Clip Vit Large Patch 14
OpenAI's Clip Vit Large Patch 14 is a research-oriented model utilizing a ViT-L/14 Transformer architecture as an image encoder and a masked self-attention Transformer as a text encoder, trained with a contrastive loss to maximize the similarity of image-text pairs. It is genuinely best at enabling researchers to explore zero-shot, arbitrary image classification and understand robustness, generalization, and other capabilities of computer vision models.
Input
Output
Context
0K
Max Output
0K
Parameters
427.6M
Technical Specifications
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Estimated GPU requirements for Clip Vit Large Patch 14
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 Clip Vit Large Patch 14
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Clip Vit Large Patch 14 advertises a context window of 77 tokens, with a maximum output of 77 tokens in a single response. 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. Clip Vit Large Patch 14 is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
Clip Vit Large Patch 14 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 Clip Vit Large Patch 14 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; Clip Vit Large Patch 14 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 0.8 GB at INT8 for the weights and a default context, from the catalog's 427.6M 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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