What are the hardware requirements for Gemma 3n E2B 8bit?

Gemma 3n E2B 8bit requires approximately 6.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 3n E2B 8bit and how large is it?

Gemma 3n E2B 8bit is developed by mlx-community. 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 Gemma 3n E2B 8bit?

Gemma 3n E2B 8bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 3n E2B 8bit by mlx-community — VRAM 6.6GB

Name: Gemma 3n E2B 8bit
Author: mlx-community

Gemma 3n E2B 8bit is an open-source language model by mlx-community. Features: LLM, VRAM: 6.6GB, License: gemma, Quantized.

8-bit 8bit Audio-text-to-text Automatic-speech-recognition Automatic-speech-translation Base model:google/gemma-3n-e4b Base model:quantized:google/ge... Endpoints compatible Gemma3n Image-text-to-text Mlx Quantized Region:us Safetensors Sharded Tensorflow Video-text-to-text

Model Card on HF 🤗: https://huggingface.co/mlx-community/gemma-3n-E2B-8bit

Gemma 3n E2B 8bit Parameters and Internals

LLM Name	Gemma 3n E2B 8bit
Repository 🤗	https://huggingface.co/mlx-community/gemma-3n-E2B-8bit
Base Model(s)	google/gemma-3n-E4B google/gemma-3n-E4B
Required VRAM	6.6 GB
Updated	2026-08-08
Maintainer	mlx-community
Model Type	gemma3n
Model Files	5.3 GB: 1-of-2 1.3 GB: 2-of-2
Quantization Type	8bit
Model Architecture	Gemma3nForConditionalGeneration
License	gemma
Transformers Version	4.53.2
Padding Token	<pad>

Rank the Gemma 3n E2B 8bit 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

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