QClaw-4B-mlx_4bit_GPTQ
Built by WaveCut, QClaw-4B-mlx_4bit_GPTQ is a 4 billion parameter chat model. QClaw-4B-mlx_4bit_GPTQ is an open-weights chat model with roughly 4 billion parameters.
by WaveCut · 4B parameters
Best for
Ways to use QClaw-4B-mlx_4bit_GPTQ in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your WaveCut API key. osFoundry discovers QClaw-4B-mlx_4bit_GPTQ 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
QClaw-4B-mlx_4bit_GPTQ 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 QClaw-4B-mlx_4bit_GPTQ
QClaw-4B-mlx_4bit_GPTQ runs on a single 16GB consumer GPU (~3 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~10 GB).
QClaw-4B-mlx_4bit_GPTQ vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about QClaw-4B-mlx_4bit_GPTQ
Is QClaw-4B-mlx_4bit_GPTQ free to use?
QClaw-4B-mlx_4bit_GPTQ 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 QClaw-4B-mlx_4bit_GPTQ 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 QClaw-4B-mlx_4bit_GPTQ need?
Approximately 3 GB at Q4 quantisation, or 10 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run QClaw-4B-mlx_4bit_GPTQ locally?
Yes. QClaw-4B-mlx_4bit_GPTQ is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is QClaw-4B-mlx_4bit_GPTQ best at?
QClaw-4B-mlx_4bit_GPTQ is well-suited to text generation.
How do I use QClaw-4B-mlx_4bit_GPTQ in osFoundry?
Paste your WaveCut API key in the key dialog (or deploy the open weights for self-hostable models), assign QClaw-4B-mlx_4bit_GPTQ to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by WaveCut on April 25, 2026. Source: https://huggingface.co/WaveCut/QClaw-4B-mlx_4bit_GPTQ