
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