Qwen3.5-397B-A17B-JANG_2L
Built by JANGQ-AI, Qwen3.5-397B-A17B-JANG_2L is a 397 billion parameter chat model. Qwen3.5-397B-A17B-JANG_2L is an open-weights chat model with roughly 397 billion parameters.
by JANGQ-AI · 397B parameters
Best for
Ways to use Qwen3.5-397B-A17B-JANG_2L in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your JANGQ-AI API key. osFoundry discovers Qwen3.5-397B-A17B-JANG_2L 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
Qwen3.5-397B-A17B-JANG_2L 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 Qwen3.5-397B-A17B-JANG_2L
Qwen3.5-397B-A17B-JANG_2L runs on a multi-GPU setup or H200 141GB at Q4 (~239 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~953 GB).
Qwen3.5-397B-A17B-JANG_2L vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Qwen3.5-397B-A17B-JANG_2L
Is Qwen3.5-397B-A17B-JANG_2L free to use?
Qwen3.5-397B-A17B-JANG_2L 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 Qwen3.5-397B-A17B-JANG_2L 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 Qwen3.5-397B-A17B-JANG_2L need?
Approximately 239 GB at Q4 quantisation, or 953 GB at full FP16 precision. Requires multi-GPU at higher quantisation.
Can I run Qwen3.5-397B-A17B-JANG_2L locally?
Yes. Qwen3.5-397B-A17B-JANG_2L is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Qwen3.5-397B-A17B-JANG_2L best at?
Qwen3.5-397B-A17B-JANG_2L is well-suited to text generation.
How do I use Qwen3.5-397B-A17B-JANG_2L in osFoundry?
Paste your JANGQ-AI API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen3.5-397B-A17B-JANG_2L to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by JANGQ-AI on March 20, 2026. Source: https://huggingface.co/JANGQ-AI/Qwen3.5-397B-A17B-JANG_2L