What are the hardware requirements for Convnext Xxlarge.clip Laion2b Soup Ft In1k?

Convnext Xxlarge.clip Laion2b Soup Ft In1k requires approximately 3.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Convnext Xxlarge.clip Laion2b Soup Ft In1k and how large is it?

Convnext Xxlarge.clip Laion2b Soup Ft In1k is developed by timm, a model with 2b 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 Convnext Xxlarge.clip Laion2b Soup Ft In1k?

Convnext Xxlarge.clip Laion2b Soup Ft In1k is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Convnext Xxlarge.clip Laion2b Soup Ft In1k by timm — VRAM 3.4GB

Name: Convnext Xxlarge.clip Laion2b Soup Ft In1k
Author: timm

Convnext Xxlarge.clip Laion2b Soup Ft In1k is an open-source language model by timm. Features: 2b LLM, VRAM: 3.4GB, License: apache-2.0.

Arxiv:2103.00020 Arxiv:2201.03545 Arxiv:2210.08402 Dataset:imagenet-1k Dataset:laion-2b Image-classification Pytorch Region:us Safetensors Timm

Model Card on HF 🤗: https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k

Convnext Xxlarge.clip Laion2b Soup Ft In1k Parameters and Internals

LLM Name	Convnext Xxlarge.clip Laion2b Soup Ft In1k
Repository 🤗	https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k
Model Size	2b
Required VRAM	3.4 GB
Updated	2026-08-10
Maintainer	timm
Model Files	3.4 GB 3.4 GB
Model Architecture	AutoModel
License	apache-2.0

Rank the Convnext Xxlarge.clip Laion2b Soup Ft In1k capabilities

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Note: green Score (e.g. "73.2") means that the model is better than timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k.