makisu-gemma4-e2b
makisu-gemma4-e2b (RinKana, 2026) is a 2 billion parameter image-generation model. makisu-gemma4-e2b is an open-weights image model with roughly 2 billion parameters.
by RinKana · 2B parameters
Best for
Ways to use makisu-gemma4-e2b in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your RinKana API key. osFoundry discovers makisu-gemma4-e2b 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
makisu-gemma4-e2b 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 makisu-gemma4-e2b
makisu-gemma4-e2b 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).
makisu-gemma4-e2b vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about makisu-gemma4-e2b
Is makisu-gemma4-e2b free to use?
makisu-gemma4-e2b 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 makisu-gemma4-e2b 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 makisu-gemma4-e2b need?
Approximately 2 GB at Q4 quantisation, or 5 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run makisu-gemma4-e2b locally?
Yes. makisu-gemma4-e2b is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is makisu-gemma4-e2b best at?
makisu-gemma4-e2b is well-suited to image text to text.
How do I use makisu-gemma4-e2b in osFoundry?
Paste your RinKana API key in the key dialog (or deploy the open weights for self-hostable models), assign makisu-gemma4-e2b to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by RinKana on April 26, 2026. Source: https://huggingface.co/RinKana/makisu-gemma4-e2b