What are the hardware requirements for WizardLM 2 8x22B Beige 4.0bpw H6 EXL2?

WizardLM 2 8x22B Beige 4.0bpw H6 EXL2 requires approximately 70.8 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 WizardLM 2 8x22B Beige 4.0bpw H6 EXL2 and how large is it?

WizardLM 2 8x22B Beige 4.0bpw H6 EXL2 is developed by LoneStriker. 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 WizardLM 2 8x22B Beige 4.0bpw H6 EXL2?

WizardLM 2 8x22B Beige 4.0bpw H6 EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

WizardLM 2 8x22B Beige 4.0bpw H6 EXL2 by LoneStriker — VRAM 70.8GB, 64K context

Name: WizardLM 2 8x22B Beige 4.0bpw H6 EXL2
Author: LoneStriker

WizardLM 2 8x22B Beige 4.0bpw H6 EXL2 is an open-source language model by LoneStriker. Features: LLM, VRAM: 70.8GB, Context: 64K, MoE, Quantized, Instruction-Based, Merged, LLM Explorer Score: 0.13.

Merged Model Arxiv:2403.19522 4-bit Base model:alpindale/wizardlm-... Base model:fireworks-ai/mixtra... Base model:migtissera/tess-2.0... Base model:openbmb/eurux-8x22b... Conversational Endpoints compatible Exl2 Instruct Mixtral Moe Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/LoneStriker/WizardLM-2-8x22B-Beige-4.0bpw-h6-exl2

WizardLM 2 8x22B Beige 4.0bpw H6 EXL2 Parameters and Internals

Model Type

merged model

Additional Notes

This model is an attempt to tone down the purple prose of WizardLM2-8X22B.

Training Details

Methodology:

Model was merged using the Model Stock merge method.

LLM Name	WizardLM 2 8x22B Beige 4.0bpw H6 EXL2
Repository 🤗	https://huggingface.co/LoneStriker/WizardLM-2-8x22B-Beige-4.0bpw-h6-exl2
Base Model(s)	Eurux 8x22b Nca WizardLM 2 8x22B Mixtral 8x22b Instruct Oh migtissera/Tess-2.0-Mixtral-8x22B openbmb/Eurux-8x22b-nca alpindale/WizardLM-2-8x22B fireworks-ai/mixtral-8x22b-instruct-oh migtissera/Tess-2.0-Mixtral-8x22B
Merged Model	Yes
Required VRAM	70.8 GB
Updated	2026-05-25
Maintainer	LoneStriker
Model Type	mixtral
Instruction-Based	Yes
Model Files	8.6 GB: 1-of-9 8.6 GB: 2-of-9 8.6 GB: 3-of-9 8.6 GB: 4-of-9 8.6 GB: 5-of-9 8.6 GB: 6-of-9 8.6 GB: 7-of-9 8.6 GB: 8-of-9 2.0 GB: 9-of-9
Quantization Type	exl2
Model Architecture	MixtralForCausalLM
Context Length	65536
Model Max Length	65536
Transformers Version	4.41.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the WizardLM 2 8x22B Beige 4.0bpw H6 EXL2 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 WizardLM 2 8x22B Beige 4.0bpw H6 EXL2

Best Alternatives	Context / RAM	Downloads	Likes
...M 2 8x22B Beige 5.0bpw H6 EXL2	64K / 88.5 GB	8	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
...B Instruct V0.1 8.0bpw H8 EXL2	64K / 120.2 GB	6	1
...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
... 4bit MoE 2bit Metaoffload HQQ	32K / 18.3 GB	32	16

Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/WizardLM-2-8x22B-Beige-4.0bpw-h6-exl2.