Can I Run Generative AI Locally to Keep My Files Private?

Can I use an AI chatbot without sending my files to a cloud service?

Yes, if the chatbot runs a model locally and processes your files on your own device. LM Studio says local chats and document retrieval run on the machine; once the model is downloaded, the app can work offline. Its documentation puts the boundary plainly: “Once you get an LLM onto your machine, you should be good to go entirely offline.”

There is a setup phase. Finding models and downloading them require an internet connection, with requests to services such as Hugging Face. After that, LM Studio says prompts entered in local chat and documents used for retrieval-augmented generation stay on the computer. That distinction matters: downloading a model uses the network; asking that local model a question does not, according to the documentation.

Check which mode is active before using sensitive files. Ollama’s privacy policy says locally processed prompts, responses, and interactions are not collected, stored, transmitted, or accessible to Ollama. It also says limited device and usage metadata, such as app version and request counts, may be collected. Cloud-hosted models are a separate mode, where the service processes prompts and responses to fulfill requests.

For business documents, local inference lowers the risk of sending their contents to a model provider. It does not make every part of the computer private by itself. Keep the model local, confirm that the app is not routing requests to a hosted service, and account for the operating system, backups, and any other software that can access the files.

What computer do I need to run an AI model locally?

You need enough memory for the model and its context, with GPU memory often setting the practical ceiling for a fast desktop setup. A laptop can run local models too, but the model choices depend on its available GPU memory and system memory. Model size alone does not determine the requirement.

NVIDIA’s developer playbook gives a useful starting range for RTX graphics cards: 4B models for 6–8 GB of GPU memory, 9B or 12B models for 12–16 GB, and 27B models for 24 GB or more. These are starting points, not guarantees. Parameter count, quantization, and context window all affect memory use, and longer contexts need more room.

NVIDIA explains that quantizing a model to 8-bit or 4-bit can reduce memory needs, allowing larger models to fit on GPUs with less memory. Lower-bit quantization can also reduce response quality when it is aggressive. The best choice is the largest model that fits comfortably, rather than one that leaves the computer struggling for memory.

These ranges come from NVIDIA’s RTX-focused guidance; they do not establish a universal minimum for every laptop or desktop. A system can split model use across GPU and system memory, though that does not mean performance will be the same as fitting everything in GPU memory. Ollama’s ollama ps command shows whether a model uses GPU memory, system memory, or both. Its FAQ example shows a 70B model occupying 42 GB, a reminder that large models can exceed ordinary computer memory.

Before buying hardware, check the model’s intended size and the current support page for your graphics card. Ollama lists NVIDIA support by compute capability and driver version, and lists supported AMD families with a ROCm requirement for Linux. Compatibility depends on both the specific card and its drivers, so a GPU name alone is not enough to confirm support.

Are local AI models actually private?

They keep prompts and documents from the model provider when the model processes them locally, but privacy depends on which model mode and app behavior you use. “Local” describes where inference happens; it does not guarantee that every related network request or device activity stays offline.

LM Studio distinguishes between downloading a model and using it: discovery and downloads need the internet, while local chat and document processing can occur offline. Ollama likewise says local prompts and responses are not transmitted to it, while noting that it may collect limited usage metadata. Its cloud-hosted models follow a different path because the service processes the prompt and response.

That gives freelancers and small-business owners a practical check: identify where the model runs, then identify what the app sends elsewhere. A local model with a cloud feature enabled is not the same privacy arrangement as an offline local session. When the documents are sensitive, test the actual workflow with the network disconnected and read the application’s current privacy and mode descriptions.

Setup begins with a desktop app such as Ollama or LM Studio, then a model small enough for the computer’s memory. NVIDIA’s beginner workflow follows those same steps: install the app, choose and download a fitting model, then chat. Start with a modest model and a short context. Increase model size or context only when the current setup cannot handle the task.

Expect tradeoffs. A local model’s speed and answer quality depend on hardware, memory allocation, model size, and quantization. Ollama’s default context window is 4,096 tokens; longer contexts and parallel requests need additional memory. Ollama says macOS and Windows apps download updates automatically, while Linux updates can be installed manually. Local control removes a cloud dependency, but it also puts model selection and maintenance in your hands.

For routine drafting, summarizing, or searching documents, a smaller local model may be enough. For harder reasoning or longer documents, a larger model and more memory may help, but the sources do not provide a universal performance threshold for every task or computer. The sensible decision is not “local at any cost.” It is whether the privacy boundary is worth the hardware limits and upkeep for the work at hand.