Model Detail
Nemotron-3-Embed-8B-BF16
—Nemotron-3-Embed-8B-BF16 is a large language model with 8B parameters released by NVIDIA. The model is registered under the sentence-similarity pipeline tag on Hugging Face, distributed under a other license.
Nemotron-3-Embed-8B-BF16 ships with 8B parameters. Total weight footprint is approximately 8.0 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
Nemotron-3-Embed-8B-BF16 is best fit for general-purpose chat and instruction-following workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval
Nemotron-Labs-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context
arXiv:2606.26493v2 Announce Type: replace Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their potential for parallel and iterative generation. However, existing approaches use a single network for both context representation and iterative denoising
Nemotron-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context
arXiv:2606.26493v1 Announce Type: new Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their potential for parallel and iterative generation. However, existing approaches use a single network for both context representation and iterative denoising, fo
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
arXiv:2606.15007v1 Announce Type: cross Abstract: We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 trillion text tokens, then extended the context length to 1M tokens, an
Marginal Alignment Does Not Guarantee Joint-Distribution Fidelity: An Official-Reference Audit of Nemotron-Personas-Korea with Cross-Locale Replication
arXiv:2606.12433v1 Announce Type: cross Abstract: Synthetic persona datasets cite alignment with official demographics as a basis for trust, yet downstream users consume them as joint structures across age, sex, region, occupation, education, name, and institutional status. Marginal alignment does n
