What are the hardware requirements for Gemma4 Merged K8s?

Gemma4 Merged K8s requires approximately 15.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma4 Merged K8s and how large is it?

Gemma4 Merged K8s is developed by AnveshGummala, a model with 7.9b 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 Gemma4 Merged K8s?

Gemma4 Merged K8s is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma4 Merged K8s by AnveshGummala — VRAM 15.9GB

Name: Gemma4 Merged K8s
Author: AnveshGummala

Gemma4 Merged K8s is an open-source language model by AnveshGummala. Features: 7.9b LLM, VRAM: 15.9GB, Merged.

Merged Model Gemma4 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/AnveshGummala/gemma4-merged-k8s

Gemma4 Merged K8s Parameters and Internals

LLM Name	Gemma4 Merged K8s
Repository 🤗	https://huggingface.co/AnveshGummala/gemma4-merged-k8s
Merged Model	Yes
Model Size	7.9b
Required VRAM	15.9 GB
Updated	2026-09-14
Maintainer	AnveshGummala
Model Type	gemma4
Model Files	5.6 GB: 1-of-7 2.0 GB: 2-of-7 2.0 GB: 3-of-7 2.0 GB: 4-of-7 2.0 GB: 5-of-7 2.0 GB: 6-of-7 0.3 GB: 7-of-7
Model Architecture	Gemma4ForConditionalGeneration
Transformers Version	5.14.1
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Gemma4 Merged K8s 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 Gemma4 Merged K8s

Best Alternatives	Context / RAM	Downloads	Likes
ContextPilot E4B	0K / 15.9 GB	404	5
Firehouse Cactus 1.0	0K / 15.9 GB	736	0
Quatfit Mini	0K / 31.8 GB	1074	7
Gemma 4 E4B It Qat Bf16	0K / 15.9 GB	77	1
... 4 E4B It Qat Q4 0 Unquantized	0K / 15.9 GB	10950	27
... 4 E4B It Qat Q4 0 Unquantized	0K / 15.9 GB	1285	3
...a 4 E4B Abliterated Uncensored	0K / 15.9 GB	279	1
...t Q4 0 Unquantized Abliterated	0K / 15.9 GB	113	7

Note: green Score (e.g. "73.2") means that the model is better than AnveshGummala/gemma4-merged-k8s.