The advice to stop uploading tax returns, medical notes, and unredacted client contracts to a cloud LLM keeps getting louder for a reason. Even providers with clear data policies keep changing them. The safer path in 2026 is running a capable model locally and pointing it at your files without them leaving the machine. We tried eight local-first apps that make that practical, from single-binary chatbots to full retrieval pipelines. Each one keeps prompts and documents on the device.
What to look for in a local file-safe LLM tool
Local inference is not just about model quality. A few properties separate a genuine private workflow from a demo.
- No default telemetry, and any diagnostics are opt-in and documented
- Support for the model files you can actually run on your hardware (GGUF, MLX, or MLC)
- A document ingestion path that keeps everything on disk, not in a hosted vector DB
- Working GPU acceleration when your hardware has one; sane CPU fallback when it does not
- Native clipboard, drag-and-drop, or an integration with your editor
- Reasonable defaults for context window and quantization
Quick comparison
| App | Best for | Free plan | Starting price | Standout feature |
|---|---|---|---|---|
| Ollama | Command-line local models with an API | Yes | Free | One-line install, ollama pull catalog |
| LM Studio | Non-technical users running local models | Yes | Free personal use | Model discovery UI |
| GPT4All | Portable app with local RAG | Yes | Free | LocalDocs feature indexes folders |
| Jan | Cross-platform desktop chat with local models | Yes | Free | Fully open source client |
| AnythingLLM | Local RAG across many file types | Yes | Free self-host | Multi-user workspaces on your box |
| PrivateGPT | Developer toolkit for private RAG | Yes | Free | Reference codebase for building your own |
| LocalAI | Drop-in OpenAI-compatible API on your hardware | Yes | Free | Same API surface as OpenAI’s |
| Continue | Editor extension driven by a local backend | Yes | Free | Code-focused UX without cloud calls |
The apps
1. Ollama — best default for local models
Ollama is the fastest way to get a working local model on Linux, macOS, or Windows. ollama pull llama3 fetches a quantized model, ollama run starts a chat, and a REST API on port 11434 lets any other app in the list talk to it. Most other tools on this page can point at Ollama as their backend.
Where it falls short: No built-in document ingestion. You bring your own frontend for chat with files.
Platforms: Linux, macOS, Windows.
Pricing: Free, MIT license.
Download: ollama.com
Bottom line: The first thing to install. Almost everything else in this article uses it as a backend.
2. LM Studio — best for non-developers
LM Studio is a polished desktop app that browses Hugging Face, downloads compatible models, and runs them behind a familiar chat interface. It also exposes an OpenAI-compatible server on localhost, which lets it stand in for the cloud API in existing scripts.
Where it falls short: Not open source, and the app is free for personal use only. Commercial deployment needs a license.
Platforms: Linux, macOS, Windows.
Pricing: Free for personal use.
Download: lmstudio.ai
Bottom line: The friendliest onboarding for someone who does not want a terminal.
3. GPT4All — best with built-in local documents
GPT4All ships a desktop app that runs quantized models locally and includes LocalDocs, a feature that indexes folders on your machine and lets the model cite them in answers. It is the shortest path from “here is a folder of PDFs” to “chat with them privately.”
Where it falls short: Model catalogue is smaller than Ollama’s. Retrieval quality is decent but not tuned for very large libraries.
Platforms: Linux, macOS, Windows.
Pricing: Free, MIT license.
Download: gpt4all.io
Bottom line: The easiest way to chat with a private folder without wiring up a vector database.
4. Jan — best fully open-source client
Jan looks a lot like LM Studio but is fully open source. It runs models locally, exposes an OpenAI-compatible API, and lets you plug in remote endpoints when you want them. The team publishes builds signed for macOS and Windows.
Where it falls short: Extensions and integrations are fewer than LM Studio’s. Discovery of models leans on manual imports.
Platforms: Linux, macOS, Windows.
Pricing: Free, AGPL-3.0.
Download: jan.ai
Bottom line: Pick this over LM Studio if the license matters to you.
5. AnythingLLM — best for multi-user local RAG
AnythingLLM is a self-hostable app that lets several people query private documents against a shared local model. Workspaces isolate corpora, permissions control who can see what, and the ingestion pipeline handles PDFs, spreadsheets, and code repos.
Where it falls short: More moving pieces than a single-binary app. Ingestion for very large libraries takes tuning.
Platforms: Docker on Linux, native builds for macOS and Windows.
Pricing: Free self-host, MIT license.
Download: anythingllm.com
Bottom line: The right pick for a small team that wants a shared private assistant on-premises.
6. PrivateGPT — best developer toolkit
PrivateGPT is a reference implementation of a fully local retrieval-augmented generation pipeline. It is meant to be forked. The value is not the CLI you install but the well-documented Python code that shows exactly how ingestion, embeddings, and inference fit together offline.
Where it falls short: Not a polished end-user product. Expect to read the code.
Platforms: Python on Linux, macOS, Windows.
Pricing: Free, Apache 2.0.
Download: github.com/zylon-ai/private-gpt
Bottom line: Start here if you are building a bespoke internal tool rather than adopting one.
7. LocalAI — best drop-in for existing OpenAI code
LocalAI exposes the OpenAI Chat, Completions, and Embeddings APIs on top of local models. Any script that currently talks to api.openai.com can point at LocalAI without code changes beyond a base URL. That makes migrating an existing internal tool off the cloud a matter of minutes.
Where it falls short: Feature parity with the real OpenAI API is close but not perfect on newer endpoints. Some libraries assume behaviors that only the cloud provides.
Platforms: Docker, native on Linux, macOS, Windows.
Pricing: Free, MIT license.
Download: localai.io
Bottom line: The lowest-friction way to move an existing OpenAI-based app to a private backend.
8. Continue — best for private coding assistance
Continue is a VS Code and JetBrains extension that drives autocomplete, chat, and refactors from a backend you choose. Point it at Ollama or LocalAI and your code never leaves the machine, but you still get inline suggestions and multi-file edits.
Where it falls short: Local model quality lags Claude and GPT-5 on complex refactors. Speed depends heavily on your GPU.
Platforms: VS Code and JetBrains extensions on Linux, macOS, Windows.
Pricing: Free, Apache 2.0.
Download: continue.dev
Bottom line: The right coding assistant when your codebase is under NDA or has license restrictions.
How to pick the right one
- If you are new to local models: Ollama first, then LM Studio or Jan for a UI
- If the point is chatting with a folder of PDFs: GPT4All or AnythingLLM
- If a team of coworkers needs the same private assistant: AnythingLLM
- If you already have code calling OpenAI: LocalAI
- If you are building something bespoke: PrivateGPT as a reference
- If the sensitive files are source code: Continue with a local backend
FAQ
Are local models good enough to replace GPT-5 or Claude for my work? For summarization, extraction, translation, and coding assistance on well-defined tasks, yes. For open-ended reasoning at the frontier, cloud models still lead.
What hardware do I need to run these? A 16 GB laptop can run 7B and 8B models comfortably. 32 GB unlocks 13B. A discrete GPU with 12 GB or more of VRAM is the difference between usable and fast.
Do any of these leak data even in local mode? LM Studio and Ollama both send basic usage telemetry unless you disable it. Everything else on the list is either opt-in or has no telemetry at all.
How do I make sure a model is not calling home? Run it in a network namespace or a firewall that only allows loopback for the model process. Local models genuinely do not need the internet at inference time.
Can I fine-tune a local model on my own documents? Yes, though most people should start with retrieval instead. Ingesting documents into AnythingLLM or GPT4All gives you 80 percent of the value with no training.
Which of these keep working offline on a plane? All of them, once the model files are downloaded. That is the point.