g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B
Released by laion in 2026, g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B is a 32 billion parameter chat model. g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B is an open-weights chat model with roughly 32 billion parameters.
by laion · 32B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your laion API key. osFoundry discovers g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B 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
g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B 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 g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B
g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B runs on a 24GB consumer or workstation GPU (~20 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~77 GB).
g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B
Is g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B free to use?
g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B 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 g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B 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 g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B need?
Approximately 20 GB at Q4 quantisation, or 77 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B locally?
Yes. g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B best at?
g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B in osFoundry?
Paste your laion API key in the key dialog (or deploy the open weights for self-hostable models), assign g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by laion on May 3, 2026. Source: https://huggingface.co/laion/g1_top8_85k_gptlong_swegym_32b_step300__Qwen3-32B