Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF
mradermacher's Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF packs 80 billion parameters into a chat model. Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF is an open-weights chat model with roughly 80 billion parameters.
by mradermacher · 80B parameters
Best for
- complex multi-step reasoning
- agent orchestration with tool use
- long-document analysis and summarisation
Ways to use Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your mradermacher API key. osFoundry discovers Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-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
Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-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 Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF
Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF runs on a single A100 80GB or H100 80GB at Q4 quantisation (~48 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~192 GB).
Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF
Is Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF free to use?
Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-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 Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-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 Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF need?
Approximately 48 GB at Q4 quantisation, or 192 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF locally?
Yes. Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF best at?
Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF is well-suited to complex multi-step reasoning, agent orchestration with tool use, long-document analysis and summarisation.
How do I use Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF in osFoundry?
Paste your mradermacher API key in the key dialog (or deploy the open weights for self-hostable models), assign Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by mradermacher on December 21, 2025. Source: https://huggingface.co/mradermacher/Smoothie-Qwen3-Next-80B-A3B-Instruct-i1-GGUF