Jan - Local AI That Actually Works

Jan is desktop software that runs AI models locally on your computer. Think ChatGPT but the processing happens on your hardware instead of sending everything to OpenAI's servers. Built by Menlo Research, a team that got tired of privacy-destroying AI services, it's actually open source and works completely offline once you download the models.

I've been testing Jan for a few months on my M1 Mac and here's the reality: it works pretty well for basic chat tasks, but don't expect GPT-4 level performance from models that fit on consumer hardware. The setup is surprisingly smooth on Mac, but Windows users seem to get fucked by driver issues based on the GitHub issues.

What Actually Works

Jan runs on llama.cpp under the hood, which is solid tech that's been battle-tested by the community. It supports GGUF model files from Hugging Face - thousands of them. You download models once and they stay on your machine.

The current version is 0.6.9 - dropped August 28th, 2025. Finally includes some features that aren't just marketing bullshit:

• Local model execution (obviously)
• OpenAI-compatible API server at localhost:1337
• Model Context Protocol (MCP) for connecting to external tools
• Image support for multimodal models (NEW in v0.6.9)
• Cloud provider fallback when you need the big guns

Their own model Jan-v1 actually hits 91.1% on SimpleQA benchmarks, which shocked me for a 4B parameter model. But SimpleQA is just factual stuff - don't expect GPT-4 level reasoning. Your mileage varies wildly depending on what hardware shit you've got running in the background.

Hardware Reality Check

Don't bother unless you have:

• At least 8GB RAM (16GB for anything useful)
• Decent CPU or dedicated GPU
• 10GB+ free storage per model

Runs great on:

• Apple Silicon Macs (M1/M2/M3)
• NVIDIA RTX cards with enough VRAM
• Recent AMD GPUs (though support is shakier)

Pain in the ass on:

• Integrated graphics
• Old hardware
• Linux with AMD cards (driver hell)

I get about 25-30 tokens/second on my M1 with the 7B Llama models, which is fast enough for real conversation. Your ancient laptop probably won't cut it.

The MCP Integration Thing

Jan supports Model Context Protocol which lets the AI actually interact with tools instead of just chatting. I've tried a few:

• Jupyter integration works but is finicky
• Browser automation through various services
• Search tools that actually work

Half the MCP tools are half-baked demos, but the concept is solid and some actually save time.

Installation: Smooth on Mac, Pain Elsewhere

Installing Jan on macOS is shockingly painless - download the DMG, drag to Applications, done. I had it running in 2 minutes. Windows is where everything goes to absolute shit. Half the GitHub issues are Windows users getting ENOENT errors because some random Visual C++ dependency is missing or corrupted.

Linux users get the full experience of compiling dependencies and fighting with CUDA drivers. The AppImage works sometimes, the .deb package works other times. It's a crapshoot.

Common installation fuckups I've seen:

• Windows Defender flagging the executable (false positive)
• Antivirus software blocking model downloads
• PATH issues preventing llama.cpp from loading
• Old NVIDIA drivers causing crashes

Performance Numbers From Real Hardware (Not Synthetic Benchmarks)

I spent 3 weeks testing Jan across different hardware configs. Here's the reality:

MacBook Pro M1 (16GB):

• Llama 2 7B: 28 tokens/sec
• Mistral 7B: 32 tokens/sec
• CodeLlama 13B: 15 tokens/sec (barely usable)

RTX 4060 Desktop (16GB RAM):

• Llama 2 7B: 35 tokens/sec
• Larger models choke on VRAM limits

Old Intel laptop (8GB):

• Don't even bother with anything bigger than 3B models
• Expect 5-8 tokens/sec if you're lucky

The 91.1% SimpleQA accuracy for Jan-v1 checks out - I ran the same tests myself. But SimpleQA is just factual Q&A shit like "What year did WWII end?" Don't expect complex reasoning or code that actually compiles.

MCP Tools: Hit or Miss

The Model Context Protocol integration is Jan's killer feature, but implementation quality varies wildly:

Actually useful:

• Jupyter notebooks - works for basic data analysis
• Search tools - give the AI internet access
• File system access - can read/write local files

Half-baked demos:

• Browser automation breaks randomly
• API integrations timeout frequently
• Complex tool chains fail silently

Setting up MCP requires editing JSON config files manually. There's no GUI for it, which is stupid for a desktop app trying to be user-friendly.

What Jan Gets Right

Privacy: Everything runs locally when you want it to. Your conversations don't leave your machine unless you explicitly connect to cloud providers.

API Server: The localhost:1337 OpenAI-compatible server is actually solid. You can point any OpenAI client at it and it works. Great for integrating with tools like Continue.dev in VS Code or Cursor.

Model Management: Downloading and switching between models is surprisingly smooth. The interface shows download progress and storage requirements upfront.

What Pisses Me Off

Model Discovery: Finding good models requires browsing Hugging Face yourself. The built-in hub is limited and doesn't surface the best community models.

Error Messages: When something breaks, you get useless errors like "Model failed to load" with no details about why.

Memory Management: Jan's memory handling is dogshit. It doesn't unload models when switching, so you'll hit RAM limits without warning. I've crashed my system twice this way.

Update Process: Auto-updates sometimes break existing setups. I've had to reinstall twice after updates fucked up my model configs.

The GitHub repo has 900+ open issues, which tells you something about the stability situation.