gemma-4-31B-it-GGUF_HB16
Built by gecfdo, gemma-4-31B-it-GGUF_HB16 is a 31 billion parameter image-generation model. gemma-4-31B-it-GGUF_HB16 is an open-weights image model with roughly 31 billion parameters.
by gecfdo · 31B parameters
Best for
Ways to use gemma-4-31B-it-GGUF_HB16 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your gecfdo API key. osFoundry discovers gemma-4-31B-it-GGUF_HB16 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-GGUF_HB16 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-GGUF_HB16
gemma-4-31B-it-GGUF_HB16 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-GGUF_HB16 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-GGUF_HB16
Is gemma-4-31B-it-GGUF_HB16 free to use?
gemma-4-31B-it-GGUF_HB16 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-GGUF_HB16 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-GGUF_HB16 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-GGUF_HB16 locally?
Yes. gemma-4-31B-it-GGUF_HB16 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-GGUF_HB16 best at?
gemma-4-31B-it-GGUF_HB16 is well-suited to image text to text.
How do I use gemma-4-31B-it-GGUF_HB16 in osFoundry?
Paste your gecfdo API key in the key dialog (or deploy the open weights for self-hostable models), assign gemma-4-31B-it-GGUF_HB16 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by gecfdo on April 5, 2026. Source: https://huggingface.co/gecfdo/gemma-4-31B-it-GGUF_HB16