AI agents are poised to deliver as much as $450 billion from revenue gains and cost savings by 2028, according to Capgemini. Developers building these agents are turning to higher-performing reasoning models to improve AI agent platforms and physical AI systems.
At SIGGRAPH, NVIDIA today announced an expansion of two model families with reasoning capabilities - NVIDIA Nemotron and NVIDIA Cosmos - that leaders across industries are using to drive productivity via teams of AI agents and humanoid robots.
CrowdStrike, Uber, Magna, NetApp and Zoom are among some of the enterprises tapping into these model families.
New NVIDIA Nemotron Nano 2 and Llama Nemotron Super 1.5 models offer the highest accuracy in their size categories for scientific reasoning, math, coding, tool-calling, instruction-following and chat. These new models give AI agents the power to think more deeply and work more efficiently - exploring broader options, speeding up research and delivering smarter results within set time limits.
Think of the model as the brain of an AI agent - it provides the core intelligence. But to make that brain useful for a business, it must be embedded into an agent that understands specific workflows, in addition to industry and business jargon, and operates safely. NVIDIA helps enterprises bridge that gap with leading libraries and AI blueprints for onboarding, customizing and governing AI agents at scale.
Cosmos Reason is a new reasoning vision language model (VLM) for physical AI applications that excels in understanding how the real world works, using structured reasoning to understand concepts like physics, object permanence and space-time alignment.
Cosmos Reason is purpose-built to serve as the reasoning backbone to a robot vision language action (VLA) model, or critique and caption training data for robotics and autonomous vehicles, and equip runtime visual AI agents with spatial-temporal understanding and reasoning of physical operations, like in factories or cities.
Nemotron: Highest Accuracy and Efficiency for Agentic Enterprise AI As enterprises develop AI agents to tackle complex, multistep tasks, models that can provide strong reasoning accuracy with efficient token generation enable intelligent, autonomous decision-making at scale.
NVIDIA Nemotron is a family of advanced open reasoning models that use leading models, NVIDIA-curated open datasets and advanced AI techniques to provide an accurate and efficient starting point for AI agents.
The latest Nemotron models deliver leading efficiency in three ways: a new hybrid model architecture, compact quantized models and a configurable thinking budget that provides developers with control over token generation, resulting in 60% lower reasoning costs. This combination lets the models reason more deeply and respond faster, without needing more time or computing power. This means better results at a lower cost.
Nemotron Nano 2 provides as much as 6x higher token generation compared with other leading models of its size.
Llama Nemotron Super 1.5 achieves leading performance and the highest reasoning accuracy in its class, empowering AI agents to reason better, make smarter decisions and handle complex tasks independently. It's now available in NVFP4, or 4-bit floating point, which delivers as much as 6x higher throughput on NVIDIA B200 GPUs compared with NVIDIA H100 GPUs.
The chart above shows the Nemotron model delivers top reasoning accuracy in the same timeframe and on the same compute budget, delivering the highest accuracy per dollar.
Along with the two new Nemotron models, NVIDIA is also announcing its first open VLM training dataset - Llama Nemotron VLM dataset v1 - with 3 million samples of optical character recognition, visual QA and captioning data that power the previously released Llama 3.1 Nemotron Nano VL 8B model.
In addition to the accuracy of the reasoning models, agents also rely on retrieval-augmented generation to fetch the latest and most relevant information from connected data across disparate sources to make informed decisions. The recently released Llama 3.2 NeMo Retriever embedding model tops three visual document retrieval leaderboards - ViDoRe V1, ViDoRe V2 and MTEB VisualDocumentRetrieval - for boosting agentic system accuracy.
Using these reasoning and information retrieval models, a deep research agent built using the AI-Q NVIDIA Blueprint is currently No. 1 for open and portable agents on DeepResearch Bench.
NVIDIA NeMo and NVIDIA NIM microservices support the entire AI agent lifecycle - from development and deployment to monitoring and optimization of the agentic systems.
Cosmos Reason: A Breakthrough in Physical AI
VLMs marked a breakthrough for computer vision and robotics, empowering machines to identify objects and patterns. However, nonreasoning VLMs lack the ability to understand and interact with the real world - meaning they can't handle ambiguity or novel experiences, nor solve complex multistep tasks.
NVIDIA Cosmos Reason is a new open, customizable, 7-billion-parameter reasoning VLM for physical AI and robotics. Cosmos Reason lets robots and vision AI agents reason like humans, using prior knowledge, physics understanding and common sense to understand and act in the physical world.
Cosmos Reason enables advanced capabilities across robotics and physical AI applications such as training data critiquing and captioning, robot decision-making and video analytics AI agents.
It can help automate the curation and annotation of large, diverse training datasets, accelerating the development of high-accuracy AI models. It can also serve as a sophisticated reasoning engine for robot planning, parsing complex instructions into actionable steps for VLA models, even in new environments.
It also powers video analytics AI agents built on the NVIDIA Blueprint for video search and su
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AI agents are poised to deliver as much as $450 billion from revenue gains and cost savings by 2028, according to Capgemini. Developers building these agents are turning to higher-performing reasoning models to improve AI agent platforms and physical AI systems.
