LLaDA-8B-Base-gg2m
Built by Fraser, LLaDA-8B-Base-gg2m is a 8 billion parameter chat model. LLaDA-8B-Base-gg2m is an open-weights chat model with roughly 8 billion parameters.
by Fraser · 8B parameters
Best for
Ways to use LLaDA-8B-Base-gg2m in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Fraser API key. osFoundry discovers LLaDA-8B-Base-gg2m 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
LLaDA-8B-Base-gg2m 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 LLaDA-8B-Base-gg2m
LLaDA-8B-Base-gg2m runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~20 GB).
LLaDA-8B-Base-gg2m vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about LLaDA-8B-Base-gg2m
Is LLaDA-8B-Base-gg2m free to use?
LLaDA-8B-Base-gg2m 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 LLaDA-8B-Base-gg2m 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 LLaDA-8B-Base-gg2m need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run LLaDA-8B-Base-gg2m locally?
Yes. LLaDA-8B-Base-gg2m is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is LLaDA-8B-Base-gg2m best at?
LLaDA-8B-Base-gg2m is well-suited to text generation.
How do I use LLaDA-8B-Base-gg2m in osFoundry?
Paste your Fraser API key in the key dialog (or deploy the open weights for self-hostable models), assign LLaDA-8B-Base-gg2m to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Fraser on February 10, 2026. Source: https://huggingface.co/Fraser/LLaDA-8B-Base-gg2m