Rad Dino
Microsoft's Rad Dino is a vision transformer model trained using the self-supervised learning method DINOv2 to encode chest X-rays. It is particularly suited for downstream tasks such as image classification, segmentation, clustering, and retrieval, often achieving good performance without requiring fine-tuning.
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Parameters
86.6M
Technical Specifications
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Estimated GPU requirements for Rad Dino
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 Rad Dino
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Microsoft has not published a context window for Rad Dino, so the catalog shows it as not available rather than estimating one.
Yes. Rad Dino is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
Rad Dino accepts Image and produces Embedding. 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 Rad Dino 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; Rad Dino 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 0.5 GB at INT8 for the weights and a default context, from the catalog's 86.6M 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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