What are the hardware requirements for Gemma 2B Gptq 8bit?

Gemma 2B Gptq 8bit requires approximately 3.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 2B Gptq 8bit and how large is it?

Gemma 2B Gptq 8bit is developed by vessl, a model with 2b 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 2B Gptq 8bit?

Gemma 2B Gptq 8bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 2B Gptq 8bit by vessl — VRAM 3.1GB, 8K context

Name: Gemma 2B Gptq 8bit
Author: vessl

Gemma 2B Gptq 8bit is an open-source language model by vessl. Features: 2b LLM, VRAM: 3.1GB, Context: 8K, Quantized, LLM Explorer Score: 0.13.

Arxiv:1910.09700 8-bit 8bit Endpoints compatible Gemma Gptq Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/vessl/gemma-2b-gptq-8bit

Gemma 2B Gptq 8bit Parameters and Internals

LLM Name	Gemma 2B Gptq 8bit
Repository 🤗	https://huggingface.co/vessl/gemma-2b-gptq-8bit
Model Size	2b
Required VRAM	3.1 GB
Updated	2026-05-24
Maintainer	vessl
Model Type	gemma
Model Files	3.1 GB
GPTQ Quantization	Yes
Quantization Type	gptq\|8bit
Model Architecture	GemmaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.41.1
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	float16

Rank the Gemma 2B Gptq 8bit 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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Gemma 1.1 2B It GPTQ	8K / 3.1 GB	33834	1
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... 2.8 Gemma 2B HQQ 1bit Smashed	8K / 1.3 GB	5	0
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Note: green Score (e.g. "73.2") means that the model is better than vessl/gemma-2b-gptq-8bit.