Nemotron-3-Embed

NVIDIA Nemotron 3 Embed

Model Overview

Description:

Nemotron-3-Embed-1B-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.

Among models of comparable size, Nemotron-3-Embed-1B-BF16 achieves state-of-the-art performance across multiple multilingual retrieval benchmarks. Read more details in our Blog Post.

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: Apache License, Version 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-1B-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-1B-BF16

Model Architecture:

Architecture Type: Transformer

Network Architecture: Ministral-3-3B-Instruct-2512 based pruned model

Number of model parameters: The model has approximately 1.14B parameters

Hidden Size: 2048

The Nemotron-3-Embed-1B-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 2048.

Nemotron-3-Embed-1B-BF16 was derived from the Ministral-3-3B-Instruct-2512 through two iterative rounds of structured pruning and distillation. First, the 3B parent model was trained as a text-embedding model. Then it was pruned to 2B using NVIDIA ModelOpt mcore_minitron Neural Architecture Search (NAS) [NVIDIA/Model-Optimizer, paper]. This process searches across hidden width, FFN size, attention heads, and depth, then selects the best candidate from the top-10 Pareto front. Candidates were evaluated against the parent model’s representations using a 50k in-domain calibration corpus, which was also used to estimate importance scores.

The resulting 2B model was then distilled from the fine-tuned Nemotron-3-Embed-8B-BF16 embedding teacher model to recover accuracy. Distillation used combined cosine distance loss (COS) and mean squared error (MSE) loss on a multilingual, in-domain retrieval data blend. The same pruning-and-distillation procedure, using the same dataset blend, was then repeated to produce the final 1.14B embedding model.

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 2048-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 1024 or 512 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-1B-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