Nemotron-3-Embed-1B-BF16

Name: Nemotron-3-Embed-1B-BF16
Author: nvidia

by nvidia

The 1B sibling of Nemotron-3-Embed, for when 8B does not fit the budget

332Kdl/month

145likes

HuggingFace Run it on your own data, free

Identifier

Model ID

nvidia/Nemotron-3-Embed-1B-BF16

Overview

Same family and same retrieval-first intent as the 8B, at roughly an eighth of the size. The tradeoff is the usual one: recall on hard queries against throughput and cost per million chunks.

The useful way to choose is to run both over your own corpus rather than a benchmark. A 1B model that answers your queries is worth more than an 8B that answers someone else's, and the gap between them narrows sharply on domains where the vocabulary is already unambiguous.

Packaged for sentence-transformers and vLLM, like its larger sibling.

Architecture

Ministral3Model, 1,140,918,272 parameters, BF16 weights. Sentence-similarity pipeline packaged for sentence-transformers.

Key Capabilities

•Dense text embeddings for semantic search
•Retrieval over document chunks for RAG
•Cheap enough to run over a large corpus rather than a sample
•A first-pass encoder ahead of a larger reranking stage

Use Cases on Mixpeek

•The text half of a multimodal index where budget rules out an 8B encoder
•First-stage retrieval feeding a more expensive reranker
•Corpora large enough that per-chunk cost dominates the decision
•A baseline to measure whether the 8B is worth its extra cost on your data

Use Nemotron-3-Embed-1B-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-1B-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.