vit_base_patch32_clip_224.laion2b
timm's vit_base_patch32_clip_224.laion2b packs 2 billion parameters into a image-generation model. vit_base_patch32_clip_224.laion2b is an open-weights image model with roughly 2 billion parameters.
by timm · 2B parameters
Best for
Ways to use vit_base_patch32_clip_224.laion2b in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your timm API key. osFoundry discovers vit_base_patch32_clip_224.laion2b 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
vit_base_patch32_clip_224.laion2b 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 vit_base_patch32_clip_224.laion2b
vit_base_patch32_clip_224.laion2b 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 (~5 GB).
vit_base_patch32_clip_224.laion2b vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about vit_base_patch32_clip_224.laion2b
Is vit_base_patch32_clip_224.laion2b free to use?
vit_base_patch32_clip_224.laion2b 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 vit_base_patch32_clip_224.laion2b 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 vit_base_patch32_clip_224.laion2b need?
Approximately 2 GB at Q4 quantisation, or 5 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run vit_base_patch32_clip_224.laion2b locally?
Yes. vit_base_patch32_clip_224.laion2b is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is vit_base_patch32_clip_224.laion2b best at?
vit_base_patch32_clip_224.laion2b is well-suited to image feature extraction.
How do I use vit_base_patch32_clip_224.laion2b in osFoundry?
Paste your timm API key in the key dialog (or deploy the open weights for self-hostable models), assign vit_base_patch32_clip_224.laion2b 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 December 24, 2024. Source: https://huggingface.co/timm/vit_base_patch32_clip_224.laion2b