Qwen3.5-9B-MLX-5.6bit-vision
Qwen3.5-9B-MLX-5.6bit-vision (spicyneuron, 2026) is a 9 billion parameter image-generation model. Qwen3.5-9B-MLX-5.6bit-vision is an open-weights image model with roughly 9 billion parameters.
by spicyneuron · 9B parameters
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Ways to use Qwen3.5-9B-MLX-5.6bit-vision in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your spicyneuron API key. osFoundry discovers Qwen3.5-9B-MLX-5.6bit-vision 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
Qwen3.5-9B-MLX-5.6bit-vision 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 Qwen3.5-9B-MLX-5.6bit-vision
Qwen3.5-9B-MLX-5.6bit-vision 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).
Qwen3.5-9B-MLX-5.6bit-vision vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Qwen3.5-9B-MLX-5.6bit-vision
Is Qwen3.5-9B-MLX-5.6bit-vision free to use?
Qwen3.5-9B-MLX-5.6bit-vision 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 Qwen3.5-9B-MLX-5.6bit-vision 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 Qwen3.5-9B-MLX-5.6bit-vision need?
Approximately 6 GB at Q4 quantisation, or 22 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Qwen3.5-9B-MLX-5.6bit-vision locally?
Yes. Qwen3.5-9B-MLX-5.6bit-vision is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Qwen3.5-9B-MLX-5.6bit-vision best at?
Qwen3.5-9B-MLX-5.6bit-vision is well-suited to image text to text.
How do I use Qwen3.5-9B-MLX-5.6bit-vision in osFoundry?
Paste your spicyneuron API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen3.5-9B-MLX-5.6bit-vision to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by spicyneuron on April 27, 2026. Source: https://huggingface.co/spicyneuron/Qwen3.5-9B-MLX-5.6bit-vision