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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsYou can use DeepSeek without sending prompts to a cloud service by downloading model weights and running them with a local inference app on your computer. This is different from using the hosted DeepSeek assistant: prompts sent to DeepSeek’s Services are processed by the service, and its privacy policy cautions users not to provide sensitive personal data.
Local DeepSeek and the hosted assistant are different
“DeepSeek” can mean downloadable model weights or a hosted product. DeepSeek’s download page describes its consumer assistant and DeepSeek Harness desktop downloads; it does not describe the assistant as a local, offline model runner. Separately, DeepSeek says it releases model weights, parameters, and inference tool code for users to download and deploy under the MIT License. Check the terms for the specific model and any third-party quantization you choose.
With a hosted assistant, your input is sent to a service for processing. DeepSeek’s privacy policy identifies Hangzhou DeepSeek Artificial Intelligence Co., Ltd. as the provider and controller of its Services. It says the Services may collect prompts, uploaded files, photos, chat history, device identifiers, IP addresses, diagnostic and performance data, and other information. The policy says the Services are not designed or intended to process sensitive personal data, cautions users not to provide it, and says personal data may be processed and stored in China.
How to use DeepSeek locally
- Choose a model variant. Confirm that its files and license suit your use. DeepSeek’s model disclosure describes public releases of weights, parameters, and inference code under the MIT License; verify the exact model and any third-party quantization you download.
- Install a local runner. LM Studio’s documentation covers downloading models and running DeepSeek R1 locally on macOS, Windows, and Linux. Ollama’s privacy policy distinguishes local model operation from its cloud-hosted models.
- Download the model files from a source you trust. Local inference requires the files to be on your computer. The cited documentation does not establish storage or memory requirements for every model variant.
- Select the local model and check the backend. Before entering sensitive text, confirm that the app is using the downloaded model rather than a hosted model. Review any connected tools or services as well.
- For a strict no-network boundary, restrict connectivity after setup. Model downloads require a connection unless you transfer the files another way. After downloading the runner and model, disconnect the computer or block outbound traffic, then confirm your required workflow still works. This is a practical precaution, not a vendor audit of your particular installation.
What local use does—and does not—keep private
Ollama’s policy states: “We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” That statement is Ollama’s description of content processed locally in Ollama; it is not an independent verification and does not cover every app, plugin, model source, or network connection on your computer. The same policy says Ollama may collect limited device and usage metadata and distinguishes local use from its cloud-hosted models.
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A local model does not automatically make the whole workflow offline. LM Studio can serve local models through endpoints on the computer or network and supports MCP servers. DeepSeek Harness’s surfaced processing statement warns that external models, web tools, MCP services, plugins, and other invoked services can upload data. For a stricter boundary, disable integrations you do not need, avoid exposing endpoints to untrusted networks, and restrict outbound connections after setup. These are precautions based on documented capabilities and data-flow warnings, not tested findings about a specific installation.
Check hardware and model requirements before downloading
Local inference requires a computer with enough resources to store the model files and run the selected model. LM Studio documents support for Apple Silicon Macs, x64 and ARM64 Windows PCs, and x64 Linux PCs. The official material cited here does not give a verified minimum RAM, GPU, VRAM, or storage amount for the models discussed. Requirements vary with the model variant, quantization, context length, and runtime, so check the exact model documentation and test your configuration rather than assuming a particular laptop will work.
Rank #2
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Expect model errors, even when processing locally
Keeping inference on your computer changes where prompts are processed; it does not make answers reliable by itself. DeepSeek’s model disclosure says it cannot guarantee that a model will not hallucinate. Verify important answers independently, especially when they could affect safety, finances, health, or legal decisions.
Quick Recap
Best Value
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