What are the hardware requirements for Gemma 2B?

Gemma 2B requires approximately 5 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 and how large is it?

Gemma 2B is developed by unsloth, 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?

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

Gemma 2B by unsloth — VRAM 5GB, 8K context

Name: Gemma 2B
Author: unsloth

Gemma 2B is an open-source language model by unsloth. Features: 2b LLM, VRAM: 5GB, Context: 8K, License: apache-2.0, LLM Explorer Score: 0.13.

En Endpoints compatible Gemma Gemma-2b Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/unsloth/gemma-2b

Gemma 2B Parameters and Internals

Model Type

Text Generation, Language Models

Use Cases

Areas:

Research, Commercial Applications

Applications:

Finetuning language models quickly and efficiently

Primary Use Cases:

Model finetuning with reduced memory and faster performance

Considerations:

The model finetuning process may have limitations based on the dataset and hardware used.

Additional Notes

The model supports quick and efficient finetuning through pre-developed notebooks accessible on platforms like Colab and Kaggle.

Input Output

Accepted Modalities:

Text

Performance Tips:

Ensure optimal hardware setup to maximize performance benefits.

LLM Name	Gemma 2B
Repository 🤗	https://huggingface.co/unsloth/gemma-2b
Model Size	2b
Required VRAM	5 GB
Updated	2026-07-30
Maintainer	unsloth
Model Type	gemma
Model Files	5.0 GB
Supported Languages	en
Model Architecture	GemmaForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.44.2
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Rank the Gemma 2B 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

Quantized Models of the Gemma 2B

Model	Likes	Downloads	VRAM
Gemma Han 2B	0	27	5 GB

Best Alternatives to Gemma 2B

Best Alternatives	Context / RAM	Downloads	Likes
Gemma 1.1 2B It	8K / 5.1 GB	209209	173
Gemma 2B It	8K / 5 GB	16802	3
Gemma 1.1 2B It	8K / 5 GB	43	3
Gemma 2B It Peru Legal Es V2	8K / 5.1 GB	8	1
Codegemma 2B	8K / 5.1 GB	33091	101
Bge Reranker V2 Gemma	8K / 10 GB	261121	87
Gita SastraGPT V1	8K / 5.1 GB	4	1
Caca Tinny 2B V3	8K / 5.1 GB	4	1
Pandas Tutor Gemma 2B	8K / 5.1 GB	6	1
Gemma Qlora Customer Support	8K / 5.1 GB	8	0

Note: green Score (e.g. "73.2") means that the model is better than unsloth/gemma-2b.