InternVL3-8B-6bit
InternVL3-8B-6bit (mlx-community, 2025) is a 8 billion parameter image-generation model. InternVL3-8B-6bit is an open-weights image model with roughly 8 billion parameters.
by mlx-community · 8B parameters
Best for
Ways to use InternVL3-8B-6bit in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your mlx-community API key. osFoundry discovers InternVL3-8B-6bit 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
InternVL3-8B-6bit 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 InternVL3-8B-6bit
InternVL3-8B-6bit runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~20 GB).
InternVL3-8B-6bit vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about InternVL3-8B-6bit
Is InternVL3-8B-6bit free to use?
InternVL3-8B-6bit 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 InternVL3-8B-6bit 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 InternVL3-8B-6bit need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run InternVL3-8B-6bit locally?
Yes. InternVL3-8B-6bit is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is InternVL3-8B-6bit best at?
InternVL3-8B-6bit is well-suited to image text to text.
How do I use InternVL3-8B-6bit in osFoundry?
Paste your mlx-community API key in the key dialog (or deploy the open weights for self-hostable models), assign InternVL3-8B-6bit to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by mlx-community on April 18, 2025. Source: https://huggingface.co/mlx-community/InternVL3-8B-6bit