What are the hardware requirements for Llama 3.2 3Bx4?

Llama 3.2 3Bx4 requires approximately 20 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 3.2 3Bx4 and how large is it?

Llama 3.2 3Bx4 is developed by cloudyu, a model with 9.9b 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 3.2 3Bx4?

Llama 3.2 3Bx4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3.2 3Bx4 by cloudyu — VRAM 20GB, 128K context

Name: Llama 3.2 3Bx4
Author: cloudyu

Llama 3.2 3Bx4 is an open-source language model by cloudyu. Features: 9.9b LLM, VRAM: 20GB, Context: 128K, License: llama3.2, MoE, LLM Explorer Score: 0.22.

En Mixtral Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/cloudyu/Llama-3.2-3Bx4

Llama 3.2 3Bx4 Parameters and Internals

LLM Name	Llama 3.2 3Bx4
Repository 🤗	https://huggingface.co/cloudyu/Llama-3.2-3Bx4
Model Size	9.9b
Required VRAM	20 GB
Updated	2025-02-05
Maintainer	cloudyu
Model Type	mixtral
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 5.0 GB: 4-of-4
Supported Languages	en
Model Architecture	MixtralForCausalLM
License	llama3.2
Context Length	131072
Model Max Length	131072
Transformers Version	4.46.3
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|begin_of_text\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the Llama 3.2 3Bx4 capabilities

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

Best Alternatives to Llama 3.2 3Bx4

Best Alternatives	Context / RAM	Downloads	Likes
Llama 3.2 4x3B Instruct	128K / 20 GB	8	0

Note: green Score (e.g. "73.2") means that the model is better than cloudyu/Llama-3.2-3Bx4.