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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesYes. A desktop AI workstation can run downloaded open-weight models locally, keeping prompts and documents on the machine when the model and the tools handling them are also local. Privacy is not automatic: cloud models, web search, remote endpoints, or connected integrations can send requests outside the workstation. Internet access may still be needed to download software and models or check for updates.
What “local” means for privacy
With local inference, the model files are on the workstation and the workstation performs the computation. NVIDIA describes this as running AI workflows on a PC or workstation, including chat, coding, agents, and document question-and-answer. LM Studio says its downloaded models, document chat, and local inference server can process content on the device or local network. Ollama says locally processed prompts and responses are not collected, stored, transmitted, or accessed by Ollama, according to its privacy policy. Those are vendor descriptions of their own products, not an independent security audit of every component on a computer.
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The important distinction is where each part of a workflow runs. A local model does not make a remote search service, cloud model, plugin, or other connected tool local. A browser-based interface can still send requests to a local model endpoint, but the browser interface alone does not establish where inference happens.
Check the actual network paths
Before entering sensitive material, identify the selected model and provider, whether web search or cloud features are enabled, and the destination configured for inference. Check whether the application is pointed to a local endpoint or a remote URL. Ollama distinguishes local processing from cloud-hosted models; its policy says cloud requests are processed transiently. LM Studio describes cloud models and web search as optional cloud services, and says model searches, downloads, and software update checks use the network.
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- Local model and local tools: prompts and documents can stay on the workstation or local network, depending on the configured services.
- Cloud model or remote endpoint: the content sent for inference leaves the workstation and is handled by that service.
- Web search or connected integration: requests or selected content may be sent to the service involved, even if the main model runs locally.
- Downloads and updates: these are network activity during setup or maintenance, distinct from sending prompts during local inference.
Local execution by itself does not establish that unrelated operating-system services or other software never transmit data. The cited product documentation describes stated product behavior; it is not a traffic audit of the entire workstation.
Can a local model run offline?
Yes, after setup, if the model files and required local software are already available. LM Studio says local model inference, document chat, and its local inference server work without an internet connection. NVIDIA’s Open WebUI and Ollama setup example requires network access to download the container and models; that setup traffic is separate from using those downloaded components offline.
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If offline use is a requirement, download the software and model files first, then disconnect the workstation or otherwise restrict its network access and confirm the intended workflow still works. Be aware that this also prevents online searches, cloud models, downloads, and update checks.
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Choose a model that fits the workstation
Start with the model, context length, and workload you intend to use, then compare their memory demands with the workstation’s GPU memory or unified memory. NVIDIA’s guide gives these examples for RTX GPU memory: 6–8 GB for Qwen 3.5 4B; 12–16 GB for Qwen 3.5 9B or Gemma 4 12B; and 24 GB or more for Qwen 3.6 27B. It lists Qwen 3.6 35B for DGX Spark. These are NVIDIA’s examples, not universal fit guarantees; the model version, quantization, context length, runtime, and other applications affect actual memory use.
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Parameter count influences capability, memory use, and speed. Quantized weights can reduce VRAM needs, but more aggressive quantization can reduce response quality. A longer context includes more than the latest question: prompt, conversation history, tool output, and retrieved documents all contribute and use memory. Compare expected inference speed as well as memory fit; NVIDIA describes tokens per second as one measure of inference speed.
Storage for model files and runtime is separate from memory needed while generating responses. In NVIDIA’s DGX Spark Open WebUI setup, the guide lists an approximately 7 GB container image and approximately 15 GB for gpt-oss:20b or 25 GB for qwen3.6:latest. Those are download and storage examples for that documented configuration, not general workstation requirements.
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Ways to set up a local workflow
Start with a local chat
NVIDIA suggests LM Studio, Ollama Desktop, or llama.cpp as options for running a downloaded model. Choose a model compatible with the machine, obtain its files, and use the application’s local chat workflow. For document questions, NVIDIA also describes using AnythingLLM with local inference. Confirm the selected model and endpoint before using sensitive documents.
Use a self-hosted browser interface
NVIDIA documents Open WebUI connected to local Ollama inference. The interface runs in a browser, but the relevant privacy question is where the configured model endpoint runs and what receives each request. Follow the endpoint configuration rather than assuming that a browser interface means cloud inference—or that it guarantees local inference.
Scope development tools and endpoints
NVIDIA AI Workbench supports projects using local or remote GPU locations and runs projects in sandboxed containers, with changes visible in the application. Containers can help scope project dependencies, but the documentation does not establish that all network access is blocked. NVIDIA’s Personal AI Router documentation describes a loopback-only HTTP proxy endpoint for the documented configuration; do not assume that property applies to other applications or endpoint settings.
Quick Recap
A practical privacy checklist
- Confirm the model is downloaded and the selected provider is local.
- Check the inference endpoint and make sure it is the intended local address, not a remote URL.
- Review whether cloud models, web search, or connected tools are enabled.
- Determine what each integration receives; a local model does not control data sent by a separate service.
- For offline operation, complete downloads and setup first, then verify the workflow without a network connection.
- Match model size and context length to available memory, and leave enough storage for model files and runtime components.
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