Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M
Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M (majentik, 2026) is a 120 billion parameter chat model. Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M is an open-weights chat model with roughly 120 billion parameters.
by majentik · 120B parameters
Best for
Ways to use Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your majentik API key. osFoundry discovers Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M 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-TurboQuant-GGUF-Q5_K_M 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-TurboQuant-GGUF-Q5_K_M
Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M 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-TurboQuant-GGUF-Q5_K_M 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-TurboQuant-GGUF-Q5_K_M
Is Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M free to use?
Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M 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-TurboQuant-GGUF-Q5_K_M 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-TurboQuant-GGUF-Q5_K_M 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-TurboQuant-GGUF-Q5_K_M locally?
Yes. Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M 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-TurboQuant-GGUF-Q5_K_M best at?
Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M is well-suited to text generation.
How do I use Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M in osFoundry?
Paste your majentik API key in the key dialog (or deploy the open weights for self-hostable models), assign Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M 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 14, 2026. Source: https://huggingface.co/majentik/Nemotron-3-Super-120B-A12B-TurboQuant-GGUF-Q5_K_M