Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8
Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 (RedHatAI, 2025) is a 24 billion parameter image-generation model. Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 is an open-weights image model with roughly 24 billion parameters.
by RedHatAI · 24B parameters
Best for
Ways to use Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your RedHatAI API key. osFoundry discovers Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 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-3.1-24B-Instruct-2503-quantized.w8a8 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-3.1-24B-Instruct-2503-quantized.w8a8
Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 runs on a single 16GB consumer GPU (~15 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~58 GB).
Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 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-3.1-24B-Instruct-2503-quantized.w8a8
Is Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 free to use?
Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 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-3.1-24B-Instruct-2503-quantized.w8a8 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-3.1-24B-Instruct-2503-quantized.w8a8 need?
Approximately 15 GB at Q4 quantisation, or 58 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 locally?
Yes. Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 best at?
Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 is well-suited to image text to text.
How do I use Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 in osFoundry?
Paste your RedHatAI API key in the key dialog (or deploy the open weights for self-hostable models), assign Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by RedHatAI on April 15, 2025. Source: https://huggingface.co/RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8