MedVLThinker-3B-RL_m23k
MedVLThinker-3B-RL_m23k (UCSC-VLAA, 2025) is a 3 billion parameter image-generation model. MedVLThinker-3B-RL_m23k is an open-weights image model with roughly 3 billion parameters.
by UCSC-VLAA · 3B parameters
Best for
Ways to use MedVLThinker-3B-RL_m23k in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your UCSC-VLAA API key. osFoundry discovers MedVLThinker-3B-RL_m23k 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
MedVLThinker-3B-RL_m23k 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 MedVLThinker-3B-RL_m23k
MedVLThinker-3B-RL_m23k runs on a single 16GB consumer GPU (~2 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~8 GB).
MedVLThinker-3B-RL_m23k vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about MedVLThinker-3B-RL_m23k
Is MedVLThinker-3B-RL_m23k free to use?
MedVLThinker-3B-RL_m23k 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 MedVLThinker-3B-RL_m23k 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 MedVLThinker-3B-RL_m23k need?
Approximately 2 GB at Q4 quantisation, or 8 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run MedVLThinker-3B-RL_m23k locally?
Yes. MedVLThinker-3B-RL_m23k is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is MedVLThinker-3B-RL_m23k best at?
MedVLThinker-3B-RL_m23k is well-suited to image text to text.
How do I use MedVLThinker-3B-RL_m23k in osFoundry?
Paste your UCSC-VLAA API key in the key dialog (or deploy the open weights for self-hostable models), assign MedVLThinker-3B-RL_m23k to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by UCSC-VLAA on August 2, 2025. Source: https://huggingface.co/UCSC-VLAA/MedVLThinker-3B-RL_m23k