Model reference · open weights
llama-embed-nemotron is an open-weight embedding model from nvidia, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
llama-embed-nemotron-8b Model Overview Description: llama-embed-nemotron-8b is a versatile text embedding model trained by NVIDIA and optimized for retrieval, reranking, semantic similarity, and classification use cases. This model has robust capabilities for multilingual and cross-lingual text retrieval. It is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems. This model achieves state-of-the-art performance on the multilingual MTEB leaderboard as of October 21, 2025. Together with the model weights, we're releasing the full recipe behind the llama-embed-nemotron-8b: - A detailed technical report focusing on our Synthetic Data Generation (SDG) pipeline and core design choices. - The training dataset, featuring a curated mix of public and synthetic data. - The full training code via the NeMo AutoModel framework. This model is for non-commercial/research use only. License/Terms of Use Governing Terms for llama-embed-nemotron-8b model: NVIDIA License <br Additional Information: Llama-3.1 Community License Agreement for meta-llama/Llama-3.1-8B. Acceptable Use Policy. Built with Llama. Team - Yauhen Babakhin - Radek Osmulski - Ronay Ak - Gabriel Moreira - Mengyao Xu - Benedikt Schifferer - Bo Liu - Even Oldridge Correspondence to Yauhen Babakhin (ybabakhin@nvidia.com) and Bo Liu (boli@nvidia.com). Citation NVIDIA’s Retrieval Models Deployment Geography: Global <br Use Case: <br The llama-embed-nemotron-8b model is intended for researchers developing applications that need to understand or retrieve information from text. It is well-suited for multilingual RAG systems in which queries and documents are textual and may be in different languages. <br Release Date: <br Hugging Face on 10/21/2025 via https://huggingface.co/nvidia/llama-embed-nemotron-8b <br Model Architecture: - Architecture Type: Transformer Decoder <br - Network Architecture: Llama-3.1-8B with bi-directional attention <br - This model was developed based on meta-llama/Llama-3.1-8B model. <br - Number of model parameters: 7,504,924,672 <br This llama-embed-nemotron-8b embedding model is a fine-tuned version of Llama-3.1-8B transformer decoder archite
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | nvidia |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 7.5B |
| Variants | 1 |
| Runs with | sentence-transformers |
| Released | 2025-10-07 |
| Popularity | 419k downloads / month |
| Likes | 170 |
| Licence | Commercial licence needed |
How it works
Variants
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.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| llama-embed-nemotron-8b | 7.5B | BF16 | ~17.3 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys llama-embed-nemotron for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (llama-embed-nemotron 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":"llama-embed-nemotron","input":"text to embed"}'
Details
Languages
Trained / evaluated on
Tags
Papers
Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗
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