gemma-4-26B-A4B-it-TurboQuant-MLX-2bit
gemma-4-26B-A4B-it-TurboQuant-MLX-2bit (majentik, 2026) is a 26 billion parameter image-generation model. gemma-4-26B-A4B-it-TurboQuant-MLX-2bit is an open-weights image model with roughly 26 billion parameters.
by majentik · 26B parameters
Best for
Ways to use gemma-4-26B-A4B-it-TurboQuant-MLX-2bit in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your majentik API key. osFoundry discovers gemma-4-26B-A4B-it-TurboQuant-MLX-2bit 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-4-26B-A4B-it-TurboQuant-MLX-2bit 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-4-26B-A4B-it-TurboQuant-MLX-2bit
gemma-4-26B-A4B-it-TurboQuant-MLX-2bit runs on a single 16GB consumer GPU (~16 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~63 GB).
gemma-4-26B-A4B-it-TurboQuant-MLX-2bit vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about gemma-4-26B-A4B-it-TurboQuant-MLX-2bit
Is gemma-4-26B-A4B-it-TurboQuant-MLX-2bit free to use?
gemma-4-26B-A4B-it-TurboQuant-MLX-2bit 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-4-26B-A4B-it-TurboQuant-MLX-2bit 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-4-26B-A4B-it-TurboQuant-MLX-2bit need?
Approximately 16 GB at Q4 quantisation, or 63 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run gemma-4-26B-A4B-it-TurboQuant-MLX-2bit locally?
Yes. gemma-4-26B-A4B-it-TurboQuant-MLX-2bit is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is gemma-4-26B-A4B-it-TurboQuant-MLX-2bit best at?
gemma-4-26B-A4B-it-TurboQuant-MLX-2bit is well-suited to image text to text.
How do I use gemma-4-26B-A4B-it-TurboQuant-MLX-2bit in osFoundry?
Paste your majentik API key in the key dialog (or deploy the open weights for self-hostable models), assign gemma-4-26B-A4B-it-TurboQuant-MLX-2bit 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-4-26B-A4B-it-TurboQuant-MLX-2bit