Nemotron-3-Embed-8B-BF16

Name: Nemotron-3-Embed-8B-BF16
Author: nvidia

by nvidia

8B text embedding model aimed at retrieval and RAG rather than classification

153Kdl/month

86likes

HuggingFace Run it on your own data, free

Identifier

Model ID

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

Overview

This is a retrieval-first text embedding model. The card tags it for text-embeddings, retrieval, semantic-search and RAG, and ships it in a sentence-transformers layout with vLLM support, which tells you what it is meant to sit inside.

Size is the tradeoff to think about. At 8B parameters this is roughly two orders of magnitude larger than the MiniLM-class encoders most pipelines start with, and it will not be the thing you run over a hundred million chunks. It is the thing you run when recall on hard queries matters more than throughput, or as a reranking stage behind a cheaper first pass.

A 1B variant exists in the same family if this does not fit the budget.

Architecture

Ministral3Model, 7,952,683,008 parameters, BF16 weights. Sentence-similarity pipeline packaged for sentence-transformers and vLLM.

Key Capabilities

•Dense text embeddings for semantic search
•Retrieval over document chunks for RAG
•Reranking a candidate set from a smaller encoder
•Served through vLLM for batched throughput

Use Cases on Mixpeek

•The text half of a multimodal index, alongside an image or video encoder
•High-recall retrieval where a small encoder keeps missing the right chunk
•Second-stage reranking behind a fast first-pass embedding
•Grounding an agent's answers in your own documents

Use Nemotron-3-Embed-8B-BF16 on Mixpeek

Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

Open Studio

How It Runs on Mixpeek

On Mixpeek, Nemotron-3-Embed-8B-BF16 runs as a managed extractor inside a processing pipeline. Point a bucket of sentence similarity data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.