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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driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteYes—you can run an LLM on your own PC without sending prompts to a cloud service. Install a local runtime such as Ollama or LM Studio, download model weights while connected to the internet, then run inference on your computer. For a strict privacy boundary, disconnect the PC or block the runtime’s network access after downloads and updates finish. Keep model files on storage you control, leave local APIs bound to localhost, and do not enable cloud models for sensitive work.
“Private” describes the inference path, not every operation performed by the application. Model searches, downloads, update checks and optional cloud features may still contact the vendor’s servers. Treat those activities separately from local chat content.
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What running an LLM locally actually means
A local LLM is a model whose weights and inference process run on your computer rather than on a remote AI service. Your prompt is tokenized, processed and turned into a response by software on the PC. The model files remain on a drive you control, and the response can be generated without an internet connection once all required components are installed.
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This is different from opening a browser-based chatbot. A browser session sends prompts to a provider’s servers, while a local runtime can process the same type of request entirely on your machine. It is also different from a local app that silently forwards selected requests to a cloud model. Always check which model is active and whether a cloud option is enabled.
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The practical privacy boundary
- Local inference: prompts, responses and document-retrieval (RAG) content are processed on the PC.
- Network operations: model catalogs, downloads, runtime downloads, update checks and telemetry can use the internet.
- Exposed services: a local API bound to a network address may accept requests from other devices, not just the computer running the model.
- Cloud modes: an optional hosted model changes the data path and should be disabled for confidential work.
Ollama’s privacy policy, updated in March 2026, states: “Ollama runs on your local device. We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” LM Studio’s offline documentation says: “Nothing you enter into LM Studio when chatting with LLMs leaves your device.” Those statements apply to local processing; they do not mean the software never makes any network request.
Check your PC and storage before installing
Start with disk space, because model files—not the launcher—usually consume the most storage. Ollama’s Windows documentation lists at least 4 GB for the application itself. Downloaded models can take tens to hundreds of gigabytes, depending on model size, quantization and how many versions you keep. Reserve additional capacity for a second model, temporary downloads, logs and updates.
Storage checklist
- Keep at least the runtime’s stated installation space free; for Ollama on Windows that is 4 GB.
- Measure available space before downloading a model, not after the download begins.
- Leave headroom for an alternate model and future updates.
- Use a portable external SSD when internal storage is limited. Put the model directory on a volume protected by your operating-system account and disk permissions.
Model size is only one part of the hardware requirement. Quantization, context length, batch size and the selected backend affect memory use and speed. There is no universal RAM, VRAM or tokens-per-second threshold that guarantees a particular experience on every model.
Choose a local runtime
Pick the interface and control level that match how you intend to work. The same model family can behave differently under different quantization formats and backends, so compare the runtime first and test the exact model on your own hardware.
| Runtime | Best fit | Operating-system coverage | Interface and API | Hardware/control emphasis |
|---|---|---|---|---|
| Ollama | People who want a simple local service and command line | Windows documentation is available; support depends on the current release | Local service with a localhost API at http://localhost:11434 |
Can use NVIDIA and AMD Radeon GPUs on Windows; straightforward service management |
| LM Studio | Users who prefer a graphical interface, model browser and local document chat | macOS, Windows and Linux | GUI plus OpenAI-like local endpoints; can serve on the local network when deliberately configured | Convenient model discovery and loading; less manual than a lower-level backend |
| llama.cpp | Developers who need direct control over inference and compiled backends | Build and platform dependent | Command-line and embeddable components rather than a single turnkey GUI | Supports Metal, CUDA, Vulkan, SYCL and OpenVINO paths, with additional ROCm-related options depending on build |
Ollama is usually the shortest path from installation to a scriptable local endpoint. LM Studio is easier when you want to search a catalog, load a model and chat through a GUI. llama.cpp is the lower-level choice when you need to compile, tune or integrate the inference engine yourself.
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Install the runtime
Ollama on Windows
Ollama’s Windows installer runs without administrator rights and installs in the user account by default. During setup, decide where model data should live. The OLLAMA_MODELS environment variable can move model storage to another drive, which is useful when the system disk is small.
- Download the Windows installer while connected to the internet.
- Run it under the Windows account that will use the models. The documented installer does not require administrator approval.
- Before downloading large models, set
OLLAMA_MODELSto a directory on the intended drive if the default location is unsuitable. - Start the Ollama service and verify that it is listening locally. Do not expose the service to your LAN unless you have a specific, trusted-device reason.
LM Studio
Install the current LM Studio build for macOS, Windows or Linux. The application provides the model-search and download workflow in its Discover tab. The runtime and model files must be obtained before you take the computer offline.
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- Open Discover and search for a model compatible with your computer and intended task.
- Download the selected files and wait for the download and verification to finish.
- Load the model in the chat interface and, if needed, enable the local server for an OpenAI-like endpoint.
llama.cpp
llama.cpp is a lower-level route. You select or build an executable, choose a compatible model format and select an accelerator backend. It is appropriate when you need to control compilation flags, memory offload or integration details, but it requires more technical setup than the other two options.
Download a model while you are online
A runtime is not an LLM. You must separately obtain model weights, and the files must fit both the machine and the model’s license. Prefer a quantized build when the full-precision model will not fit, but record which quantization you selected because it affects memory use and output quality.
Model-selection checklist
- Task: choose an instruction-tuned model for interactive question answering, a coding model for software work, or a multimodal model only when your runtime and hardware support its inputs.
- File size: confirm the download size and leave space for the runtime to unpack or cache it.
- Context length: longer contexts consume more memory even when the model’s file size is unchanged.
- License: read the model’s license before using it commercially or redistributing outputs.
- Quantization: smaller quantized files reduce resource needs but can change accuracy and style.
LM Studio’s Discover tab searches online catalogs and downloads model files. Ollama downloads models through its local service and command-line workflow. In either case, finish downloading before disconnecting the machine.
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Run a first local test
Test Ollama’s localhost API
Ollama serves a local API at http://localhost:11434. After installing a model, open PowerShell and send a small request to confirm that generation works before changing firewall rules or going offline:
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$body = @{ model = "your-model-name"; prompt = "Reply with the word local."; stream = $false } | ConvertTo-Json
Invoke-RestMethod -Method Post -Uri http://localhost:11434/api/generate -ContentType "application/json" -Body $bodyReplace your-model-name with the exact name shown by your Ollama installation. A JSON response containing generated text confirms that the service, model and API are functioning. If the request fails, check that the service is running and that the model name matches the installed files.
Test LM Studio’s local server
In LM Studio, load the downloaded model, open the local server controls and start serving it. The application provides OpenAI-like endpoints. Keep the bind address on localhost for a single-PC setup. If you deliberately serve another device, use a private network, restrict firewall access and treat every client on that network as a potential caller.
Send a short test through the interface or your OpenAI-compatible client, then stop the server when it is not needed. A local endpoint is useful for editors, scripts and document tools, but it should not be exposed simply because a client library expects an HTTP URL.
Make the setup genuinely private
The strongest practical boundary is a two-phase workflow: connect only for installation, downloads and updates; disconnect or block network access for sensitive inference.
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- Complete online preparation. Install the runtime, obtain model files, download required runtime components and apply updates.
- Identify network-dependent features. Model search, download pages, update checks, cloud models and some account features require connectivity.
- Restrict listening addresses. Leave Ollama and LM Studio on localhost unless a trusted second device has a documented need to connect.
- Disconnect for confidential sessions. Disable Wi-Fi or unplug Ethernet. If the PC must stay online for other work, create an outbound firewall rule that blocks the runtime and its helper processes.
- Use local files only. Do not paste confidential text into a cloud-backed feature, browser plug-in or remote retrieval service while assuming the model is local.
- Reconnect deliberately. Allow network access only when you are ready to search for a model, download an update or perform another required maintenance task.
What the vendors say about data
LM Studio documents that downloaded-model chats and document RAG stay on the machine. Its privacy policy, effective March 28, 2025, describes processing for update checks, model searches and downloads, email and cloud requests; local chat content is excluded unless cloud services are used. The policy also says update checks can include device and application information plus an IP address, while model searches are anonymized.
Ollama distinguishes local operation from cloud-hosted models. Local prompts, responses and model interactions are described as remaining on the device, but selecting a cloud model creates a different data path. Verify the active mode before processing sensitive material.
Understand hardware acceleration and performance
Do not buy hardware based on a single advertised speed or a universal memory rule. The actual requirement depends on the model parameter count, quantization, context length, number of concurrent requests and backend.
GPU and CPU paths
- Ollama’s Windows documentation states that NVIDIA and AMD Radeon GPUs can be used.
- llama.cpp supports accelerator backends including Metal for Apple hardware, CUDA, Vulkan, SYCL and OpenVINO, with ROCm-related paths depending on the build and platform.
- When no suitable accelerator is available, inference can run on the CPU, usually with a different speed and memory trade-off.
Measure performance with the exact model and settings you plan to use. A shorter context and a smaller quantized model may feel responsive on a machine that struggles with a larger model, while a long document prompt can increase memory use even if short chats were fast.
Protect model files, logs and documents
Local processing does not protect a computer that other people or malware can already access. Use a separate operating-system account for shared machines, enable full-disk encryption where available and apply normal file permissions to model directories, chat histories, RAG indexes and logs. Back up only to storage you trust; a backup drive is another copy of the prompts and documents you fed to the model.
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Review retention settings in the runtime and any front-end that connects to it. Delete test conversations and temporary exports that contain personal or proprietary information. If you use a local API, authenticate at the client or network layer when the software supports it, and close the server when the integration is finished.
Maintenance without breaking the privacy boundary
Schedule maintenance as an intentional online event rather than leaving the runtime permanently connected.
- Reconnect, check for runtime updates and download only the model versions you need.
- Read release notes for changes to cloud features, telemetry and default bind addresses.
- Confirm that the model directory still has adequate free space before updating.
- After maintenance, stop the server, restore the firewall block or disconnect the network.
- Retest with a non-sensitive prompt after an update to confirm that the intended local model is still loaded.
Troubleshooting common failures
“The model will not download”
Check connectivity, free disk space and the model’s availability in the selected catalog. A failed partial download can consume space; remove incomplete files through the runtime’s model manager before retrying. If you intend to work offline, finish the download and any required runtime component before disconnecting.
“The API refuses the connection”
Confirm that the Ollama service or LM Studio local server is running, then verify the port and model name. A firewall rule that blocks the runtime will also block a local client in some configurations, so test the service before applying the strict offline rule.
“Another device can reach my local server”
The server is likely bound to a LAN address rather than localhost. Change the bind setting to localhost, stop and restart the server, and remove unnecessary firewall exceptions. If LAN access is intentional, limit it to a trusted subnet and specific devices.
“Generation is too slow or runs out of memory”
Use a smaller or more aggressively quantized model, shorten the context, reduce concurrent requests and verify that the intended GPU backend is active. The correct remedy depends on the model and hardware; there is no single RAM or VRAM number that solves every case.
“I am offline but the app shows missing features”
Search, downloads, update checks and cloud requests are expected to fail without connectivity. Local chat with an already downloaded model should continue. Reconnect only for the specific maintenance action, then restore the offline boundary.
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A practical decision guide
- Choose Ollama when you want the quickest command-line setup, a simple local service and a documented localhost API.
- Choose LM Studio when a GUI, cross-platform desktop support, model discovery, local document chat and OpenAI-like endpoints matter most.
- Choose llama.cpp when you need to compile or tune the inference backend and select among accelerator paths directly.
Whichever runtime you select, the privacy result comes from the same controls: download first, run inference locally, keep services on localhost, disable cloud modes and block or disconnect the network for sensitive sessions.
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