Qwen3.5-122B-A10B-FP8
Built by Shifusen, Qwen3.5-122B-A10B-FP8 is a 122 billion parameter image-generation model. Qwen3.5-122B-A10B-FP8 is an open-weights image model with roughly 122 billion parameters.
by Shifusen · 122B parameters
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Ways to use Qwen3.5-122B-A10B-FP8 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Shifusen API key. osFoundry discovers Qwen3.5-122B-A10B-FP8 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-FP8 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-FP8
Qwen3.5-122B-A10B-FP8 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-FP8 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-FP8
Is Qwen3.5-122B-A10B-FP8 free to use?
Qwen3.5-122B-A10B-FP8 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-FP8 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-FP8 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-FP8 locally?
Yes. Qwen3.5-122B-A10B-FP8 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-FP8 best at?
Qwen3.5-122B-A10B-FP8 is well-suited to image text to text.
How do I use Qwen3.5-122B-A10B-FP8 in osFoundry?
Paste your Shifusen API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen3.5-122B-A10B-FP8 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Shifusen on February 25, 2026. Source: https://huggingface.co/Shifusen/Qwen3.5-122B-A10B-FP8