DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental
Built by lovedheart, DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental is a 345 billion parameter chat model. DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental is an open-weights chat model with roughly 345 billion parameters.
by lovedheart · 345B parameters
Best for
- complex multi-step reasoning
- agent orchestration with tool use
- long-document analysis and summarisation
Ways to use DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your lovedheart API key. osFoundry discovers DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental 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
DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental 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 DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental
DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental runs on a multi-GPU setup or H200 141GB at Q4 (~207 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~828 GB).
DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental
Is DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental free to use?
DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental 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 DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental 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 DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental need?
Approximately 207 GB at Q4 quantisation, or 828 GB at full FP16 precision. Requires multi-GPU at higher quantisation.
Can I run DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental locally?
Yes. DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental best at?
DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental is well-suited to complex multi-step reasoning, agent orchestration with tool use, long-document analysis and summarisation.
How do I use DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental in osFoundry?
Paste your lovedheart API key in the key dialog (or deploy the open weights for self-hostable models), assign DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by lovedheart on December 30, 2025. Source: https://huggingface.co/lovedheart/DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental