What are the hardware requirements for Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit?

Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit requires approximately 39.7 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit and how large is it?

Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit is developed by mlx-community, a model with 70b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit?

Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit by mlx-community — VRAM 39.7GB, 128K context

Name: Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit
Author: mlx-community

Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit is an open-source language model by mlx-community. Features: 70b LLM, VRAM: 39.7GB, Context: 128K, License: llama3.1, Quantized, Instruction-Based, LLM Explorer Score: 0.16.

4-bit 4bit Base model:nvidia/llama-3.1-ne... Base model:quantized:nvidia/ll... Conversational Dataset:nvidia/helpsteer2 En Instruct Llama Llama3.1 Mlx Nvidia Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mlx-community/nvidia_Llama-3.1-Nemotron-70B-Instruct-HF_4bit

Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit Parameters and Internals

LLM Name	Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit
Repository 🤗	https://huggingface.co/mlx-community/nvidia_Llama-3.1-Nemotron-70B-Instruct-HF_4bit
Base Model(s)	nvidia/Llama-3.1-Nemotron-70B-Instruct-HF nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
Model Size	70b
Required VRAM	39.7 GB
Updated	2026-08-07
Maintainer	mlx-community
Model Type	llama
Instruction-Based	Yes
Model Files	5.3 GB: 1-of-8 5.3 GB: 2-of-8 5.3 GB: 3-of-8 5.3 GB: 4-of-8 5.3 GB: 5-of-8 5.3 GB: 6-of-8 5.3 GB: 7-of-8 2.6 GB: 8-of-8
Supported Languages	en
Quantization Type	4bit
Model Architecture	LlamaForCausalLM
License	llama3.1
Context Length	131072
Model Max Length	131072
Transformers Version	4.40.0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Nvidia Llama 3.1 Nemotron 70B Instruct HF 4bit capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
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Data Analysis and Insight Generation
Text Generation
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Code Generation
Multi-Language Support and Translation

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