We're excited to introduce Nemotron-Cascade-2-30B-A3B, an open 30B MoE model with 3B activated parameters that delivers strong reasoning and agentic capabilities. It is post-trained from the Nemotron-3-Nano-30B-A3B-Base. Nemotron-Cascade-2-30B-A3B achieves ***gold medal*** performance in both the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI). It operates in both **thinking** and **instruct** (non-thinking) modes. Benchmark Nemotron-3-Nano-30B-A3B Nemotron-3-Super-120B-A12B Qwen3.5-35B-A3B Nemotron-Cascade-2-30B-A3B Requires vLLM version >= 0.17.1. The following will create API endpoints at `http://localhost:8000/v1`:
Introducing **nvidia/nemotron-colembed-vl-8b-v2**, the state-of-the-art late interaction embedding model that currently ranks No. 1 on ViDoRe V3—a comprehensive benchmark for enterprise retrieval use cases—as of January 26, 2026, with a score of `63.54` across 8 public tasks. The model was fine-tuned for query-document retrieval. Users can input `queries`, which are text, or `documents` which are page images, to the model. The model outputs ColBERT-style multi-vector numerical representations for input queries and documents. ✨ **Key Improvements:** * ⚗️ **Advanced Model Merging:** Utilizes post-training model merging to combine the strengths of multiple fine-tuned checkpoints. This delivers the accuracy stability of an ensemble without any additional inference latency. * 🌍 **Enhanced Synthetic Data:** We significantly enriched our training mixture with diverse multilingual synthetic data, improving semantic alignment across languages and complex document types. This model is for non-commercial/research use only. See the Nemotron ColEmbed v2 paper for more details about its architecture, training and results.
Nemotron-Content-Safety-Reasoning-4B is a Large Language Model (LLM) classifier designed to function as a dynamic and adaptable guardrail for content safety and dialogue moderation (topic-following). Its primary advantage is the ability to enforce custom safety policies. Users can "bring their own safety policy", and the model will adapt its classification and reasoning to meet those specific, user-defined criteria. Use of this model is governed by the NVIDIA Open Model License, Gemma Terms of Use and Gemma Prohibited Use Policy.
NVIDIA Nemotron-H-4B-Base-8K is a large language model (LLM) developed by NVIDIA, designed as a completion model for a given piece of text. It uses a hybrid model architecture that consists primarily of Mamba-2 and MLP layers combined with just four Attention layers. The model is pruned and distilled from Nemotron-H-8B-Base-8K using 380B tokens, and features an 8K context length. The supported languages include: English, German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, and Chinese. For best performance on a given task, users are encouraged to customize the model using the NeMo Framework suite of customization tools, including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA, and more), and Model Alignment (SFT, SteerLM, RLHF, and more) using NeMo-Aligner. The model was pruned and distilled from Nemotron-H-Base-8K using our hybrid language model compression technique and then fine-tuned into Nemotron-H-4B-Instruct-128K. For more details, please refer to the paper.
NVIDIA Nemotron-H-56B-Base-8K is a large language model (LLM) developed by NVIDIA that is designed as a completion model for a given piece of text. It uses a hybrid model architecture that consists primarily of Mamba-2 and MLP layers combined with just ten Attention layers. The model features a context length of 8K. The supported languages include: English, German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, and Chinese. For more detailed information on the model architecture, training, and evaluation, please see the project page and the technical report. For best performance on a given task, users are encouraged to customize the model using the NeMo Framework suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA, and more), and Model Alignment (SFT, SteerLM, RLHF, and more) using NeMo-Aligner. This model is part of the Nemotron-H Collection. You can find the models in this family here: - Nemotron-H-56B-Base-8K - Nemotron-H-47B-Base-8K - Nemotron-H-8B-Base-8K
NVIDIA Nemotron-H-8B-Base-8K is a large language model (LLM) developed by NVIDIA that is designed as a completion model for a given piece of text. It uses a hybrid model architecture that consists primarily of Mamba-2 and MLP layers combined with just four Attention layers. The model features a context length of 8K. The supported languages include: English, German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, and Chinese. For more detailed information on the model architecture, training, and evaluation, please see the project page and the technical report. For best performance on a given task, users are encouraged to customize the model using the NeMo Framework suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA, and more), and Model Alignment (SFT, SteerLM, RLHF, and more) using NeMo-Aligner. This model is part of the Nemotron-H Collection. You can find the models in this family here: - Nemotron-H-56B-Base-8K - Nemotron-H-47B-Base-8K - Nemotron-H-8B-Base-8K
