What are the hardware requirements for Cerebrum 1.0 8x7b?

Cerebrum 1.0 8x7b requires approximately 93.6 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 Cerebrum 1.0 8x7b and how large is it?

Cerebrum 1.0 8x7b is developed by AetherResearch, 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.

How does Cerebrum 1.0 8x7b perform on standard benchmarks?

Cerebrum 1.0 8x7b has the following published scores: MMLU 72.45. Compare against reference models on this page or on the LLM Explorer leaderboards.

Cerebrum 1.0 8x7b by AetherResearch — VRAM 93.6GB, 32K context

Name: Cerebrum 1.0 8x7b
Rating: 3.75 (8 reviews)
Author: AetherResearch

Cerebrum 1.0 8x7b is an open-source language model by AetherResearch. Features: 46.7b LLM, VRAM: 93.6GB, Context: 32K, License: apache-2.0, MoE, LLM Explorer Score: 0.13, Arc: 68.1, HellaSwag: 87.3, MMLU: 72.5, GSM8K: 61.9.

Base model:finetune:mistralai/... Base model:mistralai/mixtral-8... Conversational Endpoints compatible Mixtral Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/AetherResearch/Cerebrum-1.0-8x7b

Cerebrum 1.0 8x7b Parameters and Internals

Model Type

reasoning

Use Cases

Areas:

reasoning, brainstorming, knowledge intensive, creative tasks

Primary Use Cases:

tasks that require reasoning

Training Details

Data Sources:

native chain of thought data

Methodology:

fine-tuning on a small custom dataset and targeted RLHF

Input Output

Input Format:

Alpaca-style template

Accepted Modalities:

text

Performance Tips:

Can be operated at very low temperatures for precise answers.

LLM Name	Cerebrum 1.0 8x7b
Repository 🤗	https://huggingface.co/AetherResearch/Cerebrum-1.0-8x7b
Base Model(s)	mistralai/Mixtral-8x7B-v0.1 mistralai/Mixtral-8x7B-v0.1
Model Size	46.7b
Required VRAM	93.6 GB
Updated	2026-10-21
Maintainer	AetherResearch
Model Type	mixtral
Model Files	4.9 GB: 1-of-19 5.0 GB: 2-of-19 5.0 GB: 3-of-19 4.9 GB: 4-of-19 5.0 GB: 5-of-19 5.0 GB: 6-of-19 4.9 GB: 7-of-19 5.0 GB: 8-of-19 5.0 GB: 9-of-19 4.9 GB: 10-of-19 5.0 GB: 11-of-19 5.0 GB: 12-of-19 5.0 GB: 13-of-19 4.9 GB: 14-of-19 5.0 GB: 15-of-19 5.0 GB: 16-of-19 4.9 GB: 17-of-19 5.0 GB: 18-of-19 4.2 GB: 19-of-19
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.39.0.dev0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the Cerebrum 1.0 8x7b 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 Cerebrum 1.0 8x7b

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral 8x7B Instruct V0.1	32K / 93.6 GB	842893	4720
Nous Hermes 2 Mixtral 8x7B DPO	32K / 93.6 GB	40865	457
Mixtral 8x7B V0.1	32K / 93.6 GB	49231	1820
Sensualize Mixtral Bf16	32K / 93.6 GB	0	0
Skadi Mixtral V1	32K / 93.5 GB	0	0
Franziska Mixtral V1	32K / 93.5 GB	0	0
Typhon Mixtral V1	32K / 93.4 GB	0	0
GritLM 8x7B KTO	32K / 93.6 GB	7976	3
Mixtral 8x7B Instruct V0.1	32K / 93.6 GB	7	0
Smaug Mixtral V0.1	32K / 187.7 GB	8437	12

Note: green Score (e.g. "73.2") means that the model is better than AetherResearch/Cerebrum-1.0-8x7b.