sarvam30b_INT4_quantisation
meghanamakkapati's sarvam30b_INT4_quantisation is a chat model. sarvam30b_INT4_quantisation is an open-weights chat model.
by meghanamakkapati
Best for
Ways to use sarvam30b_INT4_quantisation in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your meghanamakkapati API key. osFoundry discovers sarvam30b_INT4_quantisation 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
sarvam30b_INT4_quantisation 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.
sarvam30b_INT4_quantisation vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about sarvam30b_INT4_quantisation
Is sarvam30b_INT4_quantisation free to use?
sarvam30b_INT4_quantisation 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 sarvam30b_INT4_quantisation commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
Can I run sarvam30b_INT4_quantisation locally?
Yes. sarvam30b_INT4_quantisation is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is sarvam30b_INT4_quantisation best at?
sarvam30b_INT4_quantisation is well-suited to text generation.
How do I use sarvam30b_INT4_quantisation in osFoundry?
Paste your meghanamakkapati API key in the key dialog (or deploy the open weights for self-hostable models), assign sarvam30b_INT4_quantisation to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by meghanamakkapati on May 8, 2026. Source: https://huggingface.co/meghanamakkapati/sarvam30b_INT4_quantisation