cerebras_MiniMax-M2-REAP-162B-A10B-GGUF
Built by bartowski, cerebras_MiniMax-M2-REAP-162B-A10B-GGUF is a 162 billion parameter chat model. cerebras_MiniMax-M2-REAP-162B-A10B-GGUF is an open-weights chat model with roughly 162 billion parameters.
by bartowski · 162B parameters
Best for
Ways to use cerebras_MiniMax-M2-REAP-162B-A10B-GGUF in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your bartowski API key. osFoundry discovers cerebras_MiniMax-M2-REAP-162B-A10B-GGUF 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
cerebras_MiniMax-M2-REAP-162B-A10B-GGUF 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 cerebras_MiniMax-M2-REAP-162B-A10B-GGUF
cerebras_MiniMax-M2-REAP-162B-A10B-GGUF runs on a multi-GPU setup or H200 141GB at Q4 (~98 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~389 GB).
cerebras_MiniMax-M2-REAP-162B-A10B-GGUF vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about cerebras_MiniMax-M2-REAP-162B-A10B-GGUF
Is cerebras_MiniMax-M2-REAP-162B-A10B-GGUF free to use?
cerebras_MiniMax-M2-REAP-162B-A10B-GGUF 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 cerebras_MiniMax-M2-REAP-162B-A10B-GGUF 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 cerebras_MiniMax-M2-REAP-162B-A10B-GGUF need?
Approximately 98 GB at Q4 quantisation, or 389 GB at full FP16 precision. Requires multi-GPU at higher quantisation.
Can I run cerebras_MiniMax-M2-REAP-162B-A10B-GGUF locally?
Yes. cerebras_MiniMax-M2-REAP-162B-A10B-GGUF is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is cerebras_MiniMax-M2-REAP-162B-A10B-GGUF best at?
cerebras_MiniMax-M2-REAP-162B-A10B-GGUF is well-suited to text generation.
How do I use cerebras_MiniMax-M2-REAP-162B-A10B-GGUF in osFoundry?
Paste your bartowski API key in the key dialog (or deploy the open weights for self-hostable models), assign cerebras_MiniMax-M2-REAP-162B-A10B-GGUF to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by bartowski on November 17, 2025. Source: https://huggingface.co/bartowski/cerebras_MiniMax-M2-REAP-162B-A10B-GGUF