What are the hardware requirements for Gemma4 1 0 8 Sft 16bit Vllm?

Gemma4 1 0 8 Sft 16bit Vllm requires approximately 16 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma4 1 0 8 Sft 16bit Vllm and how large is it?

Gemma4 1 0 8 Sft 16bit Vllm is developed by didula-wso2, a model with 8b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gemma4 1 0 8 Sft 16bit Vllm perform on standard benchmarks?

Gemma4 1 0 8 Sft 16bit Vllm has the following published scores: LMSYS ELO 1318. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gemma4 1 0 8 Sft 16bit Vllm by didula-wso2 — VRAM 16GB

Name: Gemma4 1 0 8 Sft 16bit Vllm
Author: didula-wso2

Gemma4 1 0 8 Sft 16bit Vllm is an open-source language model by didula-wso2. Features: 8b LLM, VRAM: 16GB, Quantized, LLM Explorer Score: 0.38, ELO: 1318.

16bit Ballerina Base model:finetune:unsloth/ge... Base model:unsloth/gemma-4-e4b... Conversational Endpoints compatible Gemma-4 Gemma4 Image-text-to-text Quantized Region:us Safetensors Sft Unsloth

Model Card on HF 🤗: https://huggingface.co/didula-wso2/gemma4_1-0-8_sft_16bit_vllm

Gemma4 1 0 8 Sft 16bit Vllm Benchmarks

LMSys ELO 1318 vs 1272 (so35) 3.6%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Gemma4 1 0 8 Sft 16bit Vllm Parameters and Internals

LLM Name	Gemma4 1 0 8 Sft 16bit Vllm
Repository 🤗	https://huggingface.co/didula-wso2/gemma4_1-0-8_sft_16bit_vllm
Base Model(s)	Gemma 4 E4B It unsloth/gemma-4-E4B-it
Model Size	8b
Required VRAM	16 GB
Updated	2026-09-18
Maintainer	didula-wso2
Model Type	gemma4
Model Files	16.0 GB
Quantization Type	16bit
Model Architecture	Gemma4ForConditionalGeneration
Model Max Length	131072
Transformers Version	5.5.0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Gemma4 1 0 8 Sft 16bit Vllm 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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...e 4.6 Opus Reasoning Distilled	0K / 1 GB	1539	19

Note: green Score (e.g. "73.2") means that the model is better than didula-wso2/gemma4_1-0-8_sft_16bit_vllm.