What are the hardware requirements for Gemma 4 E4B It Qat Mobile Transformers?

Gemma 4 E4B It Qat Mobile Transformers requires approximately 3.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 4 E4B It Qat Mobile Transformers and how large is it?

Gemma 4 E4B It Qat Mobile Transformers is developed by google, a model with 3.4b 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 E4B It Qat Mobile Transformers?

Gemma 4 E4B It Qat Mobile Transformers is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 4 E4B It Qat Mobile Transformers by google — VRAM 3.5GB

Name: Gemma 4 E4B It Qat Mobile Transformers
Author: google

Gemma 4 E4B It Qat Mobile Transformers is an open-source language model by google. Features: 3.4b LLM, VRAM: 3.5GB, License: apache-2.0, LLM Explorer Score: 0.3.

8-bit Any-to-any Base model:google/gemma-4-e4b-... Base model:quantized:google/ge... Endpoints compatible Gemma Gemma4 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/google/gemma-4-E4B-it-qat-mobile-transformers

Gemma 4 E4B It Qat Mobile Transformers Parameters and Internals

LLM Name	Gemma 4 E4B It Qat Mobile Transformers
Repository 🤗	https://huggingface.co/google/gemma-4-E4B-it-qat-mobile-transformers
Base Model(s)	Gemma 4 E4B It google/gemma-4-E4B-it
Model Size	3.4b
Required VRAM	3.5 GB
Updated	2026-07-07
Maintainer	google
Model Type	gemma4
Model Files	3.5 GB
Model Architecture	AutoModel
License	apache-2.0
Transformers Version	5.10.0.dev0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Gemma 4 E4B It Qat Mobile Transformers 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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