Model reference · open weights

Cosmos-Reason2

LLMs nvidia Vision + text 3 builds Its own licence terms 995k dl/mo

Cosmos-Reason2 is an open-weight language model from NVIDIA. Cosmos-Reason2-32B (BF16) weighs 66.7 GB; the smallest configuration that runs it is H100 80 GB.

  • Cosmos-Reason2 is a 2.4B parameter vision language model developed by NVIDIA for physical AI and robotics tasks.
  • It processes text, video, and image inputs to generate text outputs, supporting a context length of up to 256K tokens.
  • The model is released under the NVIDIA Open Model License and is designed for use in video analytics, data curation, and robot planning.

Summary of the nvidia/Cosmos-Reason2-2B model card, 2026-10-01. The estimate below is for another build of the family.

What it is

Released byNVIDIA
Released2025-12-12
Parameters2.4B
VRAM66.7 GB for the weights

What it runs on

Memory and cards for Cosmos-Reason2-32B (BF16)

66.7 GBweights, file size
262 MBcache per 1K tokens
2.2 GBruntime overhead, at least
262,144 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … L40S 48 GB
6 smaller cards
———
A100 80 GB4134K78.2 GB
H100 80 GB2—16K78.1 GB
RTX PRO 6000 Blackwell 96 GB9275K93.8 GB
DGX Spark (GB10) 128 GB unified153125K107 GB
H200 141 GB297239K138 GB
B200 180 GB4711all 256K176 GB
4× RTX 4090 24 GB
tensor parallel
8267K23.4 GB a card
4× RTX 3090 24 GB
tensor parallel
8267K23.4 GB a card
4× RTX 5090 32 GB
tensor parallel
225181K31.0 GB a card
2× H100 80 GB
tensor parallel
358all 256K78.1 GB a card
2× A100 80 GB
tensor parallel
399all 256K78.2 GB a card
4× L40S 48 GB
tensor parallel
4611all 256K44.0 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
171.0 GB77.5 GB
579.6 GB112 GB
886.1 GB138 GB
16103 GB206 GB
32138 GB344 GB
64206 GB619 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
Cosmos-Reason2-2B ↗ 2.4BBF16 4.9 GBRTX 3060 12 GB
Cosmos-Reason2-8B ↗ 8.8BBF16 17.5 GBRTX 4090 24 GB
Cosmos-Reason2-32B (above) ↗ 33.4BBF16 66.7 GBH100 80 GB

From the model card

What NVIDIA says about Cosmos-Reason2

Read the model card

Cosmos | Code

Model Overview

Description:

NVIDIA Cosmos Reason 2 is an open, customizable, 32B-parameter reasoning vision language model (VLM) for physical AI and robotics that enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding and common sense to understand and act in the real world. This model understands space, time, and fundamental physics, and can serve as a planning model to reason what steps an embodied agent might take next.

New features with Cosmos Reason 2:

  • Enhanced physical AI reasoning with improved spatio-temporal understanding and timestamp precision.
  • Supports object detection with 2D/3D point localization and bounding box coordinates with reasoning explanations and labels.
  • Improved long-context understanding up to 256K input tokens.

Use cases:

  • Video analytics AI agents — Extract valuable insights and perform root-cause analysis on massive volumes of video data. These agents can be used to analyze and understand recorded or live video streams across city and industrial operations. Jumpstart your development of video analytics AI agents by using the NVIDIA Blueprint for video search and summarization (VSS) with Cosmos Reason as the VLM.
  • Data curation and annotation — Enable developers to automate high-quality curation and annotation of massive, diverse training datasets. Experience NVIDIA Cosmos Curator, powered by Cosmos Reason, a framework that enables developers to quickly filter, annotate, and deduplicate large amounts of sensor data necessary for physical AI development.
  • Robot planning and reasoning — Act as the brain for deliberate, methodical decision-making in a robot vision language action (VLA) model. Now robots such as humanoids and autonomous vehicles (AV) can interpret environments and complex commands, break them down into tasks and execute them using common sense, even in unfamiliar environments. Explore the NVIDIA Isaac GR00T-Dreams blueprint, which generates vast amounts of synthetic trajectory data using NVIDIA Cosmos world foundation models.

Explore the Cosmos Cookbook, a technical guide that delivers end-to-end workflows, implementation recipes, and detailed examples for building, fine-tuning, and deploying Cosmos Reason in production-ready environments.

The model is ready for commercial use.

Model Developer: NVIDIA

Model Versions

The Cosmos-Reason2 includes the following model:

  • Cosmos-Reason2-2B: Given a text prompt and an input video, think and generate the answer with respect to the input text prompt and video.
  • Cosmos-Reason2-8B: Given a text prompt and an input video, think and generate the answer with respect to the input text prompt and video.
  • Cosmos-Reason2-32B: Given a text prompt and an input video, think and generate the answer with respect to the input text prompt and video.

Model Quality Performance

[04/28/2026] For comparative evaluation, we present benchmark scores using the Physical AI Bench Leaderboard.

We also evaluated Cosmos-Reason2 against Qwen3-VL across the following domains and categories: General, Robotics, Self-Driving, and Smart Spaces.

CategoryBenchmarkCosmos-Reason2-32BQwen3-VL-32B-InstructCosmos-Reason2-8BQwen3-VL-8B-InstructCosmos-Reason2-2BQwen3-VL-2B-Instruct
GeneralOverall75.8573.0773.7371.9862.2159.60
BlinkDepth84.6882.2687.9087.1082.2674.19
BlinkSpatial86.7186.7184.6287.4175.5277.62
CVBench88.0186.7485.6085.1778.7478.67
VideoPhy243.9836.5736.8028.2412.337.92
RoboticsOverall60.6055.0656.9053.0845.5242.07
ERQA45.2546.5044.0044.0037.7537.75
CR Common65.8963.4164.2458.4454.3049.67
CR Embodied75.2559.3469.3456.8958.0348.85
Where2Place56.0051.0050.0053.0032.0032.00
Self-DrivingOverall70.1548.0867.8546.3857.3742.73
AV Collision72.0637.6774.0034.0074.3336.33
AV Stop69.3938.7857.1436.7338.7832.65
LingoQA69.0067.8072.4068.4059.0059.20
Smart SpacesOverall77.7947.5569.9642.6664.1436.63
Warehouse AI*77.79

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
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