QVQ-72B-Preview-GGUF
Released by lmstudio-community in 2024, QVQ-72B-Preview-GGUF is a 72 billion parameter image-generation model. QVQ-72B-Preview-GGUF is an open-weights image model with roughly 72 billion parameters.
by lmstudio-community · 72B parameters
Best for
Ways to use QVQ-72B-Preview-GGUF in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your lmstudio-community API key. osFoundry discovers QVQ-72B-Preview-GGUF 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
QVQ-72B-Preview-GGUF 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 QVQ-72B-Preview-GGUF
QVQ-72B-Preview-GGUF runs on a single A100 80GB or H100 80GB at Q4 quantisation (~44 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~173 GB).
QVQ-72B-Preview-GGUF vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about QVQ-72B-Preview-GGUF
Is QVQ-72B-Preview-GGUF free to use?
QVQ-72B-Preview-GGUF 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 QVQ-72B-Preview-GGUF 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 QVQ-72B-Preview-GGUF need?
Approximately 44 GB at Q4 quantisation, or 173 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run QVQ-72B-Preview-GGUF locally?
Yes. QVQ-72B-Preview-GGUF is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is QVQ-72B-Preview-GGUF best at?
QVQ-72B-Preview-GGUF is well-suited to image text to text.
How do I use QVQ-72B-Preview-GGUF in osFoundry?
Paste your lmstudio-community API key in the key dialog (or deploy the open weights for self-hostable models), assign QVQ-72B-Preview-GGUF to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by lmstudio-community on December 24, 2024. Source: https://huggingface.co/lmstudio-community/QVQ-72B-Preview-GGUF