What are the hardware requirements for Gemma 4 31B It AWQ 4bit?

Gemma 4 31B It AWQ 4bit requires approximately 20.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 4 31B It AWQ 4bit and how large is it?

Gemma 4 31B It AWQ 4bit is developed by cyankiwi, a model with 31b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gemma 4 31B It AWQ 4bit perform on standard benchmarks?

Gemma 4 31B It AWQ 4bit has the following published scores: LMSYS ELO 1451. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gemma 4 31B It AWQ 4bit by cyankiwi — VRAM 20.9GB

Name: Gemma 4 31B It AWQ 4bit
Author: cyankiwi

Gemma 4 31B It AWQ 4bit is an open-source language model by cyankiwi. Features: 31b LLM, VRAM: 20.9GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.49, ELO: 1451.

4bit Awq Base model:google/gemma-4-31b-... Base model:quantized:google/ge... Compressed-tensors Conversational Endpoints compatible Gemma4 Image-text-to-text Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/cyankiwi/gemma-4-31B-it-AWQ-4bit

Gemma 4 31B It AWQ 4bit Benchmarks

LMSys ELO 1451 vs 1272 (so35) 14.1%

^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 31B It AWQ 4bit Parameters and Internals

LLM Name	Gemma 4 31B It AWQ 4bit
Repository 🤗	https://huggingface.co/cyankiwi/gemma-4-31B-it-AWQ-4bit
Base Model(s)	Gemma 4 31B It google/gemma-4-31B-it
Model Size	31b
Required VRAM	20.9 GB
Updated	2026-08-09
Maintainer	cyankiwi
Model Type	gemma4
Model Files	5.4 GB: 1-of-4 5.4 GB: 2-of-4 5.3 GB: 3-of-4 4.8 GB: 4-of-4
AWQ Quantization	Yes
Quantization Type	awq\|4bit
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.5.0.dev0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Gemma 4 31B It AWQ 4bit 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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