nvidia/Nemotron-3-Embed-8B-BF16

Model Overview

Description:

Nemotron-3-Embed-8B-BF16 is a versatile text embedding model trained by NVIDIA and optimized for retrieval and semantic similarity tasks. It provides strong multilingual and cross-lingual retrieval capabilities and is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems. This model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, Vietnamese.

The model generates dense vector embeddings from multilingual text inputs, enabling retrieval, semantic search, and (agentic) RAG workflows. As a core component of text retrieval systems, an embedding model transforms text, such as questions or passages, into dense vector representations. These models are typically transformer encoders that process input tokens and produce embeddings suitable for efficient similarity matching.

Nemotron-3-Embed-8B-BF16 achieves state-of-the-art performance on the multilingual RTEB leaderboard as of July 16, 2026.

This model is ready for commercial use.

License/Terms of Use:

This model and its associated configuration files are licensed under the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1). Additional Information: Built with Ministral-3-8B-Instruct-2512 which is released under Apache 2.0.

This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

Deployment Geography:

Global

Use Case:

Nemotron-3-Embed-8B-BF16 is most suitable for users who want to build a multilingual question-and-answer application over a large text corpus, leveraging the latest dense retrieval technologies.

Release Date:

07/16/2026 via https://huggingface.co/nvidia/Nemotron-3-Embed-8B-BF16

Model Architecture:

Architecture Type: Transformer

Network Architecture: Ministral-3-8B-Instruct-2512 based encoder model.

Number of model parameters: The model has approximately 8B parameters.

Hidden Size: 4096

The Nemotron-3-Embed-8B-BF16 model is a transformer-based text embedding model trained with bidirectional attention masking, where the final embedding vector is obtained by applying average pooling to the transformer’s token-level representations. It encodes each input text into a dense embedding vector of dimension 4096.

Input(s):

Input Type(s): Text

Input Format(s):

• Text: List of strings

Input Parameters:

• Text: One-Dimensional (1D)

Other Properties Related to Input: Text inputs should be tokenized by the model tokenizer. The model’s max sequence length is 32768. Longer inputs should be chunked or truncated.

Output(s):

Output Type(s): Floats

Output Format(s):

• List of float arrays

Output Parameters: One-Dimensional (1D) embedding vector per input text string

Other Properties Related to Output: The model outputs a 4096-dimensional embedding vector for each input text string. It also supports dynamic embedding sizes by slicing the vector from the start (for example, keeping the first 2048 or 1024 dimensions). These sliced embeddings remain highly functional, provided the resulting sub-vector is re-normalized (L2 normalization) after slicing.

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.

Usage

For local Python examples, use the Hugging Face model ID by default. If you are working from a local checkout, replace MODEL_ID with that path. For vLLM online serving from a local checkpoint, use the local checkpoint example.

MODEL_ID = "nvidia/Nemotron-3-Embed-8B-BF16"

Use this model for retrieval-style embeddings. Add the query: prefix for queries and the passage: prefix for documents. Embeddings are L2-normalized, so dot product and cosine similarity are equivalent. The output tables use q[i] for queries and d[i] for documents. Scores are rounded to four decimals. Exact values can vary by runtime and package version.

Local Python Dependencies

The BF16 checkpoints support Transformers 5.2.0 and above. The examples also require a CUDA-enabled PyTorch installation that matches your driver and CUDA environment.

If you are not using an NVIDIA PyTorch container, install PyTorch first. Use the PyTorch local installation selector to choose the command that matches your operating system, package manager, and CUDA environment. If the default PyPI wheel matches your CUDA environment, run:

pip install --upgrade torch

For non-default CUDA environments, the selector can include an additional --index-url argument. Use that CUDA-specific index URL when the default PyPI wheel does not match your CUDA environment.

Then install Transformers and Sentence Transformers:

pip install --upgrade "transformers>=5.2.0" "sentence-transformers>=5.4.1"

NVIDIA tested the examples in nvcr.io/nvidia/pytorch:26.06-py3 with the container-provided Torch and CUDA stack. Inside NVIDIA PyTorch containers, do not upgrade Torch. Install only the missing packages:

pip install --upgrade "transformers>=5.2.0" "sentence-transformers>=5.4.1"

The tested nvcr.io/nvidia/pytorch:26.06-py3 container includes flash-attn, so the snippets use FlashAttention-2 by default. If your environment does not have FlashAttention-2, set attn_implementation or ATTN_IMPLEMENTATION to sdpa.

Sentence Transformers

Use Sentence Transformers for the simplest local Python interface. It reads the saved query and document prompts and normalization metadata.

import torch
from sentence_transformers import SentenceTransformer

MODEL_ID = "nvidia/Nemotron-3-Embed-8B-BF16"

model = SentenceTransformer(
MODEL_ID,...