Jan: Free, Private AI Desktop App Guide

What Is Jan?

Jan is a free, open-source desktop application for running large language models locally on your machine. Licensed under AGPLv3, it gives you a ChatGPT-like interface that runs entirely offline - no data leaves your computer. Built by the team at Homebrew Computer Company, Jan is designed around a simple idea: AI should be personal and private by default. The app stores all data - models, conversations, settings - in a local folder you own.

Built on llama.cpp - fast inference on consumer hardware with support for GGUF quantized models from HuggingFace. Jan.ai is one of the most downloaded local AI desktop apps around - 5.3 million downloads, 41,000+ GitHub stars, and on version 0.7.9 as of March 2026.

Installation and Setup

Operating System: Windows 10 or higher · CPU: AVX2 support required - Intel Haswell (2013+), AMD Excavator (2015+) Memory: 8GB minimum (16GB recommended) GPU: 6GB VRAM minimum for NVIDIA, AMD, or Intel Arc GPUs · Storage: 10GB free space minimum. The application supports Windows, macOS (both Intel and Apple Silicon), and Linux distributions.

Download Jan.ai directly from jan.ai or the GitHub releases page. The installation follows standard conventions for each platform: a .exe installer for Windows, .dmg for macOS, and AppImage for Linux. No complex configuration required-the default settings work well for most users. The application automatically detects your hardware and optimizes accordingly.

Jan offers multiple installation formats, including .deb and AppImage, but the AppImage is what I'd recommend for most Linux users. It's a single self-contained file that runs on virtually any distro without touching your system packages or requiring root privileges for the app itself. It requires no dependency hell.

Once installed, Jan automatically downloads a default model on first launch. Click the Hub in the left sidebar. This is Jan's built-in model library, a curated list of open-source models you can download with a single click.

Supported Models

Supported popular models include Meta Llama 3 series, Mistral 7B/8x7B, Alibaba Qwen 2/2.5 series, Google Gemma 2, DeepSeek-R1, and more. Models are stored in GGUF format, supporting quantized versions to reduce hardware requirements.

Jan's Model Hub is designed for easy installation of pre-configured models but it also allows you to install virtually any model from Hugging Face or even your own. Cloud Integration: Connect to cloud providers such as OpenAI, Anthropic, Gemini, and Groq when needed.

Key supported frameworks and formats:

• GGUF for llama.cpp, while MLX-Swift models are supported through Jan's MLX engine on Apple Silicon.
• On NVIDIA, Jan installs the llama.cpp CUDA backend. On Apple Silicon, v0.7.7 added native MLX support - a meaningful upgrade that replaced the slower llama.cpp Metal path for Mac users.
• AMD and Intel Arc GPUs work via Vulkan, though that path sees less testing.

Pros

• Complete Privacy & Offline Capability: Jan works completely offline once you've downloaded your models. All inference runs locally on your hardware with no internet connection required for chat or API server functionality.

• Zero Cost: There are no pricing tiers, no subscriptions, no usage limits, and no account required. The software runs entirely on your local hardware with zero ongoing costs.

• Open Source & Auditable: Jan combines a polished GUI with full open-source transparency and a plugin architecture.

• Unified Local + Cloud Interface: Jan's single biggest advantage in a professional-services context is the combination of a polished desktop UI and a clean OpenAI-compatible endpoint in the same product. You get the friendly chat window for the receptionist and the API for the developer, from one install.

• Automatic Hardware Optimization: GPU acceleration is automatic. Jan v0.7.9 added automatic context-length capping to avoid OOM crashes - a long-standing frustration that's now handled without manual tuning.

• Extensibility: Jan AI has a built-in extension system where community developers can create various plugins to expand functionality. Common extensions include Speech-to-Text, web search integration, RAG document querying, and code syntax highlighting.

• Model Context Protocol Support: Jan AI supports the Model Context Protocol (MCP) introduced by Anthropic, which can connect to external tools and services including Gmail, Google Drive, Notion, Slack, Figma, and more. Through MCP, AI assistants can directly read your documents, calendars, and emails, significantly boosting actual work efficiency.

Cons

• Hardware Dependent: A laptop with 32 GB of RAM can run a 13B model comfortably and a 30B model slowly. A 70B model at reasonable quant needs a serious GPU and is painful on CPU. A 400B mixture-of-experts model is simply not going to happen on a MacBook. If your use case requires larger models (complex reasoning, long-context document work, code generation at Claude or GPT-4 quality levels), you need a GPU server.

• Performance Slower Than Alternatives: Jan AI provided slow inference time as compared to Ollama on the same hardware. Ollama produced a response at a rate of 6.62 tokens/sec while Jan AI produced a response at around 5 tokens/sec. On the second test, Ollama reported 7.35 tokens per second while Jan AI responded at 3 tokens per second.

• Not Ideal for Multi-User Teams: Jan's biggest weakness is that multi-user deployments are awkward. It is a desktop app. If you want ten staff to use the same model, you either install Jan on each machine (which fragments governance and doubles storage) or you host the model on a central server running Ollama behind Open WebUI and tell Jan to point at that.

• Model Quality Varies: The quality of AI responses can vary based on the selected model, as open-source LLMs continue to evolve.

• Limited RAG Maturity: The trade is ecosystem maturity: tool support and integrations trail the mainstream. Function-calling and tool ecosystems are less mature than the mainstream assistants, and integrations are thinner: it optimises for privacy, not breadth.

• Requires Technical Setup: Jan requires commitment - both in setup time and ongoing resource management.

Best Use Cases

Jan AI appeals to various user groups, including: Writers seeking offline drafting and brainstorming tools. Developers looking to experiment with prompts or create local AI applications. Researchers handling sensitive information who prioritize data security. Individuals with unstable internet connections who still want reliable AI access. Users who prefer free software over subscription-based models.

Jan is the right choice when your primary use case is interactive AI chat and general assistance - writing help, coding questions, research, brainstorming - without a document Q&A requirement.

For example, you can create a "Code Review Assistant" using Qwen2.5-Coder, a "Translation Assistant" using Gemma2, and a "Writing Assistant" using Llama3.

Knowledge Work, Creative Industries, AcademiaFor: Designers, Researchers, Writers, Project Managers, Independent Professionals.

FAQ

Can Jan work completely offline without internet?

Yes, Jan works completely offline once you've downloaded your models. All inference runs locally on your hardware with no internet connection required for chat or API server functionality.

What are the minimum hardware requirements to run Jan?

The minimum requirements include 8GB of RAM and a CPU with AVX2 support (most processors from 2013 onward). For optimal performance, 16GB of RAM and a dedicated GPU will dramatically improve response times.

Is Jan completely free with no hidden costs?

Yes, Jan is completely free and open-source under the AGPLv3 license. There are no subscription fees, usage limits, or hidden costs for running models locally.

Can I use Jan with cloud AI services like OpenAI or Claude?

Cloud Integration: Connect to cloud providers such as OpenAI, Anthropic, Gemini, and Groq when needed. Easily switch between local and cloud models.