What are the hardware requirements for Gemma 4 E4B It GGUF?

Gemma 4 E4B It GGUF requires approximately 1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 4 E4B It GGUF and how large is it?

Gemma 4 E4B It GGUF is developed by unsloth. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gemma 4 E4B It GGUF perform on standard benchmarks?

Gemma 4 E4B It GGUF has the following published scores: LMSYS ELO 1318. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gemma 4 E4B It GGUF by unsloth — VRAM 1GB

Name: Gemma 4 E4B It GGUF
Author: unsloth

Gemma 4 E4B It GGUF is an open-source language model by unsloth. Features: LLM, VRAM: 1GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.54, ELO: 1318.

Base model:google/gemma-4-e4b-... Base model:quantized:google/ge... Conversational Endpoints compatible Gemma Gemma4 Gguf Google Image-text-to-text Q3 Quantized Region:us Unsloth

Model Card on HF 🤗: https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF

Gemma 4 E4B It GGUF Benchmarks

LMSys ELO 1318 vs 1272 (so35) 3.6%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Gemma 4 E4B It GGUF Parameters and Internals

LLM Name	Gemma 4 E4B It GGUF
Repository 🤗	https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF
Base Model(s)	Gemma 4 E4B It google/gemma-4-E4B-it
Required VRAM	1 GB
Updated	2026-07-28
Maintainer	unsloth
Model Type	gemma4
Model Files	15.1 GB 4.8 GB 4.7 GB 4.1 GB 3.9 GB 4.8 GB 5.1 GB 5.0 GB 4.8 GB 5.5 GB 5.4 GB 7.1 GB 8.2 GB 3.5 GB 3.7 GB 3.8 GB 4.6 GB 5.1 GB 6.7 GB 7.5 GB 8.7 GB 1.0 GB 1.0 GB 1.9 GB
GGUF Quantization	Yes
Quantization Type	gguf\|q3\|q4_k\|q5_k
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.5.0.dev0
Torch Data Type	bfloat16

Rank the Gemma 4 E4B It GGUF 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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