siglip2-base-patch16-naflex by Google: Open-Weight Model

Runs On

What it takes to serve siglip2-base-patch16-naflex (375M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Precision	Weights	Memory needed	Cheapest setup	Per hour	Also fits
16-bit	0.8 GB	0.9 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
8-bit	0.4 GB	0.5 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
4-bit	0.2 GB	0.2 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

SAVRN's Notes on siglip2-base-patch16-naflex

Google built this encoder for three jobs: sorting images against text labels with no task-specific training, matching images to captions for retrieval, and serving as the vision tower inside a larger vision-language model. At 375 million parameters it needs 0.9 GB of memory at 16-bit precision, so hardware choice is about what else is on the card. The cheapest card in our table, one MI300X with 192 GB at $1.85 an hour on demand, carries it alongside the language model it feeds.

Apache 2.0 clears commercial use, modification and redistribution with the notices kept. Three papers describe the method, arXiv 2502.14786, 2303.15343 and 2209.06794, and the first titles it a multilingual vision-language encoder, which matters if your labels are not in English. The release is February 2025, so confirm your inference stack supports the siglip2 model type before ordering hardware.

SAVRN Research, 2026-09-18

Model Card

By Google, published under apache-2.0, revision b53b807d3a2d.

SigLIP 2 Base

SigLIP 2 extends the pretraining objective of SigLIP with prior, independently developed techniques into a unified recipe, for improved semantic understanding, localization, and dense features.

Intended uses

You can use the raw model for tasks like zero-shot image classification and image-text retrieval, or as a vision encoder for VLMs (and other vision tasks).

Here is how to use this model to perform zero-shot image classification:

from transformers import pipeline

# load pipeline
ckpt = "google/siglip2-base-patch16-naflex"
image_classifier = pipeline(model=ckpt, task="zero-shot-image-classification")

# load image and candidate labels
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
candidate_labels = ["2 cats", "a plane", "a remote"]

# run inference
outputs = image_classifier(image, candidate_labels)
print(outputs)

You can encode an image using the Vision Tower like so:

Read the full model card (307 words)

Configuration

Architecture: Siglip2Model
Vocabulary size: 256,000
Stored precision: float32
Model type: siglip2

Identity and Version

Repository: google/siglip2-base-patch16-naflex
Publisher: Google
Task: Zero shot image classification
Modality: Other
Library: transformers
Parameters: 375M parameters
Languages: Not stated by the source
Revision: b53b807d3a2d5e2b3911292f2d69e5341cdc064c
First published: 2025-02-18
Last updated: 2025-02-21

Files and Weights

9 files, 1.5 GB in total. The weights are 1 file totalling 1.5 GB in safetensors.

Weights1 file · 1.5 GB

Configuration3 files · 1.4 KB

Tokenizer3 files · 38.6 MB

Documentation1 file · 3.4 KB

Repository1 file · 1.6 KB

Every file

File	Type	Size	SHA-256
model.safetensors	Weights	1.5 GB	ac5f28bbdf92
config.json	Configuration	329 B	—
preprocessor_config.json	Configuration	393 B	—
special_tokens_map.json	Configuration	636 B	—
README.md	Documentation	3.4 KB	—
.gitattributes	Repository	1.6 KB	—
tokenizer.json	Tokenizer	34.4 MB	58a1696e79c9
tokenizer.model	Tokenizer	4.2 MB	61a7b147390c
tokenizer_config.json	Tokenizer	40.2 KB	—

License and Download

License: apache-2.0
Access: Open weights, no gate
Download size: 1.5 GB

Download from Google

Released by Google through its official repository on Hugging Face. Read the license.

Built From

Described by arXiv:2209.06794
Described by arXiv:2303.15343
Described by arXiv:2502.14786

Memory Requirements

Precision	Weights in memory
As published	1.5 GB
16-bit	0.8 GB
8-bit	0.4 GB
4-bit	0.2 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Compare siglip2-base-patch16-naflex

Questions About siglip2-base-patch16-naflex

How much GPU memory does siglip2-base-patch16-naflex need?

About 0.9 GB at 16-bit and 0.2 GB at 4-bit: the weights (375M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run siglip2-base-patch16-naflex on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use siglip2-base-patch16-naflex commercially?

Yes. siglip2-base-patch16-naflex is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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