gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine
gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine is a 31 billion parameter image-generation model from zecanard, released April 15, 2026. gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine is an open-weights image model with roughly 31 billion parameters.
by zecanard · 31B parameters
Best for
Ways to use gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your zecanard API key. osFoundry discovers gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine 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-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine 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-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine
gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine runs on a 24GB consumer or workstation GPU (~19 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~75 GB).
gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine
Is gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine free to use?
gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine 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-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine 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-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine need?
Approximately 19 GB at Q4 quantisation, or 75 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine locally?
Yes. gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine best at?
gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine is well-suited to image text to text.
How do I use gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine in osFoundry?
Paste your zecanard API key in the key dialog (or deploy the open weights for self-hostable models), assign gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by zecanard on April 15, 2026. Source: https://huggingface.co/zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-8bit-int8-affine