Every prompt you send to a cloud-based AI service leaves your device. It travels to a remote server, gets processed, and comes back. Along the way, it may be logged, reviewed, or used in ways that depend entirely on the terms of service you agreed to. For most casual use cases, that trade-off is acceptable. For sensitive professional work — legal documents, medical records, confidential business strategy, personal financial data — it is a risk worth eliminating.
Running AI models locally means your prompts never leave your machine. The processing happens on your own hardware, the data stays on your own storage, and the outputs never touch an external server. This guide covers what you need, how to set it up, and how to get reliable performance from locally-run models.
Step 1: Assess Your Hardware
Local AI models are computationally demanding. Before downloading anything, verify that your hardware meets the minimum requirements for a functional setup.
RAM is the primary constraint. You need at least 16 gigabytes to run smaller models at acceptable speed, and 32 gigabytes or more for mid-size models that deliver quality comparable to cloud services. GPU memory is equally important if you want fast inference — a graphics card with 12 gigabytes or more of VRAM, such as an RTX 4070 or higher in the Nvidia 40 or 50 series, will dramatically outperform CPU-only processing.
If your current hardware falls short, you have two options: run smaller, more efficient models that fit your existing setup, or treat this as a longer-term project and plan your next hardware purchase around local AI requirements. Do not try to force an undersized machine — the performance will be frustrating and the experience will put you off local AI unfairly.
Step 2: Install a Local Model Server
Ollama and Jan.ai are the two most accessible options for setting up a local model server. Both install with a straightforward process similar to any other desktop application and create a local environment that can host and run open-source AI models.
Ollama is particularly well-regarded for its simple command-line interface and strong community support. Once installed, downloading and running a model is a single command. Jan.ai offers a more visual interface that may be more comfortable for users who prefer not to work in a terminal.
Both platforms support a range of open-source models, including the Llama family from Meta and Mistral’s models, which are among the strongest performers for general-purpose tasks. Install one platform, get familiar with it, and resist the urge to install both simultaneously — keep your setup simple while you are learning.
Step 3: Choose the Right Model Version
Full-precision AI models are too large for most consumer hardware. A practical solution is quantized models — versions that have been compressed to use less memory with a modest reduction in output quality.
A 4-bit quantized model uses roughly a quarter of the memory of the full-precision version. An 8-bit quantized model sits between the two in both size and quality. For most practical tasks — drafting, summarizing, answering questions about documents — a well-chosen 4-bit quantized model running on good hardware produces outputs that are difficult to distinguish from cloud-based alternatives.
When selecting a model, check its benchmark scores on standard evaluations and read user reports about how it performs on tasks similar to yours. Model size and quantization level are just two variables — architecture and training data quality matter too. A smaller well-trained model often outperforms a larger poorly-trained one.
Step 4: Connect Your Local Documents
A model running in isolation can only work with information you paste directly into the prompt. To make your local AI genuinely useful for professional work, you need to connect it to your actual documents.
This is done through a vector database — software that indexes your documents by converting them into numerical representations the AI can search and reference. ChromaDB is a popular open-source option that installs locally and integrates with most local model setups. You point it at a folder of documents — PDFs, notes, reports, contracts — and it makes that content available to your AI without the files ever leaving your machine.
This setup is sometimes called a RAG system, for Retrieval Augmented Generation. It allows your AI to answer questions about your specific documents, draft content that reflects your actual data, and provide context-aware assistance rather than generic responses.
Step 5: Establish Privacy Protocols
Even with a fully local setup, there are edge cases where data could leak. Some applications that integrate with local models have analytics enabled by default. Some model interfaces attempt to check for updates by pinging remote servers. And if your machine is connected to a network, locally processed data could theoretically be accessed by other devices on that network.
For truly sensitive work, consider creating a desktop shortcut or system script that disables your internet connection when your local AI environment is active. This creates an air-gapped working session — fully isolated from external networks. When you are done, re-enable the connection.
Also review the settings of whatever interface you use to interact with your model. Disable any telemetry, usage reporting, or automatic update checks that involve outbound network calls. The goal is a genuinely closed loop, not just an assumption of privacy.
Step 6: Maintain Your Local Setup
Local AI setups require occasional maintenance that cloud services handle automatically for you. New model versions are released regularly and often represent significant quality improvements. Check for model updates monthly and test new versions against your common tasks before fully switching.
Keep your operating system and GPU drivers updated — model performance is directly affected by driver quality. Back up your vector database periodically so you do not lose document indexing if something goes wrong with your system.
The trade-off for local AI is real: you take on maintenance responsibility that a cloud provider would otherwise handle. For the privacy and control you gain in return, most users who need this level of data security find that trade-off worthwhile.
