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You can run a ChatGPT-style assistant entirely on your computer by installing a local model runner, downloading a model that fits your memory, and chatting through a desktop or browser interface. The practical choices are LM Studio for an integrated desktop app, or Ollama for a local service and API. Add Open WebUI if you want a browser-based interface. This can keep model inference, chats, and document work on your device—but only when every configured model, embedding service, extraction process, speech component, search provider, and tool is local.
What a local AI assistant actually is
A local assistant is a language model whose inference runs on your own computer instead of on a hosted provider’s servers. A typical setup has several separate parts:
- Model files: the downloaded weights and related parameters that the computer loads into RAM, unified memory, or GPU memory.
- Model runner: software that loads the model and performs inference. Ollama and LM Studio are the two practical starting points described here.
- Chat interface: the runner’s own interface, or an optional browser interface such as Open WebUI.
- Optional local services: embedding models, document extraction, reranking, speech recognition, text-to-speech, or other tools.
“Local” describes the endpoint handling a particular operation. It does not automatically mean that every operation in the surrounding application is local. A locally running model can still be paired with a cloud search tool, hosted embedding API, remote document extractor, or online speech service.
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Choose the right setup
| Setup | Best for | Interface | Service and integration model | Privacy and offline work |
|---|---|---|---|---|
| LM Studio alone | A straightforward desktop workflow | Integrated desktop app | Download, load, and chat in one application; it can also provide a local server for compatible clients. | Downloaded-model chat and document processing stay on the device; catalog searches and new downloads need internet access. |
| Ollama alone | A local service, API, scripts, or integrations | Command-line and client interfaces | Runs a local model service. Its documented API is available at http://localhost:11434. |
Keep model files and clients local; inspect each client or tool you connect to the service. |
| Ollama plus Open WebUI | A browser-based, ChatGPT-style experience | Web browser | Open WebUI can connect to Ollama, llama.cpp, vLLM, LM Studio, and other OpenAI-compatible servers. | Requires deliberate provider selection and configuration; a local interface does not make separately configured services local. |
Choose LM Studio when installation simplicity and a desktop workflow matter most. Choose Ollama when you want a background service, a local API, or broad integration options. Add Open WebUI only when its browser interface, multi-user features, or provider flexibility justify another component to maintain.
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Check hardware and storage before downloading anything
There is no universal minimum specification. The model’s parameter count, quantization, context length, operating system, GPU arrangement, number of simultaneous users, and any document or speech workload all change the requirement.
Memory is the first constraint
Loading a model means allocating memory for its weights and other parameters. As LM Studio puts it, “Loading a model typically means allocating memory to be able to accommodate the model’s weights and other parameters in your computer’s RAM.” Quantized files reduce the footprint, but they do not eliminate it. Leave headroom for the operating system, the runner, the interface, the conversation context, and any other applications running at the same time.
- Smaller quantized models can run on a CPU or modest integrated hardware, although response speed and context capacity vary.
- Larger models generally benefit from more system RAM, unified memory, or dedicated VRAM.
- A longer context window consumes additional memory even when the model weights themselves fit.
- Document retrieval, reranking, image handling, and speech models add their own memory requirements.
Storage is more than the model download
Reserve space for model files, application caches, chat history, document indexes, embeddings, extraction models, and optional speech assets. Keep these directories on a drive with enough free space for updates and temporary files. An upgradeable SSD is useful because model collections and indexes can grow quickly, but capacity is not a guarantee of speed or quality.
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GPU support and drivers
Acceleration depends on the runner, operating system, drivers, and model format. Ollama’s Windows documentation describes support for NVIDIA and AMD Radeon GPUs. If you use a GPU-enabled container or another backend, install the matching host drivers and verify that the container can see the device. Do not assume that a graphics card will accelerate every model or every operation.
The safest order is: select a model family and quantized file first, check its stated memory footprint, then compare that requirement with your available RAM or VRAM while leaving operating-system headroom.
Install a local runner
Option A: LM Studio
- Download the current LM Studio installer for your operating system from the official LM Studio site.
- Install and open the application. Its workflow is designed around discovering a model, downloading it, loading it into memory, and chatting.
- Open Discover, choose a model whose license and quantized file suit your use, and download it while you are online.
- Load the model. If loading fails, close other memory-heavy applications, select a smaller or more heavily quantized file, reduce the context setting, or add RAM/VRAM.
- Send a short test prompt before configuring documents, tools, or long system instructions. A response confirms that the basic model path works.
LM Studio’s downloaded-model chat and document processing are designed to remain on the device. However, searching its model catalog and downloading new models require an internet connection, so complete those tasks before disconnecting.
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Option B: Ollama
- Download and install the current Ollama release for your operating system.
- On Windows, use Windows 10 version 22H2 or newer and install the NVIDIA or AMD driver path documented for your hardware.
- Use Ollama’s model workflow to download a model that fits your available memory and whose license permits your intended use.
- Start the model and submit a short prompt. Confirm that it responds before adding a web interface or retrieval pipeline.
- For local applications, point the client at Ollama’s local API endpoint:
http://localhost:11434. Keep the endpoint bound to the local machine unless you intentionally understand and secure network access.
Ollama is a service rather than a complete browser experience. You can use a compatible client, build a local application, or add Open WebUI.
Add a browser-based interface with Open WebUI
Open WebUI provides a browser interface and can connect to Ollama, LM Studio, llama.cpp, vLLM, and other OpenAI-compatible servers. It is optional: adding it does not improve the underlying model, but it can make conversations, model selection, and document workflows easier to manage.
Basic bundled Ollama deployment
- Install the current container runtime supported by your operating system.
- Deploy the documented bundled Ollama image. The standard mapping exposes the interface on host port
3000and the container’s web service on port8080, written as3000:8080. - Store Ollama data and Open WebUI data in persistent volumes. Without persistence, model downloads, settings, chats, and indexes can disappear when the container is recreated.
- Set a persistent
WEBUI_SECRET_KEY. A stable secret prevents sessions or encrypted data from becoming unusable after a restart. - Pin a release tag for a production or long-lived installation instead of tracking an unpinned moving image. Test upgrades separately before changing a dependable setup.
- Open the local web address shown by your deployment, select the local Ollama endpoint, and run a short prompt.
Provider selection is the privacy control that matters most. Open WebUI’s own warning is precise: “Running the interface locally does not make those services local.” A browser page served from your computer can still send prompts to a hosted model, cloud tool, or remote processing service if you select one.
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Configure privacy so files do not leave the computer
Use a component-by-component audit rather than relying on the word “local.” For every feature, identify the actual endpoint that receives data.
| Component | Question to answer | Private configuration |
|---|---|---|
| Chat inference | Which model server receives the prompt? | Use the local LM Studio or Ollama endpoint and a model stored on the computer. |
| Embeddings | Where are document chunks converted into vectors? | Download and select a local embedding model. |
| Extraction | Where are PDFs, scans, images, or office files parsed? | Use a local extraction service and verify that it does not call a hosted API. |
| Reranking | Where are retrieved passages scored? | Install a local reranker or leave reranking disabled. |
| Speech | Where are audio and generated speech processed? | Download local speech assets and select local processing. |
| Search and tools | Can a connector send prompts, documents, or results outside the device? | Disable hosted search and cloud tools for sensitive work, or isolate them from private conversations. |
For a hard boundary, use firewall rules or network isolation after downloading everything required. Network blocking is a backstop, not proof that a feature is local: a component that silently depends on the internet will simply fail when disconnected.
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Prepare a genuinely offline installation
Offline operation means more than downloading one language model. While connected, obtain and test every package and asset you will need:
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- The runner or container image and its matching dependencies.
- At least one model file and any tokenizer or runtime assets it requires.
- Open WebUI, if used, with a pinned release and persistent storage.
- Local embedding, extraction, reranking, and speech models for optional features.
- Driver packages and GPU runtime components appropriate to the computer.
- Download the application packages, container images, model weights, and optional assets while connected.
- Place caches, model directories, chat data, and indexes on persistent storage.
- Restart the runner and interface to ensure they can recover without re-downloading anything.
- Disconnect the network or apply the intended firewall rules.
- Test ordinary chat, a document question, and each enabled tool separately. A successful chat test does not prove that document extraction or embeddings are local.
LM Studio describes the practical boundary this way: once an LLM is on your machine, the model can run locally and you should be able to work offline. Catalog browsing and new model downloads remain online activities, so plan them in advance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Add private documents with local retrieval
Retrieval-augmented generation (RAG) lets the assistant search an index of your files and include relevant passages in a prompt. It introduces more privacy-sensitive components than ordinary chat.
Prepare the pipeline
- Choose the files and remove documents that should never be indexed.
- Use a local extraction service for text, tables, and scanned pages. Check whether image or PDF handling invokes a remote API.
- Choose and download a local embedding model. Embeddings are representations of your text; they are not automatically private just because the chat model is local.
- Store the vector index and source files on a protected local drive.
- Add a local reranker only if retrieval quality requires it, and verify its endpoint as you did for embeddings.
- Ask a question whose answer is known to be in one test document. Confirm that the returned passages and citations come from that file.
Do not treat a normal conversation as proof that RAG is configured privately. A local chat model can answer general questions while a separately configured document processor sends files elsewhere.
Choose models responsibly
Model names, releases, quantized files, supported formats, and licenses change. Before downloading, check the model’s license, intended use, context capability, quantization, and stated memory requirement. A smaller model that fits comfortably may be more useful than a larger one that constantly swaps memory or fails to load.
- Start with one model rather than filling the drive with untested variants.
- Use a short, representative prompt to check instruction following, language coverage, and response time.
- Test the context length you actually need; long pasted documents can exhaust memory even when a short chat works.
- Keep a record of the model name, quantized file, runner version, and any system prompt so that behavior can be reproduced after updates.
Troubleshoot the common failure modes
The model will not load
- Close other applications and retry with a smaller quantized file.
- Lower the context length and avoid loading multiple models simultaneously.
- Check whether the runner is attempting to use a GPU with insufficient VRAM; switch to CPU or a smaller model.
- Confirm that the model format is supported by the runner version.
Responses are extremely slow
- Verify that the intended GPU and driver are actually being used.
- Reduce context length and stop background workloads competing for memory.
- Use a smaller model or quantization rather than assuming more threads will solve memory pressure.
Open WebUI cannot reach Ollama
- Confirm that Ollama is running and that the configured endpoint is correct.
- Check container networking and host-to-container access; a container’s
localhostis not always the host computer’slocalhost. - Verify that the Ollama data volume and Open WebUI settings survived a restart.
Document answers are empty or inaccurate
- Test extraction on a simple text document before adding scanned PDFs or images.
- Confirm that the embedding model completed indexing and that the query is searching the intended collection.
- Inspect retrieved passages. If the right text is absent, the problem is extraction, chunking, indexing, or retrieval—not necessarily the chat model.
The assistant works online but not offline
List every enabled provider and asset, then reconnect only the missing local component. Typical omissions are container images, embedding or extraction models, speech assets, GPU runtime packages, and nonpersistent caches. Disable cloud tools explicitly and repeat the disconnected test.
Maintain and secure the local assistant
- Keep the operating system, GPU drivers, runner, container runtime, and interface patched, but test updates before applying them to a dependable installation.
- Pin Open WebUI and other container releases when reproducibility matters.
- Back up model manifests, configuration, system prompts, chat databases, vector indexes, and the persistent
WEBUI_SECRET_KEYseparately from large model files. - Protect local storage with the operating system’s account controls and disk encryption where appropriate.
- Do not expose the Ollama API or Open WebUI port to the wider network unless you have deliberately configured authentication, firewall rules, and access controls.
- Review provider settings after every upgrade; defaults and available integrations can change.
Which architecture should you use?
| Your priority | Recommended path | Reason |
|---|---|---|
| Fastest route to private desktop chat | LM Studio | The discover, download, load, and chat workflow is integrated. |
| Local API and automation | Ollama | It provides a local service endpoint at http://localhost:11434 for compatible clients. |
| Browser access on your own machine | Ollama plus Open WebUI, or LM Studio plus Open WebUI | Open WebUI supplies the browser layer and can connect to several local backends. |
| Strict offline document work | Any runner plus verified local extraction and embedding services | The privacy boundary includes RAG components, not just the chat model. |
| Minimal maintenance | One runner and one tested model | Every additional provider, container, tool, and model adds another update and privacy surface. |
The most dependable starting point is a single local model and a short prompt test. Add a browser interface, retrieval, speech, or tools only after the basic local path works and you have verified where each new component sends data.
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