NVIDIA Releases Cosmos 3 Edge: A 4B-Parameter Open World Model That Reasons and Generates Robot Actions On-Device

Cosmos 3 Edge: A 4B-Parameter Open World Model That Reasons and Generates Robot Actions On-Device">

NVIDIA has released Cosmos 3 Edge , a 4-billion-parameter open world model built to run on-device. It helps robots and vision AI agents understand surroundings, reason in real time, and generate robot actions locally.

The Cosmos 3 family included Cosmos 3 Nano (16B) and Cosmos 3 Super (64B) shipped on May 31, 2026 at GTC Taipei . Edge is the third and smallest tier, at roughly one-sixteenth the size of Super.

The problem is specific. Machines operate at the edge in factories, warehouses, and hospitals. They need data center–level performance on memory-constrained systems. Cosmos 3 Edge targets that gap.

A world model learns how an environment changes over time. It represents objects, motion, spatial relationships, and the effects of actions.

Consider a robot reaching for an object. Recognizing the object is only the first step. The robot must also track where the object is, how its gripper moves, and what happens on contact. A world model reasons about these relationships. It can predict the visual result of an action, infer the action that caused a change, or generate an action to reach a goal.

Cosmos 3 Edge brings these capabilities into one on-device model. Its shared representation lets a system understand the current world state, simulate possible futures, and connect those futures to actions.

Cosmos 3 uses a Mixture-of-Transformers architecture with two towers, described in the NVIDIA’s technical report .

The autoregressive tower processes vision and text tokens for understanding and reasoning. The diffusion tower processes vision, audio, and action tokens for prediction, generation, and neural simulation.

The two towers keep separate normalization layers and multilayer perceptrons. They share multimodal attention layers, which align information across language, video, audio, and action. This lets the model reason about a scene before it generates an output.

The attention pattern adapts to each modality. Language uses causal attention, where each token attends to earlier tokens. Diffusion tokens attend more broadly to the available context, supporting coherent prediction and generation. Depending on the task, the model emits reasoning tokens from the autoregressive tower, or denoised video and action tokens from the diffusion tower.

Cosmos 3 Edge uses a 2B dense transformer for its reasoner, and follows Qwen3-VL-compatible message conventions for image and video inputs, per the Cosmos GitHub repository .