What are the hardware requirements for Laserxtral?

Laserxtral requires approximately 48.3 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 Laserxtral and how large is it?

Laserxtral is developed by cognitivecomputations, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Laserxtral perform on standard benchmarks?

Laserxtral has the following published scores: MMLU 64.68. Compare against reference models on this page or on the LLM Explorer leaderboards.

Laserxtral by cognitivecomputations — VRAM 48.3GB, 32K context

Name: Laserxtral
Author: cognitivecomputations

Laserxtral is an open-source language model by cognitivecomputations. Features: 7b LLM, VRAM: 48.3GB, Context: 32K, License: cc-by-nc-2.0, LLM Explorer Score: 0.12, Arc: 69, HellaSwag: 86.8, MMLU: 64.7, GSM8K: 69.8.

Autotrain compatible Endpoints compatible Mixtral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/cognitivecomputations/laserxtral

Laserxtral Parameters and Internals

Model Type

Mixture of Experts

Additional Notes

The model is an experimentation focusing on 'lasering' each expert to denoise and enhance capabilities. It shows high truthfulness and reasoning capabilities.

LLM Name	Laserxtral
Repository 🤗	https://huggingface.co/cognitivecomputations/laserxtral
Model Size	7b
Required VRAM	48.3 GB
Updated	2025-07-13
Maintainer	cognitivecomputations
Model Type	mixtral
Model Files	9.9 GB: 1-of-5 10.0 GB: 2-of-5 10.0 GB: 3-of-5 10.0 GB: 4-of-5 8.4 GB: 5-of-5
Model Architecture	MixtralForCausalLM
License	cc-by-nc-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Laserxtral 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 Laserxtral

Best Alternatives	Context / RAM	Downloads	Likes
Multimaster 7B V6	32K / 142.5 GB	8108	2
Mini Mixtral V0.2	32K / 25.8 GB	3923	4
MultiverseBuddy 15B MoE	32K / 25.8 GB	9	0
Lumina 2	32K / 37.1 GB	6	0
OpenMistral MoE	32K / 48.3 GB	1218	0
RogerWizard 12B MoE	32K / 25.8 GB	4	1
Rawr	32K / 93.5 GB	1302	0
StarlingMaths 12B MoE	32K / 25.8 GB	5	0
Merged Model MoE	32K / 53.3 GB	7	1
Lumina 3	32K / 37.1 GB	45	0

Note: green Score (e.g. "73.2") means that the model is better than cognitivecomputations/laserxtral.