Model reference · open weights
llama-nemotron-embed 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
Model Overview Description The Llama Nemotron Embedding 1B model is optimized for multilingual and cross-lingual text question-answering retrieval with support for long documents (up to 8192 tokens) and dynamic embedding size (Matryoshka Embeddings). This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish. In addition to enabling multilingual and cross-lingual question-answering retrieval, this model reduces the data storage footprint by 35x through dynamic embedding sizing and support for longer token length, making it feasible to handle large-scale datasets efficiently. An embedding model is a crucial component of a text retrieval system, as it transforms textual information into dense vector representations. They are typically transformer encoders that process tokens of input text (for example: question, passage) to output an embedding. This model is ready for commercial use. The Llama Nemotron Embedding 1B model is a part of the NVIDIA NeMo Retriever collection of NIM, which provide state-of-the-art, commercially-ready models and microservices, optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can also readily customize them for domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants. We are excited to announce the open sourcing of this commercial embedding model. For users interested in deploying this model in production environments, it is also available via the model API in NVIDIA Inference Microservices (NIM) at llama-nemotron-embed-1b-v2. Intended use The Llama Nemotron Embedding 1B model is most suitable for users who want to build a multilingual question-and-answer application
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | nvidia |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 1.2B |
| Variants | 1 |
| Runs with | sentence-transformers |
| Released | 2025-10-16 |
| Popularity | 632k downloads / month |
| Likes | 61 |
| 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-nemotron-embed-1b-v2 | 1.2B | BF16 | ~2.8 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys llama-nemotron-embed for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (llama-nemotron-embed 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-nemotron-embed","input":"text to embed"}'
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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