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Yes. A local AI model can generate responses offline once its runtime and model files are on your device. You generally need internet access to download them first; after setup, inference can happen on your computer if the app and the features you use do not rely on a cloud service.
What “offline AI” means
A local model’s files are stored on your device, and your device performs the computation that generates a response. For example, llama.cpp can run a server using a model file at a local path, and its documentation describes an --offline option that uses cached resources and prevents network access. See the llama.cpp server documentation.
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Offline generation is different from asking an online AI service to answer a prompt: the local workflow does not need to send that prompt to a remote model. But “local” describes where inference runs; it does not, by itself, establish that every app feature, plugin, or telemetry behavior is network-free.
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What you need before disconnecting
- Choose a runtime or desktop app. Options documented by Hugging Face include command-line and server tools such as llama.cpp and Ollama, and desktop apps such as Jan and LM Studio. llama.cpp supports CPU inference and accelerator paths including CUDA and Metal. See Hugging Face’s local applications guide.
- Download the runtime and model while connected. Ollama’s quickstart documents pulling models, as well as importing a GGUF model from a local path. llama.cpp can start from a local model file. A model that is not already on the device cannot be fetched after you disconnect.
- Check storage and memory. Leave enough disk space for the model and enough RAM or video memory for the runtime to load and use it. Model size, quantization, context length, hardware and runtime all affect whether it fits and how responsive it feels.
- Test the exact offline workflow. Start the model while connected, then disconnect and try a prompt. Check whether any feature you depend on—such as web search, a remote API, or a cloud add-on—stops working. The model itself may remain usable even when those connected features do not.
How much hardware does a local model need?
There is no single minimum that applies to every model and computer. Larger models generally need more storage and memory, and may respond more slowly on less capable hardware. Match the model to available RAM or video memory and the speed you need; a published size is not a performance guarantee.
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Ollama’s live quickstart, whose publication year is not stated, gives these examples of model sizes and RAM guidance:
| Ollama documentation example | Published model size | Published RAM guidance |
|---|---|---|
| Llama 3.2 3B | 2.0 GB | Not stated for this example |
| Llama 3.1 8B | 4.7 GB | At least 8 GB available for 7B models |
| Llama 3.1 70B | 40 GB | At least 32 GB available for 33B models; no guidance stated for this 70B example |
| Llama 3.1 405B | 231 GB | Not stated for this example |
These are the figures and guidance Ollama publishes, not universal requirements. The RAM guidance names 7B, 13B and 33B model classes; it does not specify a requirement for every model in the size examples above. See the Ollama quickstart for its current guidance.
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What stops working when you are offline?
A disconnected model cannot retrieve live webpages or call a cloud service unless the workflow has some separate, already available local data or tool. Its stored knowledge also does not update itself. It can still respond using what it learned during training and, where the app supports it, text or files you provide locally.
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- Usually available: generating responses with model files already on the device.
- Unavailable without a separate local setup: live web results, remote APIs and other features that require a network connection.
- Depends on the app and configuration: plugins, file workflows and other bundled features. Verify each feature rather than assuming that “local” makes the entire application offline.
Which kind of local setup should you choose?
| Approach | Examples | Best fit | What to check |
|---|---|---|---|
| Command line or local server | llama.cpp, Ollama | People comfortable managing model files and runtime settings | Local model path, supported hardware backend, and whether the workflow stays on-device |
| Desktop graphical app | Jan, LM Studio | People who prefer selecting and using models through a GUI | That the model is downloaded and the specific features needed offline do not call remote services |
Hugging Face lists these as options for running models on a user’s machine; app capabilities and settings can change. Choose based on interface preference and hardware support, then verify offline behavior for the particular version and workflow you plan to use.
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