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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteYou can keep AI prompts on your own computer by installing a local inference runtime, downloading compatible model weights, and using the runtime’s local chat or command-line interface. Internet access is normally needed for the initial runtime and model downloads; after setup, local inference can continue offline if you avoid cloud models, web search, update checks, and other online features.
What “local AI” actually means
In a local setup, the model weights and inference process reside on your computer. Your prompt is passed to software running on that machine rather than to a hosted provider’s inference endpoint. The practical privacy boundary is therefore the runtime and the features you enable, not merely the fact that an application is installed on your computer.
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Setup can still involve network services. Downloading the runtime, finding or downloading model files, retrieving updates, signing in to an account, or using an optional cloud model can create network traffic. Some applications also offer web search or cloud-assisted tools that deliberately send information away from the computer.
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The same documentation says that searching for and downloading models, downloading runtimes, and checking for application updates require connectivity. Read the full qualification in LM Studio’s Offline Operation documentation.
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- Local inference: the prompt is processed by the model and runtime on your computer.
- Online setup: software, model weights, and updates are fetched over the internet.
- Cloud processing: an optional hosted model, web-search feature, or other remote service sends data to a provider and is outside a strict local-only policy.
- Local persistence: chats, logs, downloaded models, and document indexes may remain on the computer, so operating-system access controls and backups still matter.
Choose a runtime that matches your workflow
Three practical routes cover most local deployments. They differ mainly in interface and level of control; the available evidence does not establish a universal performance winner.
| Runtime | Interface | Model and hardware workflow | Setup style | Offline behavior after downloads |
|---|---|---|---|---|
| Ollama | Command line plus a local REST API | Uses the models supported by its catalog and local installation; platform documentation covers macOS, Windows, and Linux. | Install the platform package, then pull and run a model from a terminal. | Local prompts and responses can be handled locally; cloud-hosted models and other online features are a separate path. |
| LM Studio | Desktop application, local chat, document chat, and a local server | Desktop model discovery and runtime downloads are integrated into the application. | Install the app, download a runtime and model, then select the model in local chat. | Its documentation says core local chat, local document processing, and local server use do not require internet after setup. |
| llama.cpp | Lower-level command-line tools and server | Uses local GGUF files, offers quantization choices, and supports CPU, GPU, and CPU/GPU-hybrid execution. | Choose a package-manager, Docker, binary, or source installation, then provide a model file yourself. | Inference can stay on the machine; the operator controls model files, server exposure, and optional integrations. |
Ollama: the shortest terminal route
Ollama is suited to readers who want a concise command-line workflow or a local API without assembling an inference stack manually. Its official quickstart documents installers for macOS, Windows, and Linux, model management, and the first-run command. Use the current instructions at Ollama’s Quickstart for your operating system.
The quickstart’s example is ollama run llama3.2. On the first run, Ollama downloads the model if it is not already present, so that step needs an internet connection unless you have obtained the model through another legitimate route. Once the model is on disk, the same command can be used in an offline environment.
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Ollama’s privacy policy says prompts and responses processed locally are not collected, stored, or transmitted by Ollama. The policy distinguishes that local processing from cloud-hosted models and also describes limited device, usage, and model-download metadata that may be collected. Treat that as a statement about the provider’s policy, not as proof that every network request made by every installation is impossible.
LM Studio: a guided desktop workflow
LM Studio is the natural choice if you prefer a graphical model browser, a chat window, local document processing, or a locally hosted server. Download the desktop application and its runtime while online, download a model compatible with your computer, and start a local chat session. After those assets are present, the vendor’s offline documentation says local chat, document processing, and local server operation work without internet access.
Model searching and downloading, runtime downloads, and update checks remain online operations. If you need a strict boundary, perform those tasks before disconnecting, then avoid cloud-model and web-search features. LM Studio also publishes a desktop-app privacy policy, effective June 2026, at LM Studio’s Desktop App Privacy Policy; review the current policy before deploying it with sensitive material.
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llama.cpp: maximum control from the command line
llama.cpp is a lower-level option for users who want explicit control over model files, quantization, hardware backends, and server behavior. Its README documents package-manager, Docker, prebuilt-binary, and source-build paths, along with GGUF model files, CPU/GPU hybrid inference, and an OpenAI-compatible local HTTP server. See the project documentation at the llama.cpp README.
After placing a compatible GGUF file on the computer, the README’s command-line pattern is llama-cli -m my_model.gguf. Replace the example filename with the actual path to your model. Quantization can reduce memory requirements, while hybrid execution can let part of a model use system memory when it does not fit entirely in available VRAM. The resulting speed and output-quality trade-offs depend on the model and configuration; no universal ranking is established here.
Prepare the computer before downloading anything
Do these checks before selecting a large model. A model’s advertised file size is only one part of the resource requirement: the runtime, operating system, context length, quantization, caches, and other applications also consume memory or storage.
- Operating system: confirm that the runtime provides an installer or build path for your system. Ollama documents macOS, Windows, and Linux; llama.cpp documents several installation methods; LM Studio distributes a desktop application.
- Memory: use the published guidance as a starting point, not a guaranteed minimum. Ollama lists 8 GB of RAM for 7B models, 16 GB for 13B models, and 32 GB for 33B models. These are vendor figures, not universal requirements.
- Disk space: reserve room for the model file, additional models, caches, and normal operating-system use. Do not fill the system drive to its limit.
- Graphics hardware: determine whether your runtime can use your GPU, CPU, or a hybrid arrangement. A model that fits in system memory may still run slowly, while a model that exceeds VRAM may require offloading or a smaller quantized variant.
- Data policy: decide whether chats, uploaded documents, and logs may remain on the computer and who can access the device.
Step-by-step: install, run, and verify a local model
1. Select the privacy boundary first
Write down what “no cloud prompts” means for your use case. The strictest interpretation allows model inference only on the computer, with no web search, cloud model, remote document processing, or telemetry policy that you have not reviewed. A less restrictive setup may allow update checks while still keeping prompt text local. Making this decision first prevents an online convenience feature from quietly changing the data path.
2. Install the runtime from its official documentation
Download the installer or package only from the project’s official documentation. For Ollama, follow the operating-system-specific path in its quickstart. For LM Studio, install the desktop application and the runtime it offers. For llama.cpp, choose a package-manager package, Docker image, prebuilt binary, or source build described in its README.
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3. Obtain a compatible model while online
Use the runtime’s model catalog or a trusted model source, and check the model’s format and hardware expectations. Downloads normally require internet access. If the target computer must never connect to the internet, obtain the model file on another trusted machine, scan and verify the transfer, and copy it to removable or shared storage under your organization’s rules.
Do not assume that a model that runs in one runtime will load unchanged in another. llama.cpp specifically expects compatible GGUF files; a desktop application or a different runner may support a different set of formats or conversion steps.
4. Start with a small model
A small first run confirms that the runtime, model format, and device work before you spend hours downloading a large file. In Ollama, the documented example is ollama run llama3.2. In llama.cpp, use the documented pattern llama-cli -m my_model.gguf. In LM Studio, select the downloaded model in local chat and send a short, non-sensitive prompt.
Check that the answer appears without an account sign-in, a browser hand-off, or a request to enable a cloud feature. Record the model name, file location, runtime version, and any hardware mode shown by the application.
5. Turn off or avoid online features
Before entering sensitive text, disable or decline optional cloud-model integrations and web-search functions. Avoid pressing a model-search or update button while testing a disconnected workflow. If the application offers a local-server mode, bind and expose it only as broadly as your use case requires; a local service should not automatically be reachable from other machines.
6. Test the offline boundary
Close any browser tab or account workflow used for downloading. Then disconnect the computer from the network or apply the organization’s approved firewall rule and send a harmless test prompt. A successful response demonstrates that this particular model and runtime do not need a live connection for that local operation. It does not prove that every application component is network-free in every state, so repeat the check after major updates or configuration changes.
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7. Protect the local files
Local processing moves the exposure point from a provider’s servers to your computer. Use a strong device password, full-disk encryption where appropriate, separate user accounts, and backups that match the sensitivity of the chats and documents. Decide how long to keep conversation history and model-generated files. A disconnected computer can still be compromised by someone with physical or account access.
How much RAM and storage do models need?
The following figures come from Ollama’s quickstart and illustrate scale. They are not promises about total memory use, speed, or compatibility.
| Model example | File size listed by Ollama | What the figure means |
|---|---|---|
| Moondream 2 | 829 MB | Listed model-file size; runtime overhead and caches are additional. |
| Llama 3.1 8B | 4.7 GB | Listed model-file size; an 8B label does not by itself specify RAM, context, or speed. |
| Llama 3.1 70B | 40 GB | Large download requiring substantial storage and memory planning. |
| Llama 3.1 405B | 231 GB | Very large file; leave substantial additional space for the operating system and runtime. |
Ollama’s accompanying guidance lists 8 GB of RAM for 7B models, 16 GB for 13B models, and 32 GB for 33B models. The documentation does not make those figures universal minimums. Longer context windows, quantization choices, runtime overhead, and concurrent applications can change the amount required.
llama.cpp’s quantization levels can trade some numerical precision for lower memory use, and its CPU/GPU hybrid modes can split work when a model exceeds available graphics memory. Treat any speed or quality change as configuration-specific and measure it on the exact hardware, model, context length, and settings you intend to use.
An external SSD is optional. It can provide room for model files when the internal drive is constrained, but it is storage rather than an inference accelerator. Match its capacity and connection type to the host computer, and keep enough free space for temporary files and future model versions.
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Privacy pitfalls and how to resolve them
The first prompt works, but offline mode fails
The model may not have finished downloading, or the application may be trying to retrieve a runtime, update, or remote service. Reconnect only to complete the required download, confirm that the model is available locally, then disconnect and retry. If the same model still fails offline, check the runtime’s local model list and its logs before changing models.
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A “local” app asks for a cloud model
Local and hosted models can appear in the same interface. Select the locally downloaded model explicitly and disable cloud-model or web-search controls. If the interface does not make the route clear, use a runtime with a simpler local-only command path or verify the request at the network firewall.
The model loads slowly or runs out of memory
Choose a smaller model or a quantized variant, reduce other workloads, and check whether the context length is consuming additional memory. With llama.cpp, consider a documented CPU/GPU-hybrid configuration. Do not infer that a model file’s size equals the complete runtime requirement.
The model file does not load
Check that the file format matches the runtime. GGUF files are the expected input for the llama.cpp workflow described in its README. For other runtimes, use the model formats and compatibility information supplied by that project. A partial or corrupted download should be replaced from a trusted source.
You want privacy but still need updates
Separate operation from maintenance. Run inference offline, and schedule update checks on a controlled connection after reviewing release notes and privacy settings. Keep a copy of the working runtime and model metadata so an update can be rolled back if the new version changes behavior.
You assumed “local” means no telemetry
Local prompt processing and application telemetry are different questions. Ollama’s policy excludes locally processed prompt and response content from its described collection while noting limited device, usage, and model-download metadata. Review each project’s current policy, including LM Studio’s desktop-app policy, and use network controls if your requirements are stricter than the vendor’s stated practice.
A practical decision rule
- Choose LM Studio when a desktop interface, model discovery, local document chat, and a local server matter more than command-line control.
- Choose Ollama when you want a compact terminal workflow and a local API with straightforward model commands.
- Choose llama.cpp when you need direct control over GGUF files, quantization, hardware backends, or a configurable local HTTP server.
For a serious deployment, compare the actual model quality, context length, memory use, and measured speed on the same computer and settings. The documented capabilities above describe workflow differences, not a controlled benchmark.
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