What are the hardware requirements for Llama 3.1 8B Instruct?

Llama 3.1 8B Instruct requires approximately 16.1 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 Llama 3.1 8B Instruct and how large is it?

Llama 3.1 8B Instruct is developed by jebish7, a model with 8b 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 Llama 3.1 8B Instruct?

Llama 3.1 8B Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3.1 8B Instruct by jebish7 — VRAM 16.1GB, 128K context

Name: Llama 3.1 8B Instruct
Author: jebish7

Llama 3.1 8B Instruct is an open-source language model by jebish7. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, License: llama3.1, Instruction-Based, LLM Explorer Score: 0.23.

Arxiv:2204.05149 Base model:finetune:meta-llama... Base model:meta-llama/llama-3.... Conversational De En Es Facebook Fr Hi Instruct It Llama Llama-3 Meta Pt Pytorch Region:us Safetensors Sharded Tensorflow Th

Model Card on HF 🤗: https://huggingface.co/jebish7/Llama-3.1-8B-Instruct

Llama 3.1 8B Instruct Parameters and Internals

LLM Name	Llama 3.1 8B Instruct
Repository 🤗	https://huggingface.co/jebish7/Llama-3.1-8B-Instruct
Base Model(s)	meta-llama/Meta-Llama-3.1-8B meta-llama/Meta-Llama-3.1-8B
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-07-28
Maintainer	jebish7
Model Type	llama
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 1.2 GB: 4-of-4
Supported Languages	en de fr it pt hi es th
Model Architecture	LlamaForCausalLM
License	llama3.1
Context Length	131072
Model Max Length	131072
Transformers Version	4.42.3
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	bfloat16

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
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Data Analysis and Insight Generation
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