# siglip2-so400m-patch14-384 by Google: Open-Weight Model Source: https://savrn.com/models/siglip2-so400m-patch14-384 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## 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. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 2.3 GB | 2.7 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 1.1 GB | 1.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.6 GB | 0.7 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 24, 2026. [siglip2-so400m-patch14-384 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/siglip2-so400m-patch14-384/gpus) ## 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](https://savrn.com/papers/siglip-2-multilingual-vision-language-encoders-with-improved-semantic-understanding-locali) extends the pretraining objective of [SigLIP](https://savrn.com/papers/sigmoid-loss-for-language-image-pre-training) 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)](https://savrn.com/models/siglip2-so400m-patch14-384/card) ## 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 | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 4.5 GB | 9f4f4a49f908 | | config.json | Configuration | 559 B | — | | preprocessor_config.json | Configuration | 394 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 | cb9140fae3ac | | tokenizer.model | Tokenizer | 4.2 MB | 61a7b147390c | | tokenizer_config.json | Tokenizer | 47.2 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 4.5 GB [Download from Google](https://huggingface.co/google/siglip2-so400m-patch14-384) Released by Google through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:2209.06794](https://savrn.com/papers/pali-a-jointly-scaled-multilingual-language-image-model) - Described by [arXiv:2303.15343](https://savrn.com/papers/sigmoid-loss-for-language-image-pre-training) - Described by [arXiv:2502.14786](https://savrn.com/papers/siglip-2-multilingual-vision-language-encoders-with-improved-semantic-understanding-locali) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 4.5 GB | | 16-bit | 2.3 GB | | 8-bit | 1.1 GB | | 4-bit | 0.6 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Built on This Model - Quantized from[RK182X-VIT-siglip2-so400m-patch14-384](https://savrn.com/models/rk182x-vit-siglip2-so400m-patch14-384) - Derived from[RK182X-VIT-siglip2-so400m-patch14-384](https://savrn.com/models/rk182x-vit-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. ## Similar Models Model · Zero shot image classification ### [siglip-so400m-patch14-384](https://savrn.com/models/siglip-so400m-patch14-384) [Google](https://savrn.com/model-publishers/google) SigLIP model pre-trained on WebLi at resolution 384x384. It was introduced in the paper Sigmoid Loss for Language Image Pre-Training by Zhai et al. and first released in this repository. This model has the SoViT-400m architecture, which is the shape-optimized version as presented in Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design by Alabdulmohsin et al. Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team. SigLIP is CLIP, a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities… Open weights apache-2.0 878M parameters transformers [View model](https://savrn.com/models/siglip-so400m-patch14-384) Model · Zero shot image classification ### [clip-vit-large-patch14](https://savrn.com/models/clip-vit-large-patch14) [OpenAI](https://savrn.com/model-publishers/openai) Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here. The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. January 2021 The base model uses a ViT-L/14 Transformer architecture as an image encoder and uses a masked… Open weights 428M parameters 77 tokens transformers [View model](https://savrn.com/models/clip-vit-large-patch14) Model · Zero shot image classification ### [CLIP-ViT-L-14-laion2B-s32B-b82K](https://savrn.com/models/clip-vit-l-14-laion2b-s32b-b82k) [LAION eV](https://savrn.com/model-publishers/laion) A CLIP ViT L/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/openclip). Model training ('babysitting') done by Ross Wightman on the JUWELS Booster supercomputer. See acknowledgements below. As per the original OpenAI CLIP model card, this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts… Open weights mit 428M parameters 77 tokens open_clip [View model](https://savrn.com/models/clip-vit-l-14-laion2b-s32b-b82k) Model · Zero shot image classification ### [siglip2-giant-opt-patch16-384](https://savrn.com/models/siglip2-giant-opt-patch16-384) [Google](https://savrn.com/model-publishers/google) 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. 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: You can encode an image using the Vision Tower like so: For more code examples, we refer to the siglip documentation. SigLIP 2 adds some clever training objectives on top of SigLIP: SigLIP 2 is pre-trained on the WebLI dataset (Chen et al., 2023). The model was trained on up… Open weights apache-2.0 1.9B parameters transformers [View model](https://savrn.com/models/siglip2-giant-opt-patch16-384) Model · Zero shot image classification ### [siglip2-base-patch16-256](https://savrn.com/models/siglip2-base-patch16-256) [Google](https://savrn.com/model-publishers/google) 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. 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: You can encode an image using the Vision Tower like so: For more code examples, we refer to the siglip documentation. SigLIP 2 adds some clever training objectives on top of SigLIP: SigLIP 2 is pre-trained on the WebLI dataset (Chen et al., 2023). The model was trained on up… Open weights apache-2.0 375M parameters transformers [View model](https://savrn.com/models/siglip2-base-patch16-256) Model · Zero shot image classification ### [siglip2-base-patch16-naflex](https://savrn.com/models/siglip2-base-patch16-naflex) [Google](https://savrn.com/model-publishers/google) 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. 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: You can encode an image using the Vision Tower like so: For more code examples, we refer to the siglip2 documentation. SigLIP 2 adds some clever training objectives on top of SigLIP: SigLIP 2 is pre-trained on the WebLI dataset (Chen et al., 2023). The model was trained on up… Open weights apache-2.0 375M parameters transformers [View model](https://savrn.com/models/siglip2-base-patch16-naflex) ## Google [All models and datasets](https://savrn.com/model-publishers/google) ## Versions - [e8e487298228](https://savrn.com/models/siglip2-so400m-patch14-384/versions/e8e487298228) · current 2026-10-06 ## Explore More - [All zero shot image classification models](https://savrn.com/models/tasks/zero-shot-image-classification) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Google website](https://ai.google.dev/) - [Hugging Face record](https://huggingface.co/google/siglip2-so400m-patch14-384) - [How the hub is built](https://savrn.com/model-hub/methodology)