nvidia/Nemotron-3-Embed-8B
Nemotron-3-Embed-8B is a multilingual text embedding model from NVIDIA, based on Ministral-3-8B, that maps text into 4096-dimensional dense vectors for retrieval and semantic similarity. Spanning 34 languages, it targets multilingual RAG and question-answering over large corpora and achieves state-of-the-art results on the RTEB leaderboard.
Try the model with a sample request using the same API shape shown in the reference page.
You can add more items with the button on the right
If true, the request is rejected immediately with HTTP 429 when the model has no spare capacity, instead of waiting in the queue. Opt-in; the default (false) keeps standard queueing behavior.
Custom instruction prepending to each input. If empty, no instruction will be used.. (Default: empty)
The service tier used for processing the request. 'priority' processes the request with higher priority (premium rate); 'flex' processes it at lower priority for a discount, served only when spare capacity exists and may be retried/timed out under load. Both apply only to models that support the respective tier.
Enter a JSON array
whether to normalize the computed embeddings
The number of dimensions in the embedding. If not provided, the model's default will be used.If provided bigger than model's default, the embedding will be padded with zeros. (Default: empty, 32 ≤ dimensions ≤ 8192)
Adjust settings and run the demo to see the model output generated here in real-time.
