What are the hardware requirements for Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128?

Mixtral 8x7B Instruct V0.1 OmniQuantv1 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.

Where can I download or evaluate Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128?

Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128 by ChenMnZ — VRAM 24.7GB, 32K context

Name: Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128
Author: ChenMnZ

Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128 is an open-source language model by ChenMnZ. Features: 6.5b LLM, VRAM: 24.7GB, Context: 32K, MoE, Instruction-Based, LLM Explorer Score: 0.1.

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

Model Card on HF 🤗: https://huggingface.co/ChenMnZ/Mixtral-8x7B-Instruct-v0.1-OmniQuantv1-w4a16g128

Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128 Parameters and Internals

Additional Notes

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

LLM Name	Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128
Repository 🤗	https://huggingface.co/ChenMnZ/Mixtral-8x7B-Instruct-v0.1-OmniQuantv1-w4a16g128
Model Size	6.5b
Required VRAM	24.7 GB
Updated	2026-08-25
Maintainer	ChenMnZ
Model Type	mixtral
Instruction-Based	Yes
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

Rank the Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128 capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
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Code Generation
Multi-Language Support and Translation

Best Alternatives to Mixtral 8x7B Instruct V0.1 OmniQuantv1 W4a16g128

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...nstruct V0.1 AQLM 2Bit 1x16 Hf	32K / 13.1 GB	15	19

Note: green Score (e.g. "73.2") means that the model is better than ChenMnZ/Mixtral-8x7B-Instruct-v0.1-OmniQuantv1-w4a16g128.