llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core
grohitraj's llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core packs 8 billion parameters into a chat model. llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core is an open-weights chat model with roughly 8 billion parameters.
by grohitraj · 8B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your grohitraj API key. osFoundry discovers llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core 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-3-8b-Instruct-bnb-4bit-Optimal-Library_Core 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-3-8b-Instruct-bnb-4bit-Optimal-Library_Core
llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core 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).
llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core
Is llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core free to use?
llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core 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-3-8b-Instruct-bnb-4bit-Optimal-Library_Core 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-3-8b-Instruct-bnb-4bit-Optimal-Library_Core need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core locally?
Yes. llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core best at?
llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core in osFoundry?
Paste your grohitraj API key in the key dialog (or deploy the open weights for self-hostable models), assign llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by grohitraj on April 20, 2026. Source: https://huggingface.co/grohitraj/llama-3-8b-Instruct-bnb-4bit-Optimal-Library_Core