What are the hardware requirements for Mixtral 8x22B V0.1?

Mixtral 8x22B V0.1 requires approximately 212 GB of VRAM and supports a context window of 64K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mixtral 8x22B V0.1 and how large is it?

Mixtral 8x22B V0.1 is developed by mistral-community, a model with 140.6b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Mixtral 8x22B V0.1 perform on standard benchmarks?

Mixtral 8x22B V0.1 has the following published scores: MMLU 77.81. Compare against reference models on this page or on the LLM Explorer leaderboards.

Mixtral 8x22B V0.1 by mistral-community — VRAM 212GB, 64K context

Name: Mixtral 8x22B V0.1
Author: mistral-community

Mixtral 8x22B V0.1 is an open-source language model by mistral-community. Features: 140.6b LLM, VRAM: 212GB, Context: 64K, License: apache-2.0, MoE, HF Score: 74.5, LLM Explorer Score: 0.21, Arc: 70.5, HellaSwag: 88.7, MMLU: 77.8, TruthfulQA: 51.1, WinoGrande: 84.5, GSM8K: 74.2.

De Deploy:azure En Endpoints compatible Es Fr It Mixtral Model-index Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1

Mixtral 8x22B V0.1 Benchmarks

IFEval: 31.67 vs 88 (so35)^-64%

ARC: 70.48 vs 96.7 (so35)^-27.1%

HellaSwag: 88.73 vs 95.3 (gpt4)^-6.9%

MMLU: 77.81 vs 88.3 (so35)^-11.9%

TruthfulQA: 51.08 vs 59 (gpt4)^-13.4%

WinoGrande: 84.53 vs 87.5 (gpt4)^-3.4%

GSM8K: 74.15 vs 96.4 (so35)^-23.1%

MATH Lvl 5: 15.43

LLME Score: 0.20942

^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)

Mixtral 8x22B V0.1 (mistral-community/Mixtral-8x22B-v0.1)

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Mixtral 8x22B V0.1 Parameters and Internals

Model Type

text-generation

Additional Notes

Mixtral-8x22B is a pretrained generative Sparse Mixture of Experts LLM.

LLM Name	Mixtral 8x22B V0.1
Repository 🤗	https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1
Model Size	140.6b
Required VRAM	212 GB
Updated	2026-04-25
Maintainer	mistral-community
Model Type	mixtral
Model Files	5.0 GB: 1-of-59 4.8 GB: 2-of-59 4.8 GB: 3-of-59 4.8 GB: 4-of-59 4.8 GB: 5-of-59 4.8 GB: 6-of-59 4.8 GB: 7-of-59 4.8 GB: 8-of-59 4.8 GB: 9-of-59 4.8 GB: 10-of-59 4.8 GB: 11-of-59 4.8 GB: 12-of-59 4.8 GB: 13-of-59 4.8 GB: 14-of-59 4.8 GB: 15-of-59 4.8 GB: 16-of-59 4.8 GB: 17-of-59 4.8 GB: 18-of-59 4.8 GB: 19-of-59 4.8 GB: 20-of-59 4.8 GB: 21-of-59 4.8 GB: 22-of-59 4.8 GB: 23-of-59 4.9 GB: 24-of-59 5.0 GB: 25-of-59 5.0 GB: 26-of-59 4.9 GB: 27-of-59 4.8 GB: 28-of-59 4.8 GB: 29-of-59 4.8 GB: 30-of-59 4.8 GB: 31-of-59 4.8 GB: 32-of-59 4.8 GB: 33-of-59 4.8 GB: 34-of-59 4.8 GB: 35-of-59 4.8 GB: 36-of-59 4.8 GB: 37-of-59 4.8 GB: 38-of-59 4.8 GB: 39-of-59 4.8 GB: 40-of-59 4.8 GB: 41-of-59 4.8 GB: 42-of-59 4.8 GB: 43-of-59 4.8 GB: 44-of-59
Supported Languages	fr it de es en
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	65536
Model Max Length	65536
Transformers Version	4.40.0.dev0
Vocabulary Size	32000
Torch Data Type	bfloat16

Quantized Models of the Mixtral 8x22B V0.1

Model	Likes	Downloads	VRAM
...2 Mixtral 8x22b 6.0bpw H8 EXL2	1	2	105 GB
...2 Mixtral 8x22b 8.0bpw H8 EXL2	2	4	125 GB
...ephyr Orpo 141B A35b V0.1 GGUF	0	407	4 GB
WizardLM2 2bit	0	155	4 GB
...8x22b Instruct Oh EXL2 2.25bpw	1	6	40 GB

Best Alternatives to Mixtral 8x22B V0.1

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral 8x22B Instruct V0.1	64K / 221.4 GB	27071	748
Zephyr Orpo 141B A35b V0.1	64K / 207.2 GB	91	269
WizardLM 2 8x22B	64K / 216.8 GB	9728	411
Mixtral 8x22B V0.1	64K / 221.6 GB	6061	236
Mixtral 8x22B V0.3	64K / 221.4 GB	62	3
XLAM 8x22b R	64K / 211.8 GB	12026	45
...ixtral 8x22B Instruct V0.1 FP8	64K / 140.9 GB	548	0
Dolphin 2.9.2 Mixtral 8x22b	64K / 207.2 GB	9311	40
Dolphin 2.9.2 Mixtral 8x22b	64K / 207.2 GB	7779	43
...igHuggyD Grey WizardLM 2 8x22B	64K / 216.6 GB	6	4

Note: green Score (e.g. "73.2") means that the model is better than mistral-community/Mixtral-8x22B-v0.1.

Rank the Mixtral 8x22B V0.1 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Release v20260328a

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Mixtral 8x22B V0.1 by mistral-community