What are the hardware requirements for Llama 3.2 1B Aimv2 V2?

Llama 3.2 1B Aimv2 V2 requires approximately 0.8 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 1B Aimv2 V2 and how large is it?

Llama 3.2 1B Aimv2 V2 is developed by WonseokJayJung, a model with 1b 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 1B Aimv2 V2?

Llama 3.2 1B Aimv2 V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3.2 1B Aimv2 V2 by WonseokJayJung — VRAM 0.8GB, 128K context

Name: Llama 3.2 1B Aimv2 V2
Author: WonseokJayJung

Llama 3.2 1B Aimv2 V2 is an open-source language model by WonseokJayJung. Features: 1b LLM, VRAM: 0.8GB, Context: 128K, Quantized, Instruction-Based, LLM Explorer Score: 0.25.

4bit Conversational Endpoints compatible Gguf Instruct Llama Llama.cpp Quantized Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/WonseokJayJung/llama-3.2-1b-aimv2-v2

Llama 3.2 1B Aimv2 V2 Parameters and Internals

LLM Name	Llama 3.2 1B Aimv2 V2
Repository 🤗	https://huggingface.co/WonseokJayJung/llama-3.2-1b-aimv2-v2
Model Name	unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
Model Size	1b
Required VRAM	0.8 GB
Updated	2026-08-02
Maintainer	WonseokJayJung
Model Type	llama
Instruction-Based	Yes
Model Files	0.8 GB 2.5 GB
GGUF Quantization	Yes
Quantization Type	4bit\|gguf
Model Architecture	LlamaForCausalLM
Context Length	131072
Model Max Length	131072
Tokenizer Class	TokenizersBackend
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the Llama 3.2 1B Aimv2 V2 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

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Note: green Score (e.g. "73.2") means that the model is better than WonseokJayJung/llama-3.2-1b-aimv2-v2.