Jan vs LM Studio

Jan - Open-source ChatGPT replacement

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Getting Started - Community - Changelog - Bug reports

Jan is bringing the best of open-source AI in an easy-to-use product. Download and run LLMs with full control and privacy.

Installation

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The easiest way to get started is by downloading one of the following versions for your respective operating system:

Download from jan.ai or GitHub Releases.

Features

• Local AI Models: Download and run LLMs (Llama, Gemma, Qwen, GPT-oss etc.) from HuggingFace
• Cloud Integration: Connect to GPT models via OpenAI, Claude models via Anthropic, Mistral, Groq, MiniMax, and others
• Custom Assistants: Create specialized AI assistants for your tasks
• OpenAI-Compatible API: Local server at localhost:1337 for other applications
• Model Context Protocol: MCP integration for agentic capabilities
• Privacy First: Everything runs locally when you want it to

Build from Source

For those who enjoy the scenic route:

Prerequisites

• Node.js ≥ 20.0.0
• Yarn ≥ 4.5.3
• Make ≥ 3.81
• Rust (for Tauri)
• (macOS Apple Silicon only) MetalToolchain xcodebuild -downloadComponent MetalToolchain

Run with Make

git clone https://github.com/janhq/jan
cd jan
make dev

This handles everything: installs dependencies, builds core components, and launches the app.

Available make targets:

• make dev - Full development setup and launch
• make build - Production build
• make test - Run tests and linting
• make clean - Delete everything and start fresh

Manual Commands

yarn install
yarn build
yarn dev

Building on Windows

Run make dev from Git Bash (installed with Git for Windows) — make dispatches its recipes through sh, so a plain cmd.exe won't work.

You do not need a "Native Tools Command Prompt for VS 2022". The bundled llama.cpp engine builds with Ninja + clang-cl, and clang-cl locates the MSVC toolchain and Windows SDK on its own. What has to be installed (and on PATH for ninja/clang-cl/cmake):

• Visual Studio 2022 Build Tools (MSVC x64 workload + Windows SDK)
• LLVM (provides clang-cl)
• Ninja
• CMake
• CUDA Toolkit — only for JAN_ENGINE_VARIANT=cuda12/cuda13 builds

Engine variants are picked with JAN_ENGINE_VARIANT (tokens: cpu, vulkan, metal, cuda12, cuda13, hip/rocm, joined by -), e.g.:

make dev JAN_ENGINE_VARIANT=cuda13

"nvcc fatal : Could not open output file ...fattn-...cu.obj.d" during tauri-plugin-llamacpp(build) means the build path crossed Windows' 260-character MAX_PATH limit — nvcc does not honor the long-path opt-in. The build script now detects this and automatically relocates the llama.cpp build tree to a short directory under %LOCALAPPDATA%\jan-engine. If you hit path-length errors anyway, set JAN_ENGINE_BUILD_DIR to a short path (e.g. C:\jb) or move the checkout closer to the drive root.

System Requirements

Minimum specs for a decent experience:

• macOS: 13.6+ (8GB RAM for 3B models, 16GB for 7B, 32GB for 13B)
• Windows: 10+ with GPU support for NVIDIA/AMD/Intel Arc
• Linux: Most distributions work, GPU acceleration available

For detailed compatibility, check our installation guides.

Troubleshooting

If things go sideways:

1. Check our troubleshooting docs
2. Copy your error logs and system specs
3. Ask for help in our Discord #🆘|jan-help channel

Contributing

Contributions welcome. See CONTRIBUTING.md for the full spiel.

Note: Please sign your commits so we can verify your contributions.

Links

• Documentation - The manual you should read
• API Reference - For the technically inclined
• Changelog - What we broke and fixed
• Discord - Where the community lives

Contact

• Bugs: GitHub Issues
• Business: hello@jan.ai
• Jobs: hr@jan.ai
• General Discussion: Discord

License

Apache 2.0 - Because sharing is caring.

Acknowledgements

Built on the shoulders of giants:

• Llama.cpp
• Tauri
• Scalar

Overview

LM Studio is a desktop app for running local LLMs on your own machine. It downloads models, manages them, and serves them through a local runtime built on MLX (Apple Silicon) and llama.cpp (CPU/GPU). The current release centers on Bionic, LM Studio's agent for open models — built for work, code and document editing, natively local.

Because inference runs on your hardware, prompts, files and voice audio stay on the device unless you explicitly use a cloud feature.

Core capabilities

• Local model library — browse and download open models (including frontier open-weight models such as GLM, Kimi and DeepSeek families) from inside the app.
• Bionic agent — create and edit documents with automatic saving, run coding tasks, build automations and control the computer through an agent loop.
• Real-time voice transcription — talk to Bionic and have speech transcribed locally, with multiple language support; audio never leaves the device.
• OpenAI-compatible local server — expose loaded models on a local endpoint so other tools and SDKs can use them.
• MLX and llama.cpp backends — optimized inference on Apple Silicon and broad GPU/CPU support elsewhere.
• Model and context controls — choose quantization, context length and GPU offload per model.
• Privacy-first design — local processing by default; LM Studio states its cloud services are zero-data-retention.

What it is good at

LM Studio is useful for private coding assistance, offline work, experimenting with open models, prototyping agents without API bills, and running a local OpenAI-compatible endpoint for other applications. It is not a hosted service — performance depends on your hardware and the model size you can fit.

Getting started

1. Download LM Studio for macOS, Windows or Linux.
2. Search the model catalog and download a model that fits your RAM/VRAM.
3. Load the model and chat, or open Bionic for agentic work.
4. Start the local server if another app needs an API endpoint.
5. Adjust context length and GPU offload if you hit memory limits.

Pricing

LM Studio is free to download and use locally. Account-based and cloud features may have their own terms; check lmstudio.ai for current details.

Links

• Website: lmstudio.ai
• Docs: lmstudio.ai/docs
• Bionic docs: lmstudio.ai/docs/bionic