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NVIDIA’s New AI Models: Nemotron 3.5 Lightning, Cosmos 3 and the Future of Agentic AI
Explore NVIDIA Nemotron 3.5 Lightning and Cosmos 3, how they support agentic and Physical AI, and what these models reveal about NVIDIA’s evolving vision for autonomous intelligent systems.
How NVIDIA Cosmos 3 Enables Physical AI and Multi-Agent Workflows
Explore how NVIDIA Cosmos 3 enables Physical AI and agent-based workflows through multimodal reasoning, world simulation, action generation, synthetic data, and autonomous system development.
NVIDIA Nemotron 3.5 Lightning vs NVIDIA Cosmos 3: Key Differences and Use Cases
Compare NVIDIA Nemotron 3.5 Lightning and NVIDIA Cosmos 3, including their architectures, capabilities, performance goals, and use cases across agentic AI and Physical AI.
NVIDIA Cosmos 3 Explained: The Next Generation of Physical AI
Explore NVIDIA Cosmos 3, an open foundation model for Physical AI that combines vision reasoning, world generation, multimodal understanding, and action prediction for robotics and autonomous systems.
NVIDIA Nemotron 3.5 Lightning: What It Is and How It Advances AI Reasoning
Explore NVIDIA Nemotron 3.5 Lightning, a reasoning-capable 30B MoE model designed for efficient agentic AI, coding, tool use, long-context tasks, and low-latency inference.
Jev for Software Automation
Explore how Jev supports software automation with fast, typed, probabilistic decisions for routing, scoring, classification, verification, branching, and workflow control.
Jev as an Intelligence Primitive
Explore how Jev functions as an intelligence primitive for software, providing fast, typed, probabilistic decisions that developers can combine into larger automated workflows.
Jev as a Decision Layer
Explore how Jev can function as a decision layer inside software, turning structured state and questions into typed, probabilistic outputs for automation and workflow control.
Jev Decision-Making Pipeline
Learn how Jev’s decision-making pipeline transforms program state and structured questions into typed, probabilistic decisions that software can use for automation.
Jev Latency Explained
Learn how Jev achieves low-latency AI decisions through parallel sampling, structured outputs, calibrated probabilities, and System One architecture designed for real-time software automation.
Jev Inference Architecture
Explore Jev’s inference architecture, including parallel sampling, structured outputs, calibrated probabilities, and how TypeSafe AI enables fast machine-native decision-making.
Jev Decision Pipeline
Learn how the Jev decision pipeline works, from program state and structured questions to parallel processing, typed outputs, calibrated probabilities, and automated software actions.