What are the hardware requirements for Gemma 4 E2B It Qat 4bit?

Gemma 4 E2B It Qat 4bit requires approximately 4.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 4 E2B It Qat 4bit and how large is it?

Gemma 4 E2B It Qat 4bit is developed by mlx-community, a model with 1.4b 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 Gemma 4 E2B It Qat 4bit?

Gemma 4 E2B It Qat 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 4 E2B It Qat 4bit by mlx-community — VRAM 4.3GB

Name: Gemma 4 E2B It Qat 4bit
Author: mlx-community

Gemma 4 E2B It Qat 4bit is an open-source language model by mlx-community. Features: 1.4b LLM, VRAM: 4.3GB, Quantized, LLM Explorer Score: 0.28.

4-bit 4bit Conversational En Gemma4 Image-text-to-text Mlx Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/mlx-community/gemma-4-E2B-it-qat-4bit

Gemma 4 E2B It Qat 4bit Parameters and Internals

LLM Name	Gemma 4 E2B It Qat 4bit
Repository 🤗	https://huggingface.co/mlx-community/gemma-4-E2B-it-qat-4bit
Model Size	1.4b
Required VRAM	4.3 GB
Updated	2026-08-08
Maintainer	mlx-community
Model Type	gemma4
Model Files	4.3 GB
Supported Languages	en
Quantization Type	4bit
Model Architecture	Gemma4ForConditionalGeneration
Transformers Version	5.6.2
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Gemma 4 E2B It Qat 4bit 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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