unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT
unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT is a 9 billion parameter image-generation model from DanyDA, released March 6, 2026. unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT is an open-weights image model with roughly 9 billion parameters.
by DanyDA · 9B parameters
Best for
Ways to use unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your DanyDA API key. osFoundry discovers unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT 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
unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT 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 unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT
unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT runs on a single 16GB consumer GPU (~6 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~22 GB).
unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT
Is unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT free to use?
unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT 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 unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT 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 unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT need?
Approximately 6 GB at Q4 quantisation, or 22 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT locally?
Yes. unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT best at?
unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT is well-suited to image text to text.
How do I use unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT in osFoundry?
Paste your DanyDA API key in the key dialog (or deploy the open weights for self-hostable models), assign unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by DanyDA on March 6, 2026. Source: https://huggingface.co/DanyDA/unsloth_Qwen3.5-9B-GGUF_Qwen3.5-9B-Q8_0-GGUF-SPLIT