What are the hardware requirements for Mixtral Fusion 4x7B Instruct V0.1 AWQ?

Mixtral Fusion 4x7B Instruct V0.1 AWQ requires approximately 13 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 Mixtral Fusion 4x7B Instruct V0.1 AWQ and how large is it?

Mixtral Fusion 4x7B Instruct V0.1 AWQ is developed by TheBloke, a model with 24.2b parameters. 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 Mixtral Fusion 4x7B Instruct V0.1 AWQ?

Mixtral Fusion 4x7B Instruct V0.1 AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mixtral Fusion 4x7B Instruct V0.1 AWQ by TheBloke — VRAM 13GB, 32K context

Name: Mixtral Fusion 4x7B Instruct V0.1 AWQ
Author: TheBloke

Mixtral Fusion 4x7B Instruct V0.1 AWQ is an open-source language model by TheBloke. Features: 24.2b LLM, VRAM: 13GB, Context: 32K, License: apache-2.0, MoE, Quantized, Instruction-Based, LLM Explorer Score: 0.1.

4-bit Awq Base model:mmnga/mixtral-fusio... Base model:quantized:mmnga/mix... Conversational De En Es Fr Instruct It Mixtral Moe Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-AWQ

Mixtral Fusion 4x7B Instruct V0.1 AWQ Parameters and Internals

Model Type

mixtral

Additional Notes

This model utilizes the AWQ quantization method which offers efficient, accurate and fast low-bit weight quantization.

Supported Languages

fr (unknown), it (unknown), de (unknown), es (unknown), en (unknown)

Input Output

Input Format:

[INST] {prompt} [/INST]

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Utilize AutoAWQ for efficient inference.

LLM Name	Mixtral Fusion 4x7B Instruct V0.1 AWQ
Repository 🤗	https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-AWQ
Model Name	Mixtral Fusion 4X7B Instruct v0.1
Model Creator	momonga
Base Model(s)	mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1 mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1
Model Size	24.2b
Required VRAM	13 GB
Updated	2026-08-03
Maintainer	TheBloke
Model Type	mixtral
Instruction-Based	Yes
Model Files	10.0 GB: 1-of-2 3.0 GB: 2-of-2
Supported Languages	fr it de es en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the Mixtral Fusion 4x7B Instruct V0.1 AWQ 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 Fusion 4x7B Instruct V0.1 AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Proto Athena 4x7B	32K / 48.4 GB	5	0
Proto Athena V0.2 4x7B	32K / 48.4 GB	8	0
...erges MoE 4x7b V10 Mixtralv0.3	32K / 48.3 GB	6	0
Sixtyoneeighty 4x7B V1	32K / 48.3 GB	62	0
MixtureofMerges MoE 4x7bRP V11	32K / 48.3 GB	4	1
West Ramen 7Bx4	32K / 48.7 GB	53	0
Eclipse Mistral 4x7b	32K / 48.5 GB	16	1
MoE Merging	32K / 48.3 GB	70	0
Boundary Mistral 4x7b MoE	32K / 48.7 GB	117	1
...tral Fusion 4x7B Instruct V0.1	32K / 48.5 GB	8	18

Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-AWQ.