QWE AI Academy Most people don’t need a private LLM desktop. If you’re happy pasting into ChatGPT, keep doing that – it’s faster, smarter, and free. The interesting question is what to do when “private” stops being a preference and starts being a requirement: NDAs, client data, HIPAA machines, or code you legally can’t feed to a third party. That’s where Jan earns its keep.
This is a deployment log for Jan v0.8.3, published 2026-06-24. I’ll skip the marketing story and focus on the parts that actually break during setup.
What Jan is (in one paragraph)
Jan is an open-source desktop app that runs LLMs entirely on your hardware, with an OpenAI-compatible API on localhost so any tool that speaks OpenAI’s format can talk to it. Built by Menlo Research (Singapore), licensed under Apache 2.0 – which matters if you plan to deploy it inside a company. The project sits at 43.5k GitHub stars and 5.3 million downloads as of mid-2026. Not niche anymore.
System requirements that actually matter
The official README lists the floor bluntly: macOS 13.6+ needs 8 GB RAM for 3B models, 16 GB for 7B, and 32 GB for 13B. Windows 10+ with NVIDIA/AMD/Intel Arc GPU support. Most Linux distributions work, with GPU acceleration available.
The GPU threshold is 6 GB VRAM. Official troubleshooting spells it out: Jan detects hardware and picks a backend automatically, but only prefers the GPU path when the card reports at least 6 GB of VRAM – below that, it falls back to CPU with no warning. That laptop with a 4 GB GTX 1650? Jan loads fine, looks fine, and quietly runs everything on your processor. Check this before you blame Jan for slow tokens.
| Model size | Min system RAM | Min VRAM (for GPU path) | Notes |
|---|---|---|---|
| 3B (Q4) | 8 GB | 6 GB | Runs on CPU if VRAM below threshold |
| 7B (Q4) | 16 GB | 6-8 GB | GPU path meaningfully faster |
| 13B (Q4) | 32 GB | 10-12 GB | Discrete GPU effectively required |
Install Jan on your private LLM desktop
Grab the installer from the source directly. Windows: download the .exe from jan.ai – works on Windows 10 and 11 with no additional setup. macOS: download the .dmg (Intel or Apple Silicon build). Linux users get an AppImage, .deb, or .rpm.
Skip Homebrew Cask, winget, and Chocolatey. Community-mirrored versions lag behind the official release, and Jan ships backend fixes regularly – you’ll miss them.
# macOS (Apple Silicon example)
curl -L -o Jan.dmg https://app.jan.ai/download/latest/mac-universal
open Jan.dmg
# Linux (Debian/Ubuntu)
wget https://app.jan.ai/download/latest/linux-deb
sudo dpkg -i jan-linux-amd64-*.deb
# Verify the API server after first launch
curl http://localhost:1337/v1/modelsThat last curl is the smoke test. A successful call returns a JSON list of loaded models (per the dev.to hands-on review). If it errors, the app didn’t start the local server correctly – and no chat will work either.
First-run configuration (the settings you’ll regret ignoring)
Launch Jan, skip through the onboarding, and go straight to Settings. Four things worth changing before you download a single model:
- Turn off analytics. Jan doesn’t send prompts, but it does ship with a basic analytics ping enabled by default. For HIPAA and CMMC machines, turning it off simplifies the privacy assertion. Settings → Privacy.
- Confirm the API server binds to 127.0.0.1. Default is localhost – confirm it in settings. Exposing it on a LAN for shared use needs its own authentication layer, separate conversation.
- Disable cloud provider bridges you’re not using. The Anthropic and OpenAI toggles have no business being visible on a machine that’s supposed to stay air-gapped. One click to hide them.
- Context length: leave it at auto (or set to 0). Turns out manual context values are a source of quiet pain – setting ctx to 0 lets Jan load context directly from the model, and the errors that appeared with manual values just disappear. Documented by practical365.com from direct hands-on testing.
“Address already in use” – and the other two errors
Port 1337 collision. If your app logs show a bind failure, something else grabbed Jan’s default Local API Server port. Common culprits: another local dev server, a game server, or a previous Jan instance that didn’t exit cleanly. Fix: Settings → Local API Server → Configuration → Server Port. Change it to anything unused. This comes straight from the official troubleshooting docs.
Blank window on Linux. Some Wayland compositors cause a blank window on launch – markaicode.com flagged this in their Ubuntu install report. Launch with jan --disable-gpu or switch to X11 temporarily. This one bit me on Fedora 40 KDE – twenty minutes staring at an empty frame before I found the flag.
ggml_vulkan errors on AMD/Intel Arc. The Vulkan backend requires up-to-date GPU drivers. If you see a ggml_vulkan error in the logs (per official troubleshooting docs), update your drivers and confirm Vulkan is installed (vulkaninfo on Linux). Select the Vulkan backend under Settings → Model Providers → Llama.cpp → Backend. On Mac: Metal acceleration kicks in automatically on M-series. A ggml_metal error typically signals the model is too large for available unified memory – use a smaller or more heavily quantized model.
Upgrading and uninstalling cleanly
Jan checks for updates on launch and prompts to download. For a regulated environment, don’t just click yes – read the changelog first. The catch: v0.8.x introduced enough changes that some extensions broke silently. Worth knowing what changed before you update a production machine.
What’s in the recent releases is genuinely useful. v0.8.3 (2026-06-24) added navigable message version branching, a unified reasoning timeline, video input for local vision models, and streaming performance fixes. v0.8.2 before it brought AMD ROCm/HIP backend support on Linux and pause/resume for model downloads – both per the official Jan changelog.
To uninstall on macOS: drag Jan.app to the trash, then rm -rf ~/jan to remove the data directory (models, chats, settings all live there). On Windows: Add/Remove Programs, then delete %APPDATA%Jan. On Linux: sudo apt remove jan plus rm -rf ~/.config/Jan ~/jan. Models accumulate fast – confirm you’ve backed up anything you want before deleting.
The honest limits
Jan is not going to match GPT-4 or Claude at general reasoning on consumer hardware. That’s a physics problem, not a Jan problem. What it does give you: a private LLM desktop that works offline, runs the current generation of open models, and integrates with any OpenAI-compatible client you already use. The API on localhost:1337 is a real OpenAI-compatible endpoint – anything pointed at api.openai.com can be pointed at Jan instead.
The setup honestly isn’t the hard part. The hard part is choosing which model to download first. If you want a starting point: grab a 3B-4B Q4 model from the built-in Hub. That’s your five-minute win. Everything else is tuning.
FAQ
Does Jan need internet after install?
No. Model downloads and update checks need it. Chat, inference, and the local API server don’t.
Can I run Jan on a work laptop with 16 GB RAM and integrated graphics?
Yes, but the GPU path won’t activate – integrated graphics almost never report 6 GB VRAM, so Jan runs everything on your CPU. That’s fine for 3B-7B Q4 models; just don’t expect snappy responses on anything bigger. If you’re demoing to stakeholders, grab a small 3B model first. It responds fast enough to feel responsive, which matters more than raw capability for a first impression.
Is Jan actually private, or is that marketing?
Mostly true, with one asterisk worth reading. Local inference stays on your machine – prompts never leave. But the app ships with an analytics ping enabled by default (no prompt content, but a ping), plus optional cloud bridges to OpenAI/Anthropic/Groq that are off unless you enable them. Turn off analytics in Settings → Privacy, leave the cloud toggles alone, and you have a verifiably air-gapped setup. “Verifiably” because the source is on GitHub under Apache 2.0 – you can read exactly what it sends.
Next step: After Jan is installed and the curl smoke test returns JSON, open a terminal and point any OpenAI SDK at http://localhost:1337/v1 with any string as the API key. That’s the moment your existing scripts start running on a private LLM desktop instead of someone else’s servers.