What are the hardware requirements for Synatra Mixtral 8x7B AWQ?

Synatra Mixtral 8x7B AWQ requires approximately 24.7 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 Synatra Mixtral 8x7B AWQ and how large is it?

Synatra Mixtral 8x7B AWQ is developed by LoneStriker, 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.

Where can I download or evaluate Synatra Mixtral 8x7B AWQ?

Synatra Mixtral 8x7B AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Synatra Mixtral 8x7B AWQ by LoneStriker — VRAM 24.7GB, 32K context

Name: Synatra Mixtral 8x7B AWQ
Author: LoneStriker

Synatra Mixtral 8x7B AWQ is an open-source language model by LoneStriker. Features: 46.7b LLM, VRAM: 24.7GB, Context: 32K, License: apache-2.0, MoE, Quantized, LLM Explorer Score: 0.1.

4-bit Awq Conversational En Endpoints compatible Ko Mixtral Moe Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/LoneStriker/Synatra-Mixtral-8x7B-AWQ

Synatra Mixtral 8x7B AWQ Parameters and Internals

Additional Notes

The model showcases superior comprehension and inference capabilities.

Supported Languages

ko (unknown), en (unknown)

Training Details

Methodology:

fine-tuned version using Korean datasets

Hardware Used:

A100 80GB * 6

Input Output

Input Format:

Alpaca format: '### Instruction: {input} ### Response: {output}'

LLM Name	Synatra Mixtral 8x7B AWQ
Repository 🤗	https://huggingface.co/LoneStriker/Synatra-Mixtral-8x7B-AWQ
Model Size	46.7b
Required VRAM	24.7 GB
Updated	2026-05-28
Maintainer	LoneStriker
Model Type	mixtral
Model Files	10.0 GB: 1-of-3 10.0 GB: 2-of-3 4.7 GB: 3-of-3
Supported Languages	ko en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.1
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Synatra Mixtral 8x7B 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 Synatra Mixtral 8x7B AWQ

Best Alternatives	Context / RAM	Downloads	Likes
...kaLM Mixtral 8x7B V0.2 DPO AWQ	32K / 24.7 GB	6	0
Mixtral 8x7B Instruct V0.1 AWQ	32K / 24.7 GB	5	0
Karakuri Lm 8x7b Chat V0.1 AWQ	32K / 24.7 GB	7	0
Dolphin 2.7 Mixtral 8x7b AWQ	32K / 24.7 GB	2550	23
Mixtral Instruct AWQ	32K / 24.7 GB	2333	43
Functionary Medium V2.4 AWQ	32K / 24.7 GB	5	3
Mixtral 8x7B Instruct V0.1 AWQ	32K / 24.7 GB	1045	59
Mixtral 8x7B Instruct V0.1 AWQ	32K / 24.7 GB	1248	16
H2ogpt Mixtral 8x7b 32K AWQ	32K / 24.7 GB	6	0
...xtral Instruct AWQ Clone Dec23	32K / 24.7 GB	9	0

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