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Open-weight model · Zero shot image classification

siglip2-so400m-patch14-384

by Google google/siglip2-so400m-patch14-384

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.

Parameters1.1B
Context—
Weights4.5 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads1M

Runs On

What it takes to serve siglip2-so400m-patch14-384 (1.1B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 2.3 GB 2.7 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 1.1 GB 1.4 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.6 GB 0.7 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-so400m-patch14-384

Most deployments will not serve this model on its own. Google describes it as a vision encoder for VLMs as well as a standalone tool for zero-shot image classification and image-text retrieval, and at 2.7 GB of memory at 16-bit, 2.3 GB of it weights, it sits beside whatever language model it feeds. The cheapest host we list, one MI300X with 192 GB at $1.85 an hour, runs it with room for the rest of the stack. Twenty-seven layers, a 1,152 hidden size and a 256,000-entry vocabulary make up the 1.1B parameters.

Apache 2.0 allows commercial use, modification and redistribution, provided the license and copyright notices stay and significant changes are stated, and it includes a patent grant. Before committing, note that no context length is listed, the files total 4.58 GB in safetensors only, and the design rests on three papers in our records: arXiv:2502.14786, arXiv:2303.15343 and arXiv:2209.06794.

SAVRN Research, 2026-09-18

Model Card

By Google, published under apache-2.0, revision e8e487298228.

SigLIP 2 So400m

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-so400m-patch14-384"
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

Layers
27
Hidden size
1,152
Feed-forward size
4,304
Attention heads
16
Vocabulary size
256,000
Model type
siglip

Identity and Version

Repository
google/siglip2-so400m-patch14-384
Publisher
Google
Task
Zero shot image classification
Modality
Other
Library
transformers
Parameters
1.1B parameters
Languages
Not stated by the source
Revision
e8e487298228002f3d8a82e0cd5c8ea9c567f57f
First published
2025-02-17
Last updated
2025-02-21

Files and Weights

9 files, 4.6 GB in total. The weights are 1 file totalling 4.5 GB in safetensors.

Weights1 file · 4.5 GB
Configuration3 files · 1.6 KB
Tokenizer3 files · 38.7 MB
Documentation1 file · 3.4 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights4.5 GB 9f4f4a49f908
config.jsonConfiguration559 B —
preprocessor_config.jsonConfiguration394 B —
special_tokens_map.jsonConfiguration636 B —
README.mdDocumentation3.4 KB —
.gitattributesRepository1.6 KB —
tokenizer.jsonTokenizer34.4 MB cb9140fae3ac
tokenizer.modelTokenizer4.2 MB 61a7b147390c
tokenizer_config.jsonTokenizer47.2 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
4.5 GB
Download from Google

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

Built From

Memory Requirements

PrecisionWeights in memory
As published4.5 GB
16-bit2.3 GB
8-bit1.1 GB
4-bit0.6 GB

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

Compare siglip2-so400m-patch14-384

Questions About siglip2-so400m-patch14-384

How much GPU memory does siglip2-so400m-patch14-384 need?

About 2.7 GB at 16-bit and 0.7 GB at 4-bit: the weights (1.1B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run siglip2-so400m-patch14-384 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-so400m-patch14-384 commercially?

Yes. siglip2-so400m-patch14-384 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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