projedanismanai-v2-qwen3-14b
Released by Rudblest in 2026, projedanismanai-v2-qwen3-14b is a 14 billion parameter chat model. projedanismanai-v2-qwen3-14b is an open-weights chat model with roughly 14 billion parameters.
by Rudblest · 14B parameters
Best for
Ways to use projedanismanai-v2-qwen3-14b in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Rudblest API key. osFoundry discovers projedanismanai-v2-qwen3-14b 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
projedanismanai-v2-qwen3-14b 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 projedanismanai-v2-qwen3-14b
projedanismanai-v2-qwen3-14b runs on a single 16GB consumer GPU (~9 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~34 GB).
projedanismanai-v2-qwen3-14b vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about projedanismanai-v2-qwen3-14b
Is projedanismanai-v2-qwen3-14b free to use?
projedanismanai-v2-qwen3-14b 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 projedanismanai-v2-qwen3-14b 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 projedanismanai-v2-qwen3-14b need?
Approximately 9 GB at Q4 quantisation, or 34 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run projedanismanai-v2-qwen3-14b locally?
Yes. projedanismanai-v2-qwen3-14b is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is projedanismanai-v2-qwen3-14b best at?
projedanismanai-v2-qwen3-14b is well-suited to text generation.
How do I use projedanismanai-v2-qwen3-14b in osFoundry?
Paste your Rudblest API key in the key dialog (or deploy the open weights for self-hostable models), assign projedanismanai-v2-qwen3-14b to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Rudblest on May 7, 2026. Source: https://huggingface.co/Rudblest/projedanismanai-v2-qwen3-14b