Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4
Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 (ibnzterrell, 2024) is a 70 billion parameter chat model. Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 is an open-weights chat model with roughly 70 billion parameters.
by ibnzterrell · 70B parameters
Best for
Ways to use Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your ibnzterrell API key. osFoundry discovers Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-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
Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-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 Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4
Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 runs on a single A100 80GB or H100 80GB at Q4 quantisation (~42 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~168 GB).
Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-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 Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4
Is Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 free to use?
Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-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 Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-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 Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 need?
Approximately 42 GB at Q4 quantisation, or 168 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 locally?
Yes. Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 best at?
Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 is well-suited to text generation.
How do I use Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4 in osFoundry?
Paste your ibnzterrell API key in the key dialog (or deploy the open weights for self-hostable models), assign Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-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 ibnzterrell on October 24, 2024. Source: https://huggingface.co/ibnzterrell/Nvidia-Llama-3.1-Nemotron-70B-Instruct-HF-AWQ-INT4