gemma-4-E2B-RotorQuant-GGUF-Q5_K_M
gemma-4-E2B-RotorQuant-GGUF-Q5_K_M (majentik, 2026) is a 2 billion parameter image-generation model. gemma-4-E2B-RotorQuant-GGUF-Q5_K_M is an open-weights image model with roughly 2 billion parameters.
by majentik · 2B parameters
Best for
Ways to use gemma-4-E2B-RotorQuant-GGUF-Q5_K_M in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your majentik API key. osFoundry discovers gemma-4-E2B-RotorQuant-GGUF-Q5_K_M 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
gemma-4-E2B-RotorQuant-GGUF-Q5_K_M 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 gemma-4-E2B-RotorQuant-GGUF-Q5_K_M
gemma-4-E2B-RotorQuant-GGUF-Q5_K_M 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).
gemma-4-E2B-RotorQuant-GGUF-Q5_K_M vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about gemma-4-E2B-RotorQuant-GGUF-Q5_K_M
Is gemma-4-E2B-RotorQuant-GGUF-Q5_K_M free to use?
gemma-4-E2B-RotorQuant-GGUF-Q5_K_M 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 gemma-4-E2B-RotorQuant-GGUF-Q5_K_M 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 gemma-4-E2B-RotorQuant-GGUF-Q5_K_M need?
Approximately 2 GB at Q4 quantisation, or 5 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run gemma-4-E2B-RotorQuant-GGUF-Q5_K_M locally?
Yes. gemma-4-E2B-RotorQuant-GGUF-Q5_K_M is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is gemma-4-E2B-RotorQuant-GGUF-Q5_K_M best at?
gemma-4-E2B-RotorQuant-GGUF-Q5_K_M is well-suited to image text to text.
How do I use gemma-4-E2B-RotorQuant-GGUF-Q5_K_M in osFoundry?
Paste your majentik API key in the key dialog (or deploy the open weights for self-hostable models), assign gemma-4-E2B-RotorQuant-GGUF-Q5_K_M to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by majentik on April 13, 2026. Source: https://huggingface.co/majentik/gemma-4-E2B-RotorQuant-GGUF-Q5_K_M