What are the hardware requirements for Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ?

Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ requires approximately 18.2 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 Hf Attn 4bit MoE 2bit HQQ?

Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ 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 Hf Attn 4bit MoE 2bit HQQ by mobiuslabsgmbh — VRAM 18.2GB, 32K context

Name: Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ
Author: mobiuslabsgmbh

Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ is an open-source language model by mobiuslabsgmbh. Features: LLM, VRAM: 18.2GB, Context: 32K, License: apache-2.0, MoE, Quantized, Instruction-Based, LLM Explorer Score: 0.1.

4bit Conversational Instruct Mixtral Moe Quantized Region:us

Model Card on HF 🤗: https://huggingface.co/mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-HQQ

Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ Parameters and Internals

Model Type

text-generation

Additional Notes

This version of the model is quantized using Half-Quadratic Quantization (HQQ) with attention layers at 4-bit and experts at 2-bit.

Input Output

Input Format:

String prompt

Accepted Modalities:

text

Output Format:

Generated text

LLM Name	Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ
Repository 🤗	https://huggingface.co/mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-HQQ
Required VRAM	18.2 GB
Updated	2026-08-03
Maintainer	mobiuslabsgmbh
Model Type	mixtral
Instruction-Based	Yes
Model Files	18.2 GB
Quantization Type	4bit
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.1
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ 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 Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ

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...M 2 8x22B Beige 5.0bpw H6 EXL2	64K / 88.5 GB	8	0
...M 2 8x22B Beige 4.0bpw H6 EXL2	64K / 70.8 GB	6	0
...M 2 8x22B Beige 2.4bpw H6 EXL2	64K / 42.7 GB	5	0
...M 2 8x22B Beige 3.0bpw H6 EXL2	64K / 53.2 GB	5	0
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...8x22b Instruct Oh EXL2 2.25bpw	64K / 40.1 GB	5	1
...eryTour V2 8x7B 4.5bpw H6 EXL2	32K / 26.5 GB	5	2
...it MoE 2bitgs8 Metaoffload HQQ	32K / 24.1 GB	9	19
...hin 2.7 Mixtral 8x7b 8bpw EXL2	32K / 46.8 GB	5	2
... 4bit MoE 3bit Metaoffload HQQ	32K / 22.4 GB	12	13

Note: green Score (e.g. "73.2") means that the model is better than mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-HQQ.