What are the hardware requirements for Gemma 4 E2B It MLX 8bit?

Gemma 4 E2B It MLX 8bit requires approximately 5.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 4 E2B It MLX 8bit and how large is it?

Gemma 4 E2B It MLX 8bit is developed by unsloth, a model with 1.3b 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 MLX 8bit?

Gemma 4 E2B It MLX 8bit 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 MLX 8bit by unsloth — VRAM 5.9GB

Name: Gemma 4 E2B It MLX 8bit
Author: unsloth

Gemma 4 E2B It MLX 8bit is an open-source language model by unsloth. Features: 1.3b LLM, VRAM: 5.9GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.25.

8-bit 8bit Base model:google/gemma-4-e2b-... Base model:quantized:google/ge... Conversational Gemma Gemma4 Google Image-text-to-text Quantized Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/unsloth/gemma-4-E2B-it-MLX-8bit

Gemma 4 E2B It MLX 8bit Parameters and Internals

LLM Name	Gemma 4 E2B It MLX 8bit
Repository 🤗	https://huggingface.co/unsloth/gemma-4-E2B-it-MLX-8bit
Base Model(s)	Gemma 4 E2B It google/gemma-4-E2B-it
Model Size	1.3b
Required VRAM	5.9 GB
Updated	2026-08-08
Maintainer	unsloth
Model Type	gemma4
Model Files	5.4 GB: 1-of-2 0.5 GB: 2-of-2 4.9 GB
Quantization Type	8bit
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.5.0.dev0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

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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