What are the hardware requirements for SauerkrautLM Mixtral 8x7B?

SauerkrautLM Mixtral 8x7B requires approximately 93.6 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 SauerkrautLM Mixtral 8x7B and how large is it?

SauerkrautLM Mixtral 8x7B is developed by VAGOsolutions, a model with 46.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does SauerkrautLM Mixtral 8x7B perform on standard benchmarks?

SauerkrautLM Mixtral 8x7B has the following published scores: MMLU 66.69. Compare against reference models on this page or on the LLM Explorer leaderboards.

SauerkrautLM Mixtral 8x7B by VAGOsolutions — VRAM 93.6GB, 32K context

Name: SauerkrautLM Mixtral 8x7B
Author: VAGOsolutions

SauerkrautLM Mixtral 8x7B is an open-source language model by VAGOsolutions. Features: 46.7b LLM, VRAM: 93.6GB, Context: 32K, License: apache-2.0, MoE, LLM Explorer Score: 0.12, Arc: 68.9, HellaSwag: 86, MMLU: 66.7, GSM8K: 47.5.

Augmentation Chatml Dataset:argilla/distilabel-mat... Dataset:open-orca/slimorca De Dpo En Endpoints compatible Es Finetuned Fr German It Mistral Mixtral Moe Region:us Safetensors Sft Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/VAGOsolutions/SauerkrautLM-Mixtral-8x7B

SauerkrautLM Mixtral 8x7B Benchmarks

ARC: 68.86 vs 96.7 (so35)^-28.8%

HellaSwag: 86.01 vs 95.3 (gpt4)^-9.7%

MMLU: 66.69 vs 88.3 (so35)^-24.5%

GSM8K: 47.54 vs 96.4 (so35)^-50.7%

LLME Score: 0.12205

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

SauerkrautLM Mixtral 8x7B (VAGOsolutions/SauerkrautLM-Mixtral-8x7B)

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SauerkrautLM Mixtral 8x7B Parameters and Internals

Model Type

Mixture of Experts (MoE)

Additional Notes

Evaluated with lm-evaluation-harness v0.3.0, concerned with data contamination tests showing results below 0.1%. Licensing explains that models may be used for commercial purposes.

Supported Languages

en (English), de (German), fr (French), it (Italian), es (Spanish)

Training Details

Data Sources:

Open-Orca/SlimOrca, argilla/distilabel-math-preference-dpo, SauerkrautLM-DPO dataset, Sauerkraut-7b-HerO, HuggingFaceH4/ultrafeedback_binarized

Methodology:

SFT and DPO alignment with German data augmentation

LLM Name	SauerkrautLM Mixtral 8x7B
Repository 🤗	https://huggingface.co/VAGOsolutions/SauerkrautLM-Mixtral-8x7B
Model Size	46.7b
Required VRAM	93.6 GB
Updated	2026-05-21
Maintainer	VAGOsolutions
Model Type	mixtral
Model Files	4.9 GB: 1-of-19 5.0 GB: 2-of-19 5.0 GB: 3-of-19 4.9 GB: 4-of-19 5.0 GB: 5-of-19 5.0 GB: 6-of-19 4.9 GB: 7-of-19 5.0 GB: 8-of-19 5.0 GB: 9-of-19 4.9 GB: 10-of-19 5.0 GB: 11-of-19 5.0 GB: 12-of-19 5.0 GB: 13-of-19 4.9 GB: 14-of-19 5.0 GB: 15-of-19 5.0 GB: 16-of-19 4.9 GB: 17-of-19 5.0 GB: 18-of-19 4.2 GB: 19-of-19
Supported Languages	en de fr it es
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32002
Torch Data Type	bfloat16

Quantized Models of the SauerkrautLM Mixtral 8x7B

Model	Likes	Downloads	VRAM
SauerkrautLM Mixtral 8x7B AWQ	1	7	27 GB
SauerkrautLM Mixtral 8x7B GGUF	9	198	15 GB
SauerkrautLM Mixtral 8x7B AWQ	3	15	24 GB
SauerkrautLM Mixtral 8x7B GPTQ	2	15	23 GB

Best Alternatives to SauerkrautLM Mixtral 8x7B

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral 8x7B Instruct V0.1	32K / 93.6 GB	893197	4683
Nous Hermes 2 Mixtral 8x7B DPO	32K / 93.6 GB	9047	453
Mixtral 8x7B V0.1	32K / 93.6 GB	112078	1808
Sensualize Mixtral Bf16	32K / 93.6 GB	0	0
Skadi Mixtral V1	32K / 93.5 GB	0	0
Franziska Mixtral V1	32K / 93.5 GB	0	0
Typhon Mixtral V1	32K / 93.4 GB	0	0
Mixtral 8x7B Instruct V0.1 FP8	32K / 47.1 GB	112011	0
GritLM 8x7B KTO	32K / 93.6 GB	7952	3
Smaug Mixtral V0.1	32K / 187.7 GB	8548	12

Note: green Score (e.g. "73.2") means that the model is better than VAGOsolutions/SauerkrautLM-Mixtral-8x7B.

Rank the SauerkrautLM Mixtral 8x7B 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
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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SauerkrautLM Mixtral 8x7B by VAGOsolutions