What are the hardware requirements for Gemma3 1B Cpt Final Checkpoint 10000?

Gemma3 1B Cpt Final Checkpoint 10000 requires approximately 5.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma3 1B Cpt Final Checkpoint 10000 and how large is it?

Gemma3 1B Cpt Final Checkpoint 10000 is developed by NTIS, a model with 1b 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 Gemma3 1B Cpt Final Checkpoint 10000?

Gemma3 1B Cpt Final Checkpoint 10000 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma3 1B Cpt Final Checkpoint 10000 by NTIS — VRAM 5.2GB, 32K context

Name: Gemma3 1B Cpt Final Checkpoint 10000
Author: NTIS

Gemma3 1B Cpt Final Checkpoint 10000 is an open-source language model by NTIS. Features: 1b LLM, VRAM: 5.2GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.18.

Autotrain compatible En Endpoints compatible Gemma3 text Ko Pytorch Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/gsjang/gemma3-1b-cpt-final-checkpoint-10000

Gemma3 1B Cpt Final Checkpoint 10000 Parameters and Internals

LLM Name	Gemma3 1B Cpt Final Checkpoint 10000
Repository 🤗	https://huggingface.co/gsjang/gemma3-1b-cpt-final-checkpoint-10000
Model Size	1b
Required VRAM	5.2 GB
Updated	2025-09-23
Maintainer	NTIS
Model Type	gemma3_text
Model Files	4.0 GB: 1-of-2 1.2 GB: 2-of-2 8.0 GB 5.2 GB 0.0 GB 0.0 GB
Supported Languages	ko en
Model Architecture	Gemma3ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.51.3
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	262144
Torch Data Type	float32

Rank the Gemma3 1B Cpt Final Checkpoint 10000 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 gsjang/gemma3-1b-cpt-final-checkpoint-10000.