What are the hardware requirements for Ko Gemma 2 9B It?

Ko Gemma 2 9B It requires approximately 18.6 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 Ko Gemma 2 9B It and how large is it?

Ko Gemma 2 9B It is developed by rtzr, a model with 9b 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 Ko Gemma 2 9B It?

Ko Gemma 2 9B It is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Ko Gemma 2 9B It by rtzr — VRAM 18.6GB, 8K context

Name: Ko Gemma 2 9B It
Author: rtzr

Ko Gemma 2 9B It is an open-source language model by rtzr. Features: 9b LLM, VRAM: 18.6GB, Context: 8K, License: gemma, LLM Explorer Score: 0.15.

Arxiv:2305.18290 Base model:finetune:google/gem... Base model:google/gemma-2-9b Conversational Endpoints compatible Gemma2 Ko Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/rtzr/ko-gemma-2-9b-it

Ko Gemma 2 9B It Parameters and Internals

Model Type

text-to-text, decoder-only, large language model

Use Cases

Areas:

text generation, conversational AI, summarization

Applications:

research, commercial applications

Primary Use Cases:

Korean-language conversational tasks

Limitations:

unsuitable for non-Korean tasks

Considerations:

Ensure cultural and contextual understanding when using the model.

Additional Notes

Integrates state-of-the-art techniques and datasets for high proficiency in Korean-language text tasks.

Supported Languages

ko (proficient)

Training Details

Data Sources:

Orca-Math, dpo-mix-7k

Methodology:

Supervised Fine-Tuning, Direct Preference Optimization

Model Architecture:

decoder-only

Safety Evaluation

Risk Categories:

misinformation, bias

Input Output

Input Format:

Text string (question, prompt, or document)

Accepted Modalities:

text

Output Format:

Generated Korean-language text

Performance Tips:

Improve outputs using specified temperature and top_p values.

LLM Name	Ko Gemma 2 9B It
Repository 🤗	https://huggingface.co/rtzr/ko-gemma-2-9b-it
Base Model(s)	Gemma 2 9B google/gemma-2-9b
Model Size	9b
Required VRAM	18.6 GB
Updated	2026-07-23
Maintainer	rtzr
Model Type	gemma2
Model Files	1.9 GB: 1-of-10 2.0 GB: 2-of-10 2.0 GB: 3-of-10 2.0 GB: 4-of-10 2.0 GB: 5-of-10 2.0 GB: 6-of-10 2.0 GB: 7-of-10 2.0 GB: 8-of-10 2.0 GB: 9-of-10 0.7 GB: 10-of-10
Supported Languages	ko
Model Architecture	Gemma2ForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.42.3
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Rank the Ko Gemma 2 9B It 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

Best Alternatives to Ko Gemma 2 9B It

Best Alternatives	Context / RAM	Downloads	Likes
G2 GSHT 32K	32K / 20.4 GB	8	1
SystemGemma2 9B It	32K / 18.6 GB	5	2
Gemma 2 9B It	8K / 18.6 GB	80757	11
GWQ 9B Preview2	8K / 18.6 GB	11	10
Gemma 2 9B It	8K / 18.6 GB	6	0
Gemma 2 9B It SimPO	8K / 18.6 GB	473	173
Gemma 2 9B It	8K / 18.6 GB	323635	837
Gemma 2 9B It Blend Rebase	8K / 20.4 GB	5	0
Gemma 2 9B JEE Socratic Final	8K / 18.6 GB	1118	0
Gemma 2 9B	8K / 37.1 GB	95888	717

Note: green Score (e.g. "73.2") means that the model is better than rtzr/ko-gemma-2-9b-it.