Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L
Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L is a 120 billion parameter chat model from TitanPythons, released April 6, 2026. Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L is an open-weights chat model with roughly 120 billion parameters.
by TitanPythons · 120B parameters
Best for
Ways to use Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your TitanPythons API key. osFoundry discovers Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L automatically — assign it to a Maestro role (router, direct, orchestrator, or fallback) in the Pipeline tab and it is live in every chat. Your key, your provider account — no token markup.
Deploy a dedicated endpoint
Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L is open-weights — run it locally for free, or deploy a dedicated GPU endpoint in your workspace for reserved capacity with no rate limits.
Use it in a Room App
Room Apps declare AI features in their manifest, then call them with invokeAI:
import { invokeAI } from '@osfoundry/app-sdk'
// 'summarize' is an AI feature declared in your app manifest.
const result = await invokeAI('summarize', userText)
Call it from your own apps
Once a model is wired into your workspace you can host it as an API and reach it from your own services, scripts, or CI — outside osFoundry.
What hardware can run Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L
Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L runs on a single A100 80GB or H100 80GB at Q4 quantisation (~72 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~288 GB).
Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L
Is Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L free to use?
Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L is free to run locally on your own hardware. Hosted access through osFoundry is metered (input Free (local), output Free (local)). You can switch between local and hosted at any time.
Can I use Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
How much VRAM does Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L need?
Approximately 72 GB at Q4 quantisation, or 288 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L locally?
Yes. Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L best at?
Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L is well-suited to text generation.
How do I use Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L in osFoundry?
Paste your TitanPythons API key in the key dialog (or deploy the open weights for self-hostable models), assign Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by TitanPythons on April 6, 2026. Source: https://huggingface.co/TitanPythons/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L