mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4
mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 (mlx-community, 2025) is a 24 billion parameter chat model. mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 is an open-weights chat model with roughly 24 billion parameters.
by mlx-community · 24B parameters
Best for
Ways to use mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your mlx-community API key. osFoundry discovers mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 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
mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 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 mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4
mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 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).
mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4
Is mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 free to use?
mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 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 mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 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 mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 need?
Approximately 15 GB at Q4 quantisation, or 58 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 locally?
Yes. mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 best at?
mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 is well-suited to text generation.
How do I use mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 in osFoundry?
Paste your mlx-community API key in the key dialog (or deploy the open weights for self-hostable models), assign mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by mlx-community on December 14, 2025. Source: https://huggingface.co/mlx-community/mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-MXFP4