0f0ba43b7f
* feat: Adjust RAM/VRAM calculation for unified memory systems This commit refactors the logic for calculating **total RAM** and **total VRAM** in `is_model_supported` and `plan_model_load` commands, specifically targeting systems with **unified memory** (like modern macOS devices where the GPU list may be empty). The changes are as follows: * **Total RAM Calculation:** If no GPUs are detected (`sys_info.gpus.is_empty()` is true), **total RAM** is now set to $0$. This avoids confusing total system memory with dedicated GPU memory when planning model placement. * **Total VRAM Calculation:** If no GPUs are detected, **total VRAM** is still calculated as the system's **total memory (RAM)**, as this shared memory acts as VRAM on unified memory architectures. This adjustment improves the accuracy of memory availability checks and model planning on unified memory systems. * fix: total usable memory in case there is no system vram reported * chore: temporarily change to self-hosted runner mac * ci: revert back to github hosted runner macos --------- Co-authored-by: Louis <louis@jan.ai> Co-authored-by: Minh141120 <minh.itptit@gmail.com>
Jan - Local AI Assistant
Getting Started - Docs - Changelog - Bug reports - Discord
Jan is an AI assistant that can run 100% offline on your device. Download and run LLMs with full control and privacy.
Installation
The easiest way to get started is by downloading one of the following versions for your respective operating system:
| Platform | Stable | Nightly |
| Windows | jan.exe | jan.exe |
| macOS | jan.dmg | jan.dmg |
| Linux (deb) | jan.deb | jan.deb |
| Linux (AppImage) | jan.AppImage | jan.AppImage |
Download from jan.ai or GitHub Releases.
Features
- Local AI Models: Download and run LLMs (Llama, Gemma, Qwen, etc.) from HuggingFace
- Cloud Integration: Connect to OpenAI, Anthropic, Mistral, Groq, and others
- Custom Assistants: Create specialized AI assistants for your tasks
- OpenAI-Compatible API: Local server at
localhost:1337for other applications - Model Context Protocol: MCP integration for enhanced 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 ≥ 1.22.0
- Make ≥ 3.81
- Rust (for Tauri)
Run with Make
git clone https://github.com/menloresearch/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 launchmake build- Production buildmake test- Run tests and lintingmake clean- Delete everything and start fresh
Manual Commands
yarn install
yarn build:tauri:plugin:api
yarn build:core
yarn build:extensions
yarn dev
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:
- Check our troubleshooting docs
- Copy your error logs and system specs
- Ask for help in our Discord
#🆘|jan-helpchannel
Contributing
Contributions welcome. See CONTRIBUTING.md for the full spiel.
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: