Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS
Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS is a 30 billion parameter chat model from majentik, released April 13, 2026. Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS is an open-weights chat model with roughly 30 billion parameters.
by majentik · 30B parameters
Best for
Ways to use Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your majentik API key. osFoundry discovers Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS 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-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS 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-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS
Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS runs on a 24GB consumer or workstation GPU (~18 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~72 GB).
Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS 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-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS
Is Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS free to use?
Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS 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-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS 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-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS need?
Approximately 18 GB at Q4 quantisation, or 72 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS locally?
Yes. Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS best at?
Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS is well-suited to text generation.
How do I use Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS in osFoundry?
Paste your majentik API key in the key dialog (or deploy the open weights for self-hostable models), assign Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by majentik on April 13, 2026. Source: https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS