What are the hardware requirements for Gemma 4 E2b It Hybrid?

Gemma 4 E2b It Hybrid requires approximately 9.3 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 Gemma 4 E2b It Hybrid and how large is it?

Gemma 4 E2b It Hybrid is developed by Cactus-Compute, a model with 4.6b 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 Hybrid?

Gemma 4 E2b It Hybrid 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 Hybrid by Cactus-Compute — VRAM 9.3GB, 128K context

Name: Gemma 4 E2b It Hybrid
Author: Cactus-Compute

Gemma 4 E2b It Hybrid is an open-source language model by Cactus-Compute. Features: 4.6b LLM, VRAM: 9.3GB, Context: 128K, License: gemma, LLM Explorer Score: 0.29.

Base model:finetune:google/gem... Base model:google/gemma-4-e2b-... Conversational Custom code Custom generate Gemma-4-e2b-it-hybrid Gemma4 Hybrid Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Cactus-Compute/gemma-4-e2b-it-hybrid

Gemma 4 E2b It Hybrid Parameters and Internals

LLM Name	Gemma 4 E2b It Hybrid
Repository 🤗	https://huggingface.co/Cactus-Compute/gemma-4-e2b-it-hybrid
Base Model(s)	Gemma 4 E2B It google/gemma-4-E2B-it
Model Size	4.6b
Required VRAM	9.3 GB
Updated	2026-08-17
Maintainer	Cactus-Compute
Model Type	gemma-4-e2b-it-hybrid
Model Files	0.8 GB: 1-of-3 4.7 GB: 2-of-3 3.8 GB: 3-of-3
Model Architecture	Gemma4E2BItHybridForCausalLM
License	gemma
Context Length	131072
Model Max Length	131072
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	262144

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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