Mistral-Small-4-119B-A6B-JANG_2L
Mistral-Small-4-119B-A6B-JANG_2L (JANGQ-AI, 2026) is a 119 billion parameter chat model. Mistral-Small-4-119B-A6B-JANG_2L is an open-weights chat model with roughly 119 billion parameters.
by JANGQ-AI · 119B parameters
Best for
Ways to use Mistral-Small-4-119B-A6B-JANG_2L in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your JANGQ-AI API key. osFoundry discovers Mistral-Small-4-119B-A6B-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
Mistral-Small-4-119B-A6B-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 Mistral-Small-4-119B-A6B-JANG_2L
Mistral-Small-4-119B-A6B-JANG_2L runs on a single A100 80GB or H100 80GB at Q4 quantisation (~72 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~286 GB).
Mistral-Small-4-119B-A6B-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 Mistral-Small-4-119B-A6B-JANG_2L
Is Mistral-Small-4-119B-A6B-JANG_2L free to use?
Mistral-Small-4-119B-A6B-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 Mistral-Small-4-119B-A6B-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 Mistral-Small-4-119B-A6B-JANG_2L need?
Approximately 72 GB at Q4 quantisation, or 286 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run Mistral-Small-4-119B-A6B-JANG_2L locally?
Yes. Mistral-Small-4-119B-A6B-JANG_2L is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Mistral-Small-4-119B-A6B-JANG_2L best at?
Mistral-Small-4-119B-A6B-JANG_2L is well-suited to text generation.
How do I use Mistral-Small-4-119B-A6B-JANG_2L in osFoundry?
Paste your JANGQ-AI API key in the key dialog (or deploy the open weights for self-hostable models), assign Mistral-Small-4-119B-A6B-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 23, 2026. Source: https://huggingface.co/JANGQ-AI/Mistral-Small-4-119B-A6B-JANG_2L