Llama-3.2-1B-222_add
Llama-3.2-1B-222_add (esinghal, 2026) is a 1 billion parameter chat model. Llama-3.2-1B-222_add is an open-weights chat model with roughly 1 billion parameters.
by esinghal · 1B parameters
Best for
Ways to use Llama-3.2-1B-222_add in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your esinghal API key. osFoundry discovers Llama-3.2-1B-222_add 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
Llama-3.2-1B-222_add 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 Llama-3.2-1B-222_add
Llama-3.2-1B-222_add runs on a single 16GB consumer GPU (~1 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~3 GB).
Llama-3.2-1B-222_add vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Llama-3.2-1B-222_add
Is Llama-3.2-1B-222_add free to use?
Llama-3.2-1B-222_add 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 Llama-3.2-1B-222_add 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 Llama-3.2-1B-222_add need?
Approximately 1 GB at Q4 quantisation, or 3 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Llama-3.2-1B-222_add locally?
Yes. Llama-3.2-1B-222_add is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Llama-3.2-1B-222_add best at?
Llama-3.2-1B-222_add is well-suited to text generation.
How do I use Llama-3.2-1B-222_add in osFoundry?
Paste your esinghal API key in the key dialog (or deploy the open weights for self-hostable models), assign Llama-3.2-1B-222_add to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by esinghal on April 15, 2026. Source: https://huggingface.co/esinghal/Llama-3.2-1B-222_add