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

Gemma 4 E2B It AWQ 8bit requires approximately 8.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

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

Gemma 4 E2B It AWQ 8bit is developed by axteam, a model with 5.1b 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 AWQ 8bit?

Gemma 4 E2B It AWQ 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 AWQ 8bit by axteam — VRAM 8.5GB

Name: Gemma 4 E2B It AWQ 8bit
Author: axteam

Gemma 4 E2B It AWQ 8bit is an open-source language model by axteam. Features: 5.1b LLM, VRAM: 8.5GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.28.

8bit Awq Base model:google/gemma-4-e2b-... Base model:quantized:google/ge... Compressed-tensors Conversational Endpoints compatible Gemma Gemma4 Image-text-to-text Int8 Quantized Region:us Safetensors Vllm

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

Gemma 4 E2B It AWQ 8bit Parameters and Internals

LLM Name	Gemma 4 E2B It AWQ 8bit
Repository 🤗	https://huggingface.co/axteam/gemma-4-E2B-it-AWQ-8bit
Base Model(s)	Gemma 4 E2B It google/gemma-4-E2B-it
Model Size	5.1b
Required VRAM	8.5 GB
Updated	2026-10-15
Maintainer	axteam
Model Type	gemma4
Model Files	8.5 GB
AWQ Quantization	Yes
Quantization Type	awq\|8bit
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.5.0.dev0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Gemma 4 E2B It AWQ 8bit capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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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Note: green Score (e.g. "73.2") means that the model is better than axteam/gemma-4-E2B-it-AWQ-8bit.