SAVRN
Search Contact SAVRN

Open-weight model · Any to any

gemma-4-E4B-it-MLX-6bit

by LM Studio Community lmstudio-community/gemma-4-E4B-it-MLX-6bit

LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord. 6-bit quantized version of gemma-4-E4B-it using MLX, optimized for Apple Silicon.

Parameters8B
Context131,072
Weights7.9 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads1.1M

Runs On

What it takes to serve gemma-4-E4B-it-MLX-6bit (8B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 16.0 GB 19.2 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 8.0 GB 9.6 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 4.0 GB 4.8 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By LM Studio Community, published under apache-2.0, revision 7af9c68c69b0.

LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord. 6-bit quantized version of gemma-4-E4B-it using MLX, optimized for Apple Silicon. Special thanks to the Apple Machine Learning Research team for creating MLX. LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful…

Read LM Studio Community's full model card

Community Model> gemma-4-E4B-it by google

LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord.

Model creator: google
Original model: gemma-4-E4B-it
MLX quantization: provided by LM Studio team using mlx_vlm

Technical Details

6-bit quantized version of gemma-4-E4B-it using MLX, optimized for Apple Silicon.

Special thanks

Special thanks to theApple Machine Learning Research team for creating MLX.

Disclaimers

LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

Configuration

Architecture
Gemma4ForConditionalGeneration
Context length (tokens)
131,072
Layers
42
Hidden size
2,560
Feed-forward size
10,240
Attention heads
8
Key/value heads
2
Head dimension
256
Vocabulary size
262,144
Sliding window (tokens)
512
Model type
gemma4

Identity and Version

Repository
lmstudio-community/gemma-4-E4B-it-MLX-6bit
Publisher
LM Studio Community
Task
Any to any
Modality
Multimodal
Library
transformers
Parameters
8B parameters
Languages
mlx
Revision
7af9c68c69b0679c3908a317f022fd26a49e90fd
First published
2026-04-03
Last updated
2026-07-23

Files and Weights

11 files, 7.9 GB in total. The weights are 2 files totalling 7.9 GB in safetensors.

Weights2 files · 7.9 GB
Configuration4 files · 333.0 KB
Tokenizer2 files · 32.2 MB
Documentation1 file · 2.3 KB
Other1 file · 16.3 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00002.safetensorsWeights4.6 GB bdadbe10f11e
model-00002-of-00002.safetensorsWeights3.3 GB 058e708aa309
config.jsonConfiguration36.5 KB
generation_config.jsonConfiguration208 B
model.safetensors.index.jsonConfiguration295.4 KB
processor_config.jsonConfiguration902 B
README.mdDocumentation2.3 KB
chat_template.jinjaOther16.3 KB
.gitattributesRepository1.6 KB
tokenizer.jsonTokenizer32.2 MB cc8d3a0ce364
tokenizer_config.jsonTokenizer21.7 KB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
7.9 GB
Download from LM Studio Community

Released by LM Studio Community through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published7.9 GB
16-bit16.0 GB
8-bit8.0 GB
4-bit4.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About gemma-4-E4B-it-MLX-6bit

How much GPU memory does gemma-4-E4B-it-MLX-6bit need?

About 19.2 GB at 16-bit and 4.8 GB at 4-bit: the weights (8B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run gemma-4-E4B-it-MLX-6bit on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use gemma-4-E4B-it-MLX-6bit commercially?

Yes. gemma-4-E4B-it-MLX-6bit is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is gemma-4-E4B-it-MLX-6bit's context length?

131,072 tokens, from the maximum position embeddings in its published configuration.

Similar Models

Model · Any to any

gemma-4-E4B-it

Google

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…

Open weights apache-2.0 8B parameters 131,072 tokens transformers

LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord. 4-bit quantized version of gemma-4-E4B-it using MLX, optimized for Apple Silicon. Special thanks to the Apple Machine Learning Research team for creating MLX. LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful…

Open weights apache-2.0 8B parameters 131,072 tokens transformers

LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord. 8-bit quantized version of gemma-4-E4B-it using MLX, optimized for Apple Silicon. Special thanks to the Apple Machine Learning Research team for creating MLX. LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful…

Open weights apache-2.0 8B parameters 131,072 tokens transformers

LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord. 5-bit quantized version of gemma-4-E4B-it using MLX, optimized for Apple Silicon. Special thanks to the Apple Machine Learning Research team for creating MLX. LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful…

Open weights apache-2.0 8B parameters 131,072 tokens transformers

Model · Any to any

gemma-4-E4B

Google

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…

Open weights apache-2.0 8B parameters 131,072 tokens transformers

Creating these models takes significant time, work and compute. If you find them useful consider supporting me: Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs. attn.oproj Lower refusals indicate fewer content restrictions, while lower KL divergence indicates more closeness to the original model's baseline. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections. PIQA (Physical Intuition Question Answering) a ~1,800 questions tests common-sense understanding of how the physical world works with benchmark…

Open weights apache-2.0 8B parameters 131,072 tokens transformers