ese_vovnet39b.ra_in1k
Released by timm in 2023, ese_vovnet39b.ra_in1k is a 39 billion parameter image-generation model. ese_vovnet39b.ra_in1k is an open-weights image model with roughly 39 billion parameters.
by timm · 39B parameters
Best for
Ways to use ese_vovnet39b.ra_in1k in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your timm API key. osFoundry discovers ese_vovnet39b.ra_in1k 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
ese_vovnet39b.ra_in1k 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 ese_vovnet39b.ra_in1k
ese_vovnet39b.ra_in1k runs on a 24GB consumer or workstation GPU (~24 GB VRAM with KV-cache headroom). Full-precision inference requires an H200 141GB or 2x A100 80GB at FP16 (~94 GB).
ese_vovnet39b.ra_in1k vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about ese_vovnet39b.ra_in1k
Is ese_vovnet39b.ra_in1k free to use?
ese_vovnet39b.ra_in1k 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 ese_vovnet39b.ra_in1k 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 ese_vovnet39b.ra_in1k need?
Approximately 24 GB at Q4 quantisation, or 94 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run ese_vovnet39b.ra_in1k locally?
Yes. ese_vovnet39b.ra_in1k is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is ese_vovnet39b.ra_in1k best at?
ese_vovnet39b.ra_in1k is well-suited to image classification.
How do I use ese_vovnet39b.ra_in1k in osFoundry?
Paste your timm API key in the key dialog (or deploy the open weights for self-hostable models), assign ese_vovnet39b.ra_in1k to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by timm on April 21, 2023. Source: https://huggingface.co/timm/ese_vovnet39b.ra_in1k