C-RADIO-H

About

What C-RADIO-H is

Model Overview Description This model performs visual feature extraction. For instance, RADIO generates image embeddings that can be used by a downstream model to classify images. C-RADIOv4 models are available in multiple sizes: Shape-Optimized (431M parameters). Huge (653M parameters). C-RADIOv4 was trained using an updated set of teach models: SigLIP2-g DINOv3-7B SAM3 This model is ready for commercial/non-commercial use. License/Terms of Use GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License Agreement. Deployment Geography Global Use Case The embeddings generated by this model are expected to be used by a downstream application. For example: Image-level understanding (image classification, curation, etc.). Dense processing (semantic segmentation, depth estimation, etc.). Integration into a Vision-Language Model. Release Date Hugging Face: 01/27/2026 via RADIO Collection of Models. References AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models FeatSharp: Your Vision Model Features, Sharper C-RADIOv4 (Tech Report) Model Architecture Architecture Type: Neural Network <br Network Architecture: Vision Transformer <br Number of model parameters: -SO400M size: 431M, -H size: 653M <br Input Input Type(s): Image <br Input Format(s): Red, Green, Blue (RGB) <br Input Parameters: Two Dimensional (2D) <br Other Properties Related to Input: Image resolutions up to 2048x2028 in increments of 16 pixels <br Output Output Type(s): Embeddings <br Output Format: Tensor <br Output Parameters: Two Dimensional 2D <br Other Properties Related to Output: Downstream model required to leverage image features. Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br Usage: RADIO will return a tuple with two tensors. The summary is similar to the clstoken in ViT and is meant t

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys c-radio-h for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (c-radio-h below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"c-radio-h","input":"text to embed"}'

Details

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Licence

Commercial licence needed

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