Alpamayo2-Super Overview

Description:

Alpamayo 2 Super is a Vision-Language-Action (VLA) foundation model with a 32B VLM backbone and a 2B diffusion-based action decoder (total 34B parameters), designed to tackle multiple autonomous vehicle (AV) development tasks. Alpamayo 2 Super was developed by NVIDIA as part of the broader Alpamayo Open Platform and can be integrated into autonomous driving software in the cloud for advanced end-to-end perception, reasoning, and motion planning.
\nAlpamayo2-Super was developed by NVIDIA as a part of Alpamayo.

This model is ready for commercial/non-commercial use.

License/Terms of Use:

OpenMDW-1.1

Deployment Geography:

Global

Use Case:

Developers and researchers working on autonomous vehicle systems who need a foundation model for perception, planning, and decision-making tasks. Alpamayo 2 Super supports multiple AV development tasks such as trajectory prediction, visual question answering, 2D grounding, and auto-labeling. It enables enterprises to accelerate AV software development with a unified model that combines vision-language and diffusion expertise.

Release Date:

Huggingface: 07/31/2026

Reference(s):

Alpamayo Overview
\nAlpamayo NV Developer Forum
\nOpenMDW-1.1 License
\nNVIDIA Vulnerability Disclosure Program
\nAlpamayo 2 Super Source Code
\nAlpamayo 2 Super GitHub Issues
\nAlpamayo 2 Super on HuggingFace

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Vision-Language-Action (VLA) model with a VLM backbone and diffusion-based action decoder.
\nThis model was developed based on Cosmos 3 Super Reasoner with a diffusion-based action decoder..
\nNumber of model parameters: 34B (3.4*10^10)

Input:

Input Type(s): Text, Image, Video, Other: Egomotion History
\nInput Format(s):

String
Other: RGB
Other: Egomotion History

Input Parameters: One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D)
\nOther Properties Related to Input: The model utilizes special tokens for sequence processing, including <|im_end|> for end-of-sequence and <|endoftext|> for padding, as defined in its tokenizer configuration.

Output:

Output Type(s): Text, Other: Trajectory
\nOutput Format:

Text: String
Trajectory: Vector

Output Parameters: One-Dimensional (1D), Three-Dimensional (3D)
\nOther Properties Related to Output: The trajectory API returns 64 waypoints spanning 0.1 through 6.4 seconds at 0.1-second intervals. Each trajectory contains ego-frame XYZ positions and 3x3 rotation matrices. Text outputs include variable-length Chain-of-Causation reasoning, meta-actions, visual question answers, grounding coordinates, and structured auto-labeling fields.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s): Transformers
\nSupported Hardware Microarchitecture Compatibility: : NVIDIA H100 80GB
\nSupported Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

Alpamayo 2 Super 34B trained

Integrate Alpamayo2-Super using PyTorch (≥2.8), Hugging Face Transformers (≥4.57.1), and DeepSpeed (≥0.17.4). The model can be deployed in autonomous driving software in the cloud for end-to-end perception, reasoning, and motion planning. Integration should follow V-model methodology with iterative unit and system-level testing to ensure safety and compliance.

Training, Testing, and Evaluation Datasets:

Training Dataset:

Alpamayo2 Super's training data comprises a mix of Chain of Causation (CoC) reasoning traces, Cosmos-Reason Physical AI datasets, NVIDIA's internal proprietary autonomous driving data, and public driving data.

Data Modality:

Image (multi-camera)
Text (reasoning traces, navigation guidance)
Other: Trajectory data (egomotion, future waypoints)

Image Training Data Size: More than 1 Billion Images
\nText Training Data Size: Less than a Billion Tokens
\nVideo Training Data Size: 10,000 to 1 Million Hours
\nData Collection Method by dataset: Automatic/Sensors
\nLabeling Method by dataset: Hybrid: Automated/Manually-Labelled
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): The training dataset comprises approximately 115,000 hours of multi-camera driving video with egomotion and trajectory annotations, along with 3.7 million Chain-of-Causation reasoning traces in English. It includes machine-generated data from vehicle sensors (cameras, IMUs, GPS) and synthetic reasoning traces. Sensors used are RGB cameras, inertial measurement units, and GPS.

Testing Dataset:

Link: Proprietary autonomous driving test datasets, closed-loop simulation, on-vehicle road tests.

Data Collection Method by dataset: Automatic/Sensors
\nLabeling Method by dataset: Hybrid: Automated/Manually-Labelled
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): The dataset comprises roughly 115,000 hours of multi-camera driving video with corresponding egomotion and trajectory annotations. It includes roughly 3,700,000 Chain-of-Causation (CoC) reasoning traces that provide decision-grounded, causally linked explanations of driving behaviors. Content includes machine-generated data from vehicle sensors (cameras, IMUs, and GPS) and synthetic reasoning traces. CoC annotations are in English and use a structured format that links driving decisions to causal factors. Sensors include RGB cameras, inertial measurement units, and GPS.

Evaluation Dataset:

Link: Same as Testing Dataset.

Benchmark Score: LingoQA: Lingo-Judge Score=79.2, AlpaSim: AlpaSim Score=1.50 ± 0.13, PhysicalAI-AV: minADE_6=0.911m

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	Metric	Score
LingoQA	Lingo-Judge Score	79.2
AlpaSim	AlpaSim Score	1.50 ± 0.13
PhysicalAI-AV	minADE_6	0.911m

Inference:

Acceleration Engine: PyTorch, Transformers\nHardware Requirements (GPU Architecture, Model): NVIDIA H100 80GB

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
\nPlease make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Get Help

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

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It combines a 32B VLM backbone with a 2B diffusion expert.","# Alpamayo2-Super Overview\n\n## Description: \nAlpamayo 2 Super is a Vision-Language-Action (VLA) foundation model with a 32B VLM backbone and a 2B diffusion-based action decoder (total 34B parameters), designed to tackle multiple autonomous vehicle (AV) development tasks. Alpamayo 2 Super was developed by NVIDIA as part of the broader Alpamayo Open Platform and can be integrated into autonomous driving software in the cloud for advanced end-to-end perception, reasoning, and motion planning. \nAlpamayo2-Super was developed by NVIDIA as a part of Alpamayo. \n\n_This model is ready for commercial/non-commercial use._ \n\n\n### License/Terms of Use:\nOpenMDW-1.1\n\n### Deployment Geography: \nGlobal\n\n### Use Case: \nDevelopers and researchers working on autonomous vehicle systems who need a foundation model for perception, planning, and decision-making tasks. Alpamayo 2 Super supports multiple AV development tasks such as trajectory prediction, visual question answering, 2D grounding, and auto-labeling. It enables enterprises to accelerate AV software development with a unified model that combines vision-language and diffusion expertise. \n \n\n### Release Date:\n**Huggingface:** 07/31/2026 \n\n\n## Reference(s):\n[Alpamayo Overview](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/alpamayo/) \n[Alpamayo NV Developer Forum](https://forums.developer.nvidia.com/c/autonomous-vehicles/alpamayo/766) \n[OpenMDW-1.1 License](https://openmdw.ai/license/1-1/) \n[NVIDIA Vulnerability Disclosure Program](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail) \n[Alpamayo 2 Super Source Code](https://github.com/NVlabs/alpamayo2) \n[Alpamayo 2 Super GitHub Issues](https://github.com/NVlabs/alpamayo2/issues/new/choose) \n[Alpamayo 2 Super on HuggingFace](https://huggingface.co/nvidia/Alpamayo2-Super) \n\n## Model Architecture: \n**Architecture Type:** Transformer \n**Network Architecture:** Vision-Language-Action (VLA) model with a VLM backbone and diffusion-based action decoder. \n**This model was developed based on Cosmos 3 Super Reasoner with a diffusion-based action decoder..** \n**Number of model parameters:** 34B (3.4*10^10) \n \n## Input: \n**Input Type(s):** Text, Image, Video, Other: Egomotion History \n**Input Format(s):**\n* String\n* Other: RGB\n* Other: Egomotion History\n\n**Input Parameters:** One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D) \n**Other Properties Related to Input:** The model utilizes special tokens for sequence processing, including <|im_end|> for end-of-sequence and <|endoftext|> for padding, as defined in its tokenizer configuration. \n \n\n## Output: \n**Output Type(s):** Text, Other: Trajectory \n**Output Format:**\n* Text: String\n* Trajectory: Vector\n\n**Output Parameters:** One-Dimensional (1D), Three-Dimensional (3D) \n**Other Properties Related to Output:** The trajectory API returns 64 waypoints spanning 0.1 through 6.4 seconds at 0.1-second intervals. Each trajectory contains ego-frame XYZ positions and 3x3 rotation matrices. Text outputs include variable-length Chain-of-Causation reasoning, meta-actions, visual question answers, grounding coordinates, and structured auto-labeling fields. \n \n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Software Integration:\n**Runtime Engine(s):** Transformers \n**Supported Hardware Microarchitecture Compatibility:** : NVIDIA H100 80GB \n**Supported Operating System(s):** Linux \n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment. \n\n\n## Model Version(s): \nAlpamayo 2 Super 34B trained \n\nIntegrate Alpamayo2-Super using PyTorch (≥2.8), Hugging Face Transformers (≥4.57.1), and DeepSpeed (≥0.17.4). The model can be deployed in autonomous driving software in the cloud for end-to-end perception, reasoning, and motion planning. Integration should follow V-model methodology with iterative unit and system-level testing to ensure safety and compliance. \n \n## Training, Testing, and Evaluation Datasets: \n\n## Training Dataset:\nAlpamayo2 Super's training data comprises a mix of Chain of Causation (CoC) reasoning traces, Cosmos-Reason Physical AI datasets, NVIDIA's internal proprietary autonomous driving data, and public driving data.\n\n**Data Modality:**\n* Image (multi-camera) \n* Text (reasoning traces, navigation guidance)\n* Other: Trajectory data (egomotion, future waypoints)\n\n\n**Image Training Data Size:** More than 1 Billion Images \n**Text Training Data Size:** Less than a Billion Tokens \n**Video Training Data Size:** 10,000 to 1 Million Hours \n**Data Collection Method by dataset:** Automatic/Sensors \n**Labeling Method by dataset:** Hybrid: Automated/Manually-Labelled \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** The training dataset comprises approximately 115,000 hours of multi-camera driving video with egomotion and trajectory annotations, along with 3.7 million Chain-of-Causation reasoning traces in English. It includes machine-generated data from vehicle sensors (cameras, IMUs, GPS) and synthetic reasoning traces. Sensors used are RGB cameras, inertial measurement units, and GPS.\n\n### Testing Dataset:\n**Link:** Proprietary autonomous driving test datasets, closed-loop simulation, on-vehicle road tests.\n\n**Data Collection Method by dataset:** Automatic/Sensors \n**Labeling Method by dataset:** Hybrid: Automated/Manually-Labelled \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** The dataset comprises roughly 115,000 hours of multi-camera driving video with corresponding egomotion and trajectory annotations. It includes roughly 3,700,000 Chain-of-Causation (CoC) reasoning traces that provide decision-grounded, causally linked explanations of driving behaviors. Content includes machine-generated data from vehicle sensors (cameras, IMUs, and GPS) and synthetic reasoning traces. CoC annotations are in English and use a structured format that links driving decisions to causal factors. Sensors include RGB cameras, inertial measurement units, and GPS.\n\n### Evaluation Dataset:\n**Link:** Same as Testing Dataset.\n\n**Benchmark Score:** LingoQA: Lingo-Judge Score=79.2, AlpaSim: AlpaSim Score=1.50 ± 0.13, PhysicalAI-AV: minADE_6=0.911m \n| Benchmark | Metric | Score |\n|-----------|--------|-------|\n| LingoQA | Lingo-Judge Score | 79.2 |\n| AlpaSim | AlpaSim Score | 1.50 ± 0.13 |\n| PhysicalAI-AV | minADE_6 | 0.911m | \n\n**Data Collection Method by dataset:** Automatic/Sensors \n**Labeling Method by dataset:** Hybrid: Automated/Manually-Labelled \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** The dataset comprises roughly 115,000 hours of multi-camera driving video with corresponding egomotion and trajectory annotations. It includes roughly 3,700,000 Chain-of-Causation (CoC) reasoning traces that provide decision-grounded, causally linked explanations of driving behaviors. Content includes machine-generated data from vehicle sensors (cameras, IMUs, and GPS) and synthetic reasoning traces. CoC annotations are in English and use a structured format that links driving decisions to causal factors. Sensors include RGB cameras, inertial measurement units, and GPS.\n\n## Inference:\n**Acceleration Engine:** PyTorch, Transformers\n**Hardware Requirements (GPU Architecture, Model):** NVIDIA H100 80GB\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \nPlease make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included. \nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
\n\n## Get Help\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","displayName","Alpamayo-2-Super",["D",1792434470461],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/nvidia/models/alpamayo-2-super","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_489":490,"_491":492,"_493":494,"_495":32,"_496":497},"isMultinodeEnabled","isMultiArchitecture","bias","# Bias Subcard\n\n## Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing:\n\nAge, Ethnicity, Gender, Skin Color\n\n## Measures taken to mitigate against unwanted bias:\n\nNot Applicable\n\n## Bias Metric (If Measured):\n\nNot Applicable","explainability","# Explainability Subcard\n\n## Intended Task/Domain:\n\nAdvanced Driver Assistance System\n\n## Model Type:\n\nTransformer\n\n## Intended Users:\n\nDevelopers and researchers working on autonomous vehicle systems for end-to-end perception, reasoning, and motion planning in autonomous driving software.\n\n## Output:\n\nTypes: Text. Formats: Text: String, Trajectory: Vector\n\n## Describe how the model works:\n\nThis Vision-Language-Action (VLA) model uses a transformer-based VLM backbone for processing multimodal inputs and a diffusion-based action decoder for generating outputs, enabling end-to-end perception, reasoning, and motion planning for autonomous vehicles.\n\n## Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:\n\nNot Applicable\n\n## Technical Limitations and Mitigation:\n\nThe Model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text, producing socially unacceptable or undesirable text.\n\n## Verified to have met prescribed NVIDIA quality standards:\n\nYes\n\n## Performance Metrics:\n\nAccuracy (Top-1), F-1 Score, Throughput & Latency\n\n## Potential Known Risks:\n\nThis model may [describe potential failure mode based on model type]\n\n## Terms of Use/Licensing:\n\nNone","privacy","# Privacy Subcard\n\n## Generatable or reverse engineerable personal data?\n\nNone\n\n## Personal data used to create this model?\n\nNot provided.\n\n## Was consent obtained for any personal data used?\n\nNot Applicable\n\n## How often is dataset reviewed?\n\nNot provided.\n\n## Is a mechanism in place to honor data subject right of access or deletion of personal data?\n\nNot Applicable\n\n## If personal data was collected for the development of the model, was it collected directly by NVIDIA?\n\nNot Applicable\n\n## If personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\n\nNot Applicable\n\n## If personal data was collected for the development of this AI model, was it minimized to only what was required?\n\nNot Applicable\n\n## Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model?\n\nNot provided.\n\n## Is there provenance for all datasets used in training?\n\nYes\n\n## Does data labeling (annotation, metadata) comply with privacy laws?\n\nNot provided.\n\n## Is data compliant with data subject requests for data correction or removal, if such a request was made?\n\nNot provided.\n\n## Applicable Privacy Policy\n\nhttps://www.nvidia.com/en-us/about-nvidia/privacy-policy/","safetyAndSecurity","# Safety & Security Subcard\n\n## Model Application Field(s):\n\nText Generation\n\n## Describe the life critical impact (if present).\n\nNot Applicable\n\n## Use Case Restrictions:\n\nAbide by [Insert Approved License Agreement]\n\n## Model and dataset restrictions:\n\nThe Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.","featureLabels",[480,481],"displayLabels",[469,470],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_431":478,"_472":478,"_473":479,"_475":476},{"_431":471,"_472":471,"_473":474,"_475":476},"NSPECT-7GMJ-3W2W","value","filter",{"_477":476,"_472":471},"category","label","field","False",{"_477":476,"_472":478},{"_431":487,"_472":487,"_473":488,"_475":485},{"_431":482,"_472":483,"_473":484,"_475":485},"NVIDIA NIM","nimmcro_nvidia_nim",{"_477":486,"_472":483},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_477":476,"_472":487},"governingTerms","

GOVERNING DOWNLOAD TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.

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GOVERNING DOWNLOAD TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.

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Name	Size	Updated	Actions

HF_SOURCE	179 B	October 19, 2026 UTC	curl wget Download