Q2.5-72B-Instruct
pa374ge's Q2.5-72B-Instruct packs 72 billion parameters into a chat model. Q2.5-72B-Instruct is an open-weights chat model with roughly 72 billion parameters.
by pa374ge · 72B parameters
Best for
Ways to use Q2.5-72B-Instruct in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your pa374ge API key. osFoundry discovers Q2.5-72B-Instruct 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
Q2.5-72B-Instruct 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 Q2.5-72B-Instruct
Q2.5-72B-Instruct runs on a single A100 80GB or H100 80GB at Q4 quantisation (~44 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~173 GB).
Q2.5-72B-Instruct vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Q2.5-72B-Instruct
Is Q2.5-72B-Instruct free to use?
Q2.5-72B-Instruct 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 Q2.5-72B-Instruct 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 Q2.5-72B-Instruct need?
Approximately 44 GB at Q4 quantisation, or 173 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run Q2.5-72B-Instruct locally?
Yes. Q2.5-72B-Instruct is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Q2.5-72B-Instruct best at?
Q2.5-72B-Instruct is well-suited to text generation.
How do I use Q2.5-72B-Instruct in osFoundry?
Paste your pa374ge API key in the key dialog (or deploy the open weights for self-hostable models), assign Q2.5-72B-Instruct to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by pa374ge on April 22, 2026. Source: https://huggingface.co/pa374ge/Q2.5-72B-Instruct