Qwen3.5-122B-A10B-MLX-4.7bit-vision
Released by spicyneuron in 2026, Qwen3.5-122B-A10B-MLX-4.7bit-vision is a 122 billion parameter chat model. Qwen3.5-122B-A10B-MLX-4.7bit-vision is an open-weights chat model with roughly 122 billion parameters.
by spicyneuron · 122B parameters
Best for
Ways to use Qwen3.5-122B-A10B-MLX-4.7bit-vision in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your spicyneuron API key. osFoundry discovers Qwen3.5-122B-A10B-MLX-4.7bit-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
Qwen3.5-122B-A10B-MLX-4.7bit-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 Qwen3.5-122B-A10B-MLX-4.7bit-vision
Qwen3.5-122B-A10B-MLX-4.7bit-vision runs on a single A100 80GB or H100 80GB at Q4 quantisation (~74 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~293 GB).
Qwen3.5-122B-A10B-MLX-4.7bit-vision vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Qwen3.5-122B-A10B-MLX-4.7bit-vision
Is Qwen3.5-122B-A10B-MLX-4.7bit-vision free to use?
Qwen3.5-122B-A10B-MLX-4.7bit-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 Qwen3.5-122B-A10B-MLX-4.7bit-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 Qwen3.5-122B-A10B-MLX-4.7bit-vision need?
Approximately 74 GB at Q4 quantisation, or 293 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run Qwen3.5-122B-A10B-MLX-4.7bit-vision locally?
Yes. Qwen3.5-122B-A10B-MLX-4.7bit-vision is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Qwen3.5-122B-A10B-MLX-4.7bit-vision best at?
Qwen3.5-122B-A10B-MLX-4.7bit-vision is well-suited to text generation.
How do I use Qwen3.5-122B-A10B-MLX-4.7bit-vision in osFoundry?
Paste your spicyneuron API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen3.5-122B-A10B-MLX-4.7bit-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 March 3, 2026. Source: https://huggingface.co/spicyneuron/Qwen3.5-122B-A10B-MLX-4.7bit-vision