What are the hardware requirements for Mixtral GQA 400M V4.4096?

Mixtral GQA 400M V4.4096 requires approximately 8.1 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mixtral GQA 400M V4.4096 and how large is it?

Mixtral GQA 400M V4.4096 is developed by BEE-spoke-data, a model with 400m 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 Mixtral GQA 400M V4.4096?

Mixtral GQA 400M V4.4096 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mixtral GQA 400M V4.4096 by BEE-spoke-data — VRAM 8.1GB, 4K context

Name: Mixtral GQA 400M V4.4096
Author: BEE-spoke-data

Mixtral GQA 400M V4.4096 is an open-source language model by BEE-spoke-data. Features: 400m LLM, VRAM: 8.1GB, Context: 4K, LLM Explorer Score: 0.1.

Conversational Endpoints compatible Mixtral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/BEE-spoke-data/Mixtral-GQA-400m-v4-4096

Mixtral GQA 400M V4.4096 Parameters and Internals

LLM Name	Mixtral GQA 400M V4.4096
Repository 🤗	https://huggingface.co/BEE-spoke-data/Mixtral-GQA-400m-v4-4096
Model Size	400m
Required VRAM	8.1 GB
Updated	2026-08-13
Maintainer	BEE-spoke-data
Model Type	mixtral
Model Files	5.0 GB: 1-of-2 3.1 GB: 2-of-2 0.0 GB
Model Architecture	MixtralForCausalLM
Context Length	4096
Model Max Length	4096
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float32

Rank the Mixtral GQA 400M V4.4096 capabilities

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Data Analysis and Insight Generation
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Multi-Language Support and Translation

Best Alternatives to Mixtral GQA 400M V4.4096

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral GQA 400M V2	4K / 4.3 GB	45	1
Mixtral Smol 400M	4K / 25.1 GB	11	0

Note: green Score (e.g. "73.2") means that the model is better than BEE-spoke-data/Mixtral-GQA-400m-v4-4096.