What are the hardware requirements for Gemma 2 2B It 4bit?

Gemma 2 2B It 4bit requires approximately 1.5 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 2 2B It 4bit and how large is it?

Gemma 2 2B It 4bit is developed by mlx-community, 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 2 2B It 4bit?

Gemma 2 2B It 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 2 2B It 4bit by mlx-community — VRAM 1.5GB, 8K context

Name: Gemma 2 2B It 4bit
Author: mlx-community

Gemma 2 2B It 4bit is an open-source language model by mlx-community. Features: 2b LLM, VRAM: 1.5GB, Context: 8K, License: gemma, Quantized, LLM Explorer Score: 0.15.

4bit Conversational Deploy:azure Endpoints compatible Gemma2 Mlx Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/mlx-community/gemma-2-2b-it-4bit

Gemma 2 2B It 4bit Parameters and Internals

Model Type

text generation, conversational

Additional Notes

Converted to MLX format using mlx-lm version 0.16.1.

LLM Name	Gemma 2 2B It 4bit
Repository 🤗	https://huggingface.co/mlx-community/gemma-2-2b-it-4bit
Model Size	2b
Required VRAM	1.5 GB
Updated	2026-07-27
Maintainer	mlx-community
Model Type	gemma2
Model Files	1.5 GB
Quantization Type	4bit
Model Architecture	Gemma2ForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.42.4
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Rank the Gemma 2 2B It 4bit 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 mlx-community/gemma-2-2b-it-4bit.