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DATASHEET // JAN

Jan

Open-source ChatGPT desktop — runs models locally or via API.

OPEN SOURCECPU-CAPABLERuns locally
Actually FreeNo SignupOpen SourceWatermark-FreeHobbyist-OKAPI
Visit JanUPDATED 2026-05-12 · DIRECT LINK
jan.ai/
Jan — preview image

HARDWARE REQUIREMENTS //

Runs locally · Entry GPU (6–8 GB)

CPU-CAPABLE
Min VRAM
None
Rec. VRAM
8 GB
Min RAM
8 GB
Rec. RAM
16 GB
Disk
20 GB
GPU class
Entry GPU
Apple Silicon ✓CPU-CapableQuant: Q4_K_M, Q5_K_M, Q8_0 +1

CPU-only viable for 7B models; GPU strongly recommended for 13B+.

[ EDITORIAL PICK ]

Why we recommend Jan

DERIVED FROM METADATA — NOT SPONSORED
  • Open source

    Source is public — you can audit it, fork it, and you'll never lose access to your workflows if Jan the company changes direction.

  • CPU-capable

    Doesn't require a dedicated GPU — useful on laptops and headless servers.

  • Apple Silicon

    Native Metal / MPS support — runs on M-series Macs without CUDA gymnastics.

  • 4 quant formats

    Supports Q4_K_M, Q5_K_M, Q8_0 and 1 more — you can dial VRAM use up or down to match your card.

[ EVIDENCE NOTE ]

Documentation-led datasheet

This page summarizes upstream documentation, release information, and editorially reviewed catalogue fields. It is not presented as a hands-on benchmark. Verify changing requirements at the official project; report stale data through our corrections channel.

Memory guide →

AT-A-GLANCE SIGNALS //

DERIVED FROM THIS PAGE'S DATA
  • Install difficulty
    Easy

    Runs CPU-only — no CUDA / driver gymnastics required.

  • Hardware comfort
    Runs anywhere

    No dedicated GPU required.

  • Ecosystem
    Strong devkit

    Open-source AND ships an API — easy to integrate, possible to host yourself.

  • Verification
    Recent

    Catalogue entry last updated 65 days ago — re-verification due soon.

[ COMMUNITY GUIDES & WORKFLOWS ]

Tutorials & deep-dives for Jan

Hand-picked from YouTube, Reddit, GitHub, and the wider web. Each link goes straight to the source — we don't intercept or rewrite anything.

[ MORE IN THIS NICHE ]

Other local llm runners tools we rate

Three picks across different tradeoffs — so you don't end up with three near-clones of Jan.

LIGHTEST HARDWARE //

Transformers

The library every LLM ships against first.

OPEN SOURCE2–24 GB VRAM
BEST FREE OPTION //

llama.cpp

The C++ inference engine powering most local LLMs.

OPEN SOURCECPU-CAPABLE
TOP QUALITY //

Ollama

One-command local LLM runtime.

OPEN SOURCECPU-CAPABLE

What is Jan?

Jan is the closest open-source equivalent to a polished ChatGPT desktop client. Built on llama.cpp under the hood, it ships with a model hub UI, multi-model conversation tabs, an OpenAI-compatible local API server, and remote-model support (OpenAI, Anthropic, Groq) gated behind your own keys. Cross-platform and AGPL-licensed.

Pros & cons

✓ PROS

  • Native Electron app for Win / Mac / Linux — no terminal
  • Local OpenAI-compatible server out of the box
  • Built-in model hub with one-click downloads
  • Bring-your-own-key support for remote models

– CONS

  • Electron means ~200 MB RAM overhead before any model loads
  • Plugin / extension API is still maturing

What's actually free?

AGPL desktop app; free for personal & commercial use.

✓ Actually FreeNo SignupOpen SourceWatermark-Free

Alternatives

Msty

Polished local-LLM client with split chats and knowledge stacks.

FREEMIUM · $4.16/MOCPU-CAPABLE

AnythingLLM

RAG-first local LLM workspace with workspaces and agents.

OPEN SOURCECPU-CAPABLE