Qwen3.6-35B-A3B-JANG_3K
Qwen3.6-35B-A3B-JANG_3K (bearzi, 2026) is a 35 billion parameter chat model. Qwen3.6-35B-A3B-JANG_3K is an open-weights chat model with roughly 35 billion parameters.
by bearzi · 35B parameters
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Ways to use Qwen3.6-35B-A3B-JANG_3K in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your bearzi API key. osFoundry discovers Qwen3.6-35B-A3B-JANG_3K 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
Qwen3.6-35B-A3B-JANG_3K 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 Qwen3.6-35B-A3B-JANG_3K
Qwen3.6-35B-A3B-JANG_3K runs on a 24GB consumer or workstation GPU (~21 GB VRAM with KV-cache headroom). Full-precision inference requires an H200 141GB or 2x A100 80GB at FP16 (~84 GB).
Qwen3.6-35B-A3B-JANG_3K vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Qwen3.6-35B-A3B-JANG_3K
Is Qwen3.6-35B-A3B-JANG_3K free to use?
Qwen3.6-35B-A3B-JANG_3K 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 Qwen3.6-35B-A3B-JANG_3K 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 Qwen3.6-35B-A3B-JANG_3K need?
Approximately 21 GB at Q4 quantisation, or 84 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Qwen3.6-35B-A3B-JANG_3K locally?
Yes. Qwen3.6-35B-A3B-JANG_3K is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Qwen3.6-35B-A3B-JANG_3K best at?
Qwen3.6-35B-A3B-JANG_3K is well-suited to text generation.
How do I use Qwen3.6-35B-A3B-JANG_3K in osFoundry?
Paste your bearzi API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen3.6-35B-A3B-JANG_3K to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by bearzi on April 17, 2026. Source: https://huggingface.co/bearzi/Qwen3.6-35B-A3B-JANG_3K