NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6
Built by SiddhJagani, NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 is a 12 billion parameter chat model. NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 is an open-weights chat model with roughly 12 billion parameters.
by SiddhJagani · 12B parameters
Best for
Ways to use NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your SiddhJagani API key. osFoundry discovers NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 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
NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 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 NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6
NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 runs on a single 16GB consumer GPU (~8 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~29 GB).
NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6
Is NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 free to use?
NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 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 NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 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 NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 need?
Approximately 8 GB at Q4 quantisation, or 29 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 locally?
Yes. NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 best at?
NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 is well-suited to text generation.
How do I use NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 in osFoundry?
Paste your SiddhJagani API key in the key dialog (or deploy the open weights for self-hostable models), assign NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by SiddhJagani on December 17, 2025. Source: https://huggingface.co/SiddhJagani/NVIDIA-Nemotron-Nano-12B-v2-mlx-Q6