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Yes—you can run an AI model without sending prompts to a cloud provider. Install a local inference runtime such as Ollama, LM Studio, or llama.cpp, download compatible model weights, and direct requests to the runtime on your own computer. After the model and runtime are installed, local chat can work without an internet connection. The privacy boundary is not created by installation alone: model downloads, updates, web search, optional cloud models, account services, and other connected features can still make network requests.
This guide shows how to set up local inference, choose an appropriate workflow, verify that prompts stay on the machine, and avoid the most common privacy and hardware mistakes.
What “local AI” actually means
In a local setup, the model weights are stored on your computer and the computer performs inference—the calculation that turns your prompt into a response. Your text is sent to a local process, such as an Ollama command, an LM Studio chat window, or a llama.cpp server, rather than to a vendor’s remote API.
That does not mean the entire application is permanently disconnected. You normally need internet access to download the runtime, obtain model files, download runtime components, or install updates. An application may also offer cloud-hosted models, web search, telemetry, account features, or integrations that contact external services. Treat those as separate features and disable or avoid them when a strict local-only boundary matters.
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LM Studio’s offline documentation says that, once a model is on the computer, local chat, local document processing, and its local server can operate without internet access. The same documentation notes that searching for or downloading models, downloading runtimes, and checking for updates require connectivity: LM Studio Offline Operation documentation.
For the strongest practical check, download the software and model from a trusted source, turn off optional cloud functions, then disconnect the computer from the network and run a test prompt. That is a useful verification procedure, not a guarantee that every application component is incapable of network traffic. A firewall or isolated machine can add another control, while device access controls protect prompts and chat histories saved locally.
Choose the runtime that matches your workflow
There is no universal performance winner established by the available documentation. Select by interface, model format, operating-system support, and how much configuration you want to manage.
| Runtime | Best fit | How you use it | Offline behavior after setup | Important considerations |
|---|---|---|---|---|
| Ollama | People who prefer a straightforward command line or local API | Install the service, then run commands such as ollama run llama3.2; applications can call its local REST API |
Local models can run offline; cloud-hosted models and other connected features are a separate boundary | Simple model management, with platform instructions for macOS, Windows, and Linux |
| LM Studio | People who want a desktop model browser and chat interface | Install the desktop application, download a compatible model, and chat locally or start its local server | Core local chat, document processing, and the local server do not require internet after setup | Model discovery, downloads, runtime downloads, and update checks use connectivity |
| llama.cpp | Users comfortable with command lines, servers, and detailed configuration | Provide a local GGUF model file to commands such as llama-cli -m my_model.gguf, or run its HTTP server |
Runs from local files; you control the server and options | Offers quantization choices, CPU/GPU hybrid inference, multiple installation methods, and an OpenAI-compatible local HTTP server |
These are workflow distinctions from each project’s documentation, not a controlled benchmark. For a particular task, speed and response quality depend on the model, quantization, context length, hardware, and settings.
Check hardware, memory, and disk space first
Model size is only a starting point. Runtime overhead, quantization, context length, operating-system memory, and the number of simultaneous requests all affect what your computer can handle.
Published memory guidance
Ollama’s quickstart gives these approximate figures: 8 GB of RAM for 7B models, 16 GB for 13B models, and 32 GB for 33B models. They are vendor guidance, not universal minimums. A model may load with less memory in some configurations, while a longer context or concurrent requests may require more.
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Model files can be much larger than expected
The same Ollama documentation lists these example file sizes:
| Example model | File size listed by Ollama | What the number means |
|---|---|---|
| Moondream 2 | 829 MB | Listed model-file size, not a promise of total runtime memory |
| Llama 3.1 8B | 4.7 GB | Listed model-file size; caches and operating-system space are additional |
| Llama 3.1 70B | 40 GB | Large download and substantial memory requirement |
| Llama 3.1 405B | 231 GB | Very large file; storage and hardware requirements are correspondingly high |
Leave room for more than the first model you download, model caches, logs, and normal operating-system files. An external SSD can provide additional space when the internal drive is too small. It is optional storage, not a requirement for local inference, and it does not automatically make generation faster.
Quantization and GPU memory
llama.cpp supports multiple quantization levels that reduce memory use, with model- and configuration-specific trade-offs in quality and speed. It also documents CPU/GPU hybrid inference when a model is larger than available video memory. Quantization is a way to fit a model into a given machine; it is not a guarantee of a particular response quality or tokens-per-second result.
Prepare a privacy-first setup
- Decide what must remain local. Identify whether prompts, uploaded documents, generated responses, logs, and chat history are sensitive. Local storage means those items may remain on the computer, so use an encrypted account or disk and restrict access to the machine.
- Use official project downloads. Obtain the runtime and model from a source you trust. Keep a record of the model file and version so you know what is installed.
- Plan the connected phase. Download runtimes, model weights, and required components while internet access is available. Updates and model discovery generally need connectivity.
- Turn off optional cloud functions. Do not select cloud-hosted models, remote web search, online connectors, or features that submit content to a service. Local and cloud modes can coexist in one application, so check the selected model and server endpoint for every session.
- Verify in an offline state. Disconnect Wi-Fi and wired networking, then run a harmless test prompt. If the local runtime still responds, the core inference path is available without a network. Reconnect only when you intentionally want updates or another download.
Install and run a model with Ollama
Ollama is a command-line-oriented runtime that also exposes a local API. Its official quickstart covers installation on macOS, Windows, and Linux: Ollama Quickstart.
Basic procedure
- Install Ollama for your operating system using the project’s current installer or documented Linux method.
- Open a terminal after installation.
- Start with a relatively small model that your computer can accommodate. The quickstart example is
ollama run llama3.2. The first run downloads the model if it is not already present, so internet access is required at that point. - Enter a simple prompt and wait for a response. The command should use the local model once its files are present.
- For applications, point the client at Ollama’s local REST API rather than a hosted API endpoint. Keep the address and credentials local to the machine and review any application that is sending requests.
- After downloading, disconnect from the network and run another prompt. A response while offline confirms that this model is available locally.
Ollama’s privacy boundary
Ollama’s privacy policy says prompts and responses processed locally are not collected, stored, or transmitted by Ollama. It separately states that the service may collect limited device and usage metadata and metadata related to model downloads, and it distinguishes local use from cloud-hosted models. Read the current policy at Ollama’s Privacy Policy and do not interpret its local-inference statement as a claim that every application feature is permanently network-free.
Install and run a model with LM Studio
LM Studio provides a desktop application for discovering models, downloading compatible files, chatting locally, processing documents locally, and serving a model to other programs on your computer.
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Basic procedure
- Download and install the desktop application for your operating system.
- While connected, use the application’s model search and download workflow, or import a compatible model file you already obtained.
- Select the downloaded model in the local chat interface and start with a short prompt.
- If you need programmatic access, start LM Studio’s local server and configure your client to use the local address it displays. Confirm that the client is not pointed at a cloud provider’s URL.
- Disable or avoid online search, cloud models, remote connectors, and other features that transmit content.
- Close the network connection and test local chat and, if relevant, the local server. Updates, model searches, and new downloads will not work until connectivity is restored.
LM Studio’s desktop privacy policy is effective June 2026 and should be read alongside its offline-operation documentation: LM Studio Desktop App Privacy Policy.
Install and run a model with llama.cpp
llama.cpp is a lower-level option for users who want direct control over model files, quantization, hardware backends, and server settings. Its README documents package-manager, Docker, prebuilt-binary, and source-build paths, as well as GGUF model files and an OpenAI-compatible local HTTP server: llama.cpp README.
Basic procedure
- Install a build appropriate for your operating system using one of the project’s documented methods.
- Obtain a compatible GGUF model file. Download it while connected, or copy it from trusted offline media.
- Place the file in a directory with enough free space and note its exact path.
- Run the command-line client, replacing the example path with your file:
llama-cli -m my_model.gguf. - Adjust context length, GPU layers, threads, and quantization only after the basic command works. Each setting changes memory use or speed, and the best values depend on your model and hardware.
- If another local application needs access, start llama.cpp’s HTTP server and configure that application for the local server address. Do not substitute a remote endpoint.
- Disconnect the computer from the network and repeat a prompt to verify that the model file and runtime are sufficient on their own.
How to confirm prompts are not leaving the computer
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Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Inspect the selected model. Confirm that the session uses a downloaded local model, not a similarly named cloud model.
- Inspect the endpoint. Local clients normally use a loopback address such as
localhostor127.0.0.1. A public hostname or provider URL indicates a different path. - Disable optional services. Turn off web search, online retrieval, cloud chat, telemetry choices you do not need, and third-party connectors.
- Test without networking. Disconnect the machine and send a non-sensitive prompt. Failure may simply mean the model was not downloaded or a feature still expects a connection; investigate before using sensitive material.
- Check application settings and logs. Look for proxy settings, remote API keys, synchronization, or plugins that could forward text even when the main model is local.
- Protect local files. A prompt that never leaves the computer can still be read by another user, malware, backups, or a poorly secured shared folder.
Common failure modes and fixes
The model will not download
Check connectivity, available disk space, and the model’s compatibility with the runtime. A download normally requires internet access. If the computer is intentionally offline, transfer the complete model file through trusted media and import or reference it locally.
The runtime reports insufficient memory
Choose a smaller model, a lower-memory quantization, a shorter context, or fewer simultaneous requests. Close other memory-intensive programs. A model’s file size does not equal its complete runtime footprint.
Generation is extremely slow
Verify that the runtime is using the intended CPU or GPU backend, reduce the context length, and try a smaller or more heavily quantized model. llama.cpp can use CPU/GPU hybrid inference, but the useful split depends on the hardware and configuration. No single setting is guaranteed to be fastest.
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The application still asks for an internet connection
Separate local inference from connected conveniences. Model search, runtime downloads, update checks, web search, cloud models, and account services may still need a network even when the downloaded model itself runs locally. Finish setup while connected, then disable those functions or operate the machine offline.
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Confirm that the runtime process is running, the client uses the correct local port, and the model is loaded. Check whether the client expects a particular API format; llama.cpp documents an OpenAI-compatible server, while other runtimes may expose their own local API conventions.
Responses stop when the network is disconnected
Check whether the chosen model is actually local, whether the application is invoking web search or a cloud model, and whether a required runtime component was never downloaded. Reconnect only long enough to complete the missing local setup, then test again.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing a model without overpromising hardware
Start with the task rather than the largest available model. A small model is easier to download, fit into memory, and validate offline. Increase model size only when its quality, context handling, or instruction-following is important enough to justify additional storage and memory.
- Short everyday chat: begin with a small model and verify that latency and answer quality are acceptable.
- Long documents: check the model’s context support and reserve memory for the larger prompt, not just the model file.
- Local coding assistance: compare models using the same language, files, context length, and runtime settings rather than assuming a larger parameter count is always better.
- Multiple users or applications: budget for concurrent memory use and consider a local server with access controls.
The available documentation does not provide a controlled comparison of model quality, context limits, or speed across these runtimes. Measure your own workload if those factors determine the purchase or deployment decision.
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A practical decision guide
- Choose LM Studio when a desktop interface, visual model discovery, and local chat are your priority.
- Choose Ollama when you want a simple command-line workflow, easy local model management, or a local API for applications.
- Choose llama.cpp when you need direct control of GGUF files, quantization, hardware backends, or server parameters and are comfortable troubleshooting from a terminal.
Whichever route you choose, the privacy result depends on the complete configuration: local weights, a local endpoint, no cloud model or web-search feature, and a device whose files and network access are under your control.
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FAQ
Can I install everything while online and then use the model completely offline?
Usually, yes. Download the runtime, model weights, and required components first. Then disconnect the computer and use the local chat or local server. You will need to reconnect for new models, runtime downloads, and updates.
Does local inference guarantee that no data is collected?
No. It keeps inference on your machine, but an application may collect limited device, usage, or download metadata and may offer connected features. Review the relevant policy and disable services you do not want. Ollama describes local prompt and response content as excluded from its stated collection, while distinguishing that from cloud-hosted use.
Do I need a dedicated GPU?
Not necessarily. CPU inference and CPU/GPU hybrid operation are possible, depending on the runtime and model. A GPU can change speed and memory capacity, but the sources here do not establish a universal hardware requirement or performance threshold.
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Is an external SSD required?
No. It is useful only when the internal drive lacks room for model files, caches, and normal system data. It stores the files; it does not by itself improve inference speed.
Frequently Asked Questions
Can I install everything while online and then use the model completely offline?
Usually, yes. Download the runtime, model weights, and required components first. Then disconnect the computer and use the local chat or local server. You will need to reconnect for new models, runtime downloads, and updates.
Does local inference guarantee that no data is collected?
No. It keeps inference on your machine, but an application may collect limited device, usage, or download metadata and may offer connected features. Review the relevant policy and disable services you do not want.
Do I need a dedicated GPU?
Not necessarily. CPU inference and CPU/GPU hybrid operation are possible, depending on the runtime and model. A GPU can change speed and memory capacity, but there is no universal hardware requirement.
Is an external SSD required?
No. It is useful only when the internal drive lacks room for model files, caches, and normal system data.
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