Nemotron Series by NVIDIA
The Nemotron Series is NVIDIA’s family of advanced AI models designed to power agentic AI systems capable of executing complex workflows autonomously. This series combines open-source innovation with proprietary advancements to deliver scalable solutions for text generation, multimodal reasoning, coding tasks, and autonomous workflows. Built on Meta’s Llama architecture with NVIDIA’s proprietary enhancements, the series includes both open-source models like Nemotron-4 340B for research and commercial use and proprietary offerings such as Nemotron Nano and Cosmos Nemotron Ultra for enterprise-grade applications. These models are optimized for deployment across a range of hardware configurations, from edge devices to data centers.
Current Models in the Nemotron Series:
Open-Source Models:
Nemotron-4 340B:
A flagship multilingual model with 340 billion parameters supporting over 50 natural languages and 40 programming languages. Optimized for synthetic data generation, instruction-following tasks, and efficient deployment on GPU infrastructure.
Nemotron-4 340B-Instruct:
A fine-tuned version of the base model, optimized for English-based single and multi-turn chat use-cases, supporting a context length of 4,096 tokens.
Nemotron-4 340B-Reward:
A pretrained reward model intended for use in English synthetic data generation and reinforcement learning from AI feedback. It extends the base model with an additional linear layer to output scalar values corresponding to specific attributes.
Proprietary Models:
Nemotron Nano:
A lightweight proprietary model optimized for real-time applications on PCs and edge devices. Ideal for customer support chatbots and local analytics.
Nemotron Super:
A high-performance proprietary model offering exceptional throughput on a single GPU for enterprise-grade deployments.
Nemotron Ultra:
The largest proprietary model in the series designed for data-center-scale applications requiring maximum accuracy and throughput.
Vision-Language Models (Cosmos Nemotron):
Cosmos Nemotron Nano:
A compact vision-language model capable of real-time image recognition, object detection, and visual reasoning tasks.
Cosmos Nemotron Super:
A high-throughput VLM optimized for video summarization, document analysis, and multimodal question answering on enterprise-grade hardware.
Cosmos Nemotron Ultra:
A large-scale multimodal model designed for advanced visual-language tasks such as scene understanding and activity recognition.
Key Features Across the Series:
Agentic Capabilities: Supports instruction following, function-calling workflows, coding tasks, synthetic data generation, and reasoning essential for autonomous agents.
Multimodal Processing: Cosmos Nemotron models integrate text with visual inputs to enable advanced vision-language capabilities like image captioning and video summarization.
Optimized Hardware Integration: Tailored to run efficiently on NVIDIA GPUs using Tensor Cores for accelerated performance across all models.
Flexible Deployment Options: Open-source models like Nemotron-4 are freely available under permissive licenses, while proprietary models like Nano and Ultra are offered through NVIDIA AI Enterprise or cloud platforms like Amazon Bedrock.
Synthetic Data Generation: Open-source models excel at generating high-quality training datasets at scale to train smaller specialized AI systems.
Example Use Cases:
Automating customer service workflows with real-time chatbots powered by Nemotron Nano or Super models.
Conducting video analysis for security monitoring or sports footage review using Cosmos Nemotron Ultra’s multimodal capabilities.
Generating synthetic datasets for training custom AI systems with the open-source Nemotron-4 340B model.
Enhancing coding productivity through advanced code generation tools integrated with Llama-based Nemotron models.
The Nemotron Series reflects NVIDIA’s commitment to advancing agentic AI by combining open-source accessibility with proprietary innovation to deliver scalable solutions tailored to diverse industries while leveraging its expertise in GPU optimization.