What are the hardware requirements for Mixtral 8x7B Yes Instruct LimaRP?

Mixtral 8x7B Yes Instruct LimaRP requires approximately 93.5 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 8x7B Yes Instruct LimaRP and how large is it?

Mixtral 8x7B Yes Instruct LimaRP is developed by antisoc-qa-assoc, 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 Mixtral 8x7B Yes Instruct LimaRP?

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

Mixtral 8x7B Yes Instruct LimaRP by antisoc-qa-assoc — VRAM 93.5GB, 32K context

Name: Mixtral 8x7B Yes Instruct LimaRP
Author: antisoc-qa-assoc

Mixtral 8x7B Yes Instruct LimaRP is an open-source language model by antisoc-qa-assoc. Features: 46.7b LLM, VRAM: 93.5GB, Context: 32K, License: other, MoE, Instruction-Based, LLM Explorer Score: 0.19.

Base model:ds-archive/mixtral-... Base model:finetune:ds-archive... Dataset:lemonilia/limarp En Instruct Mixtral Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/antisoc-qa-assoc/Mixtral-8x7B-Yes-Instruct-LimaRP

Mixtral 8x7B Yes Instruct LimaRP Parameters and Internals

LLM Name	Mixtral 8x7B Yes Instruct LimaRP
Repository 🤗	https://huggingface.co/antisoc-qa-assoc/Mixtral-8x7B-Yes-Instruct-LimaRP
Base Model(s)	mistralai/Mixtral-8x7B-Instruct-v0.1 Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss mistralai/Mixtral-8x7B-Instruct-v0.1 Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss
Model Size	46.7b
Required VRAM	93.5 GB
Updated	2026-08-08
Maintainer	antisoc-qa-assoc
Model Type	mixtral
Instruction-Based	Yes
Model Files	10.0 GB: 1-of-10 10.0 GB: 2-of-10 10.0 GB: 3-of-10 10.0 GB: 4-of-10 10.0 GB: 5-of-10 10.0 GB: 6-of-10 10.0 GB: 7-of-10 10.0 GB: 8-of-10 10.0 GB: 9-of-10 3.5 GB: 10-of-10
Supported Languages	en
Model Architecture	MixtralForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	4.51.3
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Mixtral 8x7B Yes Instruct LimaRP 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

Best Alternatives to Mixtral 8x7B Yes Instruct LimaRP

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral 8x7B Instruct V0.1	32K / 93.6 GB	842893	4720
Mixtral 8x7B Instruct V0.1	32K / 93.6 GB	7	0
Mixtral 8x7B Instruct V0.1 FP8	32K / 47.1 GB	3383	0
Merge Mixtral Prometheus 8x7B	32K / 91.9 GB	12	2
Notux 8x7b V1	32K / 93.6 GB	43	165
BagelMIsteryTour V2 8x7B	32K / 93.5 GB	94	17
...rkrautLM Mixtral 8x7B Instruct	32K / 93.6 GB	84	22
Mixtral 8x7B Instruct V0.1 FP8	32K / 47.1 GB	374	0
Sage Ft Mixtral 8x7b	32K / 90 GB	45	25
Dolphin 2.5 Mixtral 8x7b	32K / 93.6 GB	4125	1235

Note: green Score (e.g. "73.2") means that the model is better than antisoc-qa-assoc/Mixtral-8x7B-Yes-Instruct-LimaRP.