Siglip 2 Base Patch 16 Naflex
Google's Siglip 2 Base Patch 16 Naflex is a vision model designed for tasks such as zero-shot image classification and image-text retrieval. It is genuinely best at semantic understanding, localization, and dense features, thanks to its unified pretraining objective that combines prior techniques.
Input
Output
Context
1K
Max Output
-
Parameters
375.2M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Siglip 2 Base Patch 16 Naflex
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Siglip 2 Base Patch 16 Naflex
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Siglip 2 Base Patch 16 Naflex advertises a context window of 1,024 tokens. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Siglip 2 Base Patch 16 Naflex is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Siglip 2 Base Patch 16 Naflex accepts Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Siglip 2 Base Patch 16 Naflex is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Siglip 2 Base Patch 16 Naflex is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 0.7 GB at INT8 for the weights and a default context, from the catalog's 375.2M parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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