What are the hardware requirements for Laguna S 2.1 OQ4e Fast?

Laguna S 2.1 OQ4e Fast requires approximately 62.9 GB of VRAM and supports a context window of 1024K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Laguna S 2.1 OQ4e Fast and how large is it?

Laguna S 2.1 OQ4e Fast is developed by mlx-community, a model with 16.8b 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 Laguna S 2.1 OQ4e Fast?

Laguna S 2.1 OQ4e Fast is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Laguna S 2.1 OQ4e Fast by mlx-community — VRAM 62.9GB, 1024K context

Name: Laguna S 2.1 OQ4e Fast
Author: mlx-community

Laguna S 2.1 OQ4e Fast is an open-source language model by mlx-community. Features: 16.8b LLM, VRAM: 62.9GB, Context: 1024K, License: openmdw-1.1, LLM Explorer Score: 0.31.

4-bit Base model:poolside/laguna-s-2... Base model:quantized:poolside/... Conversational Custom code Laguna Mlx Moe Oq Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mlx-community/Laguna-S-2.1-oQ4e-fast

Laguna S 2.1 OQ4e Fast Parameters and Internals

LLM Name	Laguna S 2.1 OQ4e Fast
Repository 🤗	https://huggingface.co/mlx-community/Laguna-S-2.1-oQ4e-fast
Base Model(s)	poolside/Laguna-S-2.1 poolside/Laguna-S-2.1
Model Size	16.8b
Required VRAM	62.9 GB
Updated	2026-08-10
Maintainer	mlx-community
Model Type	laguna
Model Files	5.1 GB: 1-of-13 5.1 GB: 2-of-13 5.1 GB: 3-of-13 5.1 GB: 4-of-13 5.1 GB: 5-of-13 5.1 GB: 6-of-13 5.1 GB: 7-of-13 5.1 GB: 8-of-13 5.1 GB: 9-of-13 5.1 GB: 10-of-13 5.1 GB: 11-of-13 5.1 GB: 12-of-13 1.7 GB: 13-of-13
Model Architecture	LagunaForCausalLM
License	openmdw-1.1
Context Length	1048576
Model Max Length	1048576
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	〈\|PAD\|〉
Vocabulary Size	100352
Torch Data Type	bfloat16

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
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Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation