What are the hardware requirements for Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128?

Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128 requires approximately 24.7 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 Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128 and how large is it?

Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128 is developed by ChenMnZ, a model with 6.5b 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 8x7B V0.1 OmniQuantv2 W4a16g128?

Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128 by ChenMnZ — VRAM 24.7GB, 32K context

Name: Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128
Author: ChenMnZ

Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128 is an open-source language model by ChenMnZ. Features: 6.5b LLM, VRAM: 24.7GB, Context: 32K, MoE, LLM Explorer Score: 0.1.

Arxiv:2308.13137 Endpoints compatible Mixtral Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/ChenMnZ/Mixtral-8x7B-v0.1-OmniQuantv2-w4a16g128

Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128 Parameters and Internals

Additional Notes

For detailed usage, refer to the corresponding Jupyter notebook available in the repository.

LLM Name	Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128
Repository 🤗	https://huggingface.co/ChenMnZ/Mixtral-8x7B-v0.1-OmniQuantv2-w4a16g128
Model Size	6.5b
Required VRAM	24.7 GB
Updated	2026-08-25
Maintainer	ChenMnZ
Model Type	mixtral
Model Files	5.0 GB: 1-of-5 5.0 GB: 2-of-5 5.0 GB: 3-of-5 5.0 GB: 4-of-5 4.7 GB: 5-of-5
Model Architecture	MixtralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

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Best Alternatives to Mixtral 8x7B V0.1 OmniQuantv2 W4a16g128

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Mixtral Debug	32K / 0 GB	6	0
Mixtral Debug	32K / 0 GB	5	0
...uct V0.1 OmniQuantv1 W4a16g128	32K / 24.7 GB	12	1
...nstruct V0.1 AQLM 2Bit 1x16 Hf	32K / 13.1 GB	15	19
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...QLM 2Bit 1x16 Hf Test Dispatch	32K / 13.1 GB	35	3
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Note: green Score (e.g. "73.2") means that the model is better than ChenMnZ/Mixtral-8x7B-v0.1-OmniQuantv2-w4a16g128.