apertus1-8b-step500-gsm8k-train-step5000
Released by Ilia2003Mah in 2026, apertus1-8b-step500-gsm8k-train-step5000 is a 8 billion parameter chat model. apertus1-8b-step500-gsm8k-train-step5000 is an open-weights chat model with roughly 8 billion parameters.
by Ilia2003Mah · 8B parameters
Best for
Ways to use apertus1-8b-step500-gsm8k-train-step5000 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Ilia2003Mah API key. osFoundry discovers apertus1-8b-step500-gsm8k-train-step5000 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
apertus1-8b-step500-gsm8k-train-step5000 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 apertus1-8b-step500-gsm8k-train-step5000
apertus1-8b-step500-gsm8k-train-step5000 runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~20 GB).
apertus1-8b-step500-gsm8k-train-step5000 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about apertus1-8b-step500-gsm8k-train-step5000
Is apertus1-8b-step500-gsm8k-train-step5000 free to use?
apertus1-8b-step500-gsm8k-train-step5000 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 apertus1-8b-step500-gsm8k-train-step5000 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 apertus1-8b-step500-gsm8k-train-step5000 need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run apertus1-8b-step500-gsm8k-train-step5000 locally?
Yes. apertus1-8b-step500-gsm8k-train-step5000 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is apertus1-8b-step500-gsm8k-train-step5000 best at?
apertus1-8b-step500-gsm8k-train-step5000 is well-suited to text generation.
How do I use apertus1-8b-step500-gsm8k-train-step5000 in osFoundry?
Paste your Ilia2003Mah API key in the key dialog (or deploy the open weights for self-hostable models), assign apertus1-8b-step500-gsm8k-train-step5000 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Ilia2003Mah on April 29, 2026. Source: https://huggingface.co/Ilia2003Mah/apertus1-8b-step500-gsm8k-train-step5000