Llama-3.1-405B-Instruct-AWQ-Int4
clowman's Llama-3.1-405B-Instruct-AWQ-Int4 packs 405 billion parameters into a chat model. Llama-3.1-405B-Instruct-AWQ-Int4 is an open-weights chat model with roughly 405 billion parameters.
by clowman · 405B parameters
Best for
Ways to use Llama-3.1-405B-Instruct-AWQ-Int4 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your clowman API key. osFoundry discovers Llama-3.1-405B-Instruct-AWQ-Int4 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-3.1-405B-Instruct-AWQ-Int4 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-3.1-405B-Instruct-AWQ-Int4
Llama-3.1-405B-Instruct-AWQ-Int4 runs on a multi-GPU setup or H200 141GB at Q4 (~243 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~972 GB).
Llama-3.1-405B-Instruct-AWQ-Int4 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Llama-3.1-405B-Instruct-AWQ-Int4
Is Llama-3.1-405B-Instruct-AWQ-Int4 free to use?
Llama-3.1-405B-Instruct-AWQ-Int4 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-3.1-405B-Instruct-AWQ-Int4 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-3.1-405B-Instruct-AWQ-Int4 need?
Approximately 243 GB at Q4 quantisation, or 972 GB at full FP16 precision. Requires multi-GPU at higher quantisation.
Can I run Llama-3.1-405B-Instruct-AWQ-Int4 locally?
Yes. Llama-3.1-405B-Instruct-AWQ-Int4 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Llama-3.1-405B-Instruct-AWQ-Int4 best at?
Llama-3.1-405B-Instruct-AWQ-Int4 is well-suited to text generation.
How do I use Llama-3.1-405B-Instruct-AWQ-Int4 in osFoundry?
Paste your clowman API key in the key dialog (or deploy the open weights for self-hostable models), assign Llama-3.1-405B-Instruct-AWQ-Int4 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by clowman on April 7, 2025. Source: https://huggingface.co/clowman/Llama-3.1-405B-Instruct-AWQ-Int4