QwQ-32B-MLX-Q5
QwQ-32B-MLX-Q5 is a 32 billion parameter chat model from LibraxisAI, released June 16, 2025. QwQ-32B-MLX-Q5 is an open-weights chat model with roughly 32 billion parameters.
by LibraxisAI · 32B parameters
Best for
Ways to use QwQ-32B-MLX-Q5 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your LibraxisAI API key. osFoundry discovers QwQ-32B-MLX-Q5 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
QwQ-32B-MLX-Q5 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 QwQ-32B-MLX-Q5
QwQ-32B-MLX-Q5 runs on a 24GB consumer or workstation GPU (~20 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~77 GB).
QwQ-32B-MLX-Q5 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about QwQ-32B-MLX-Q5
Is QwQ-32B-MLX-Q5 free to use?
QwQ-32B-MLX-Q5 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 QwQ-32B-MLX-Q5 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 QwQ-32B-MLX-Q5 need?
Approximately 20 GB at Q4 quantisation, or 77 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run QwQ-32B-MLX-Q5 locally?
Yes. QwQ-32B-MLX-Q5 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is QwQ-32B-MLX-Q5 best at?
QwQ-32B-MLX-Q5 is well-suited to text generation.
How do I use QwQ-32B-MLX-Q5 in osFoundry?
Paste your LibraxisAI API key in the key dialog (or deploy the open weights for self-hostable models), assign QwQ-32B-MLX-Q5 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by LibraxisAI on June 16, 2025. Source: https://huggingface.co/LibraxisAI/QwQ-32B-MLX-Q5