Qwen2.5-VL-72B-Instruct-quantized.w4a16
Released by RedHatAI in 2025, Qwen2.5-VL-72B-Instruct-quantized.w4a16 is a 72 billion parameter image-generation model. Qwen2.5-VL-72B-Instruct-quantized.w4a16 is an open-weights image model with roughly 72 billion parameters.
by RedHatAI · 72B parameters
Best for
Ways to use Qwen2.5-VL-72B-Instruct-quantized.w4a16 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your RedHatAI API key. osFoundry discovers Qwen2.5-VL-72B-Instruct-quantized.w4a16 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
Qwen2.5-VL-72B-Instruct-quantized.w4a16 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 Qwen2.5-VL-72B-Instruct-quantized.w4a16
Qwen2.5-VL-72B-Instruct-quantized.w4a16 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).
Qwen2.5-VL-72B-Instruct-quantized.w4a16 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Qwen2.5-VL-72B-Instruct-quantized.w4a16
Is Qwen2.5-VL-72B-Instruct-quantized.w4a16 free to use?
Qwen2.5-VL-72B-Instruct-quantized.w4a16 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 Qwen2.5-VL-72B-Instruct-quantized.w4a16 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 Qwen2.5-VL-72B-Instruct-quantized.w4a16 need?
Approximately 44 GB at Q4 quantisation, or 173 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run Qwen2.5-VL-72B-Instruct-quantized.w4a16 locally?
Yes. Qwen2.5-VL-72B-Instruct-quantized.w4a16 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Qwen2.5-VL-72B-Instruct-quantized.w4a16 best at?
Qwen2.5-VL-72B-Instruct-quantized.w4a16 is well-suited to image text to text.
How do I use Qwen2.5-VL-72B-Instruct-quantized.w4a16 in osFoundry?
Paste your RedHatAI API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen2.5-VL-72B-Instruct-quantized.w4a16 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by RedHatAI on February 7, 2025. Source: https://huggingface.co/RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w4a16