--- title: "Alpamayo 2 Edge" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ee30e2b4-7cdf-4933-b8a0-f9b993f16dd0" updated: "2026-11-12" --- # Alpamayo 2 Edge > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads multi-camera driving footage, a navigation instruction and motion history, then outputs a driving trajectory with a written reason, and answers questions about the scene. For autonomous vehicle developers and researchers. Alpamayo 2 Edge is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by nvidia ## About ### What it is Alpamayo 2 Edge is a vision-language-action model from NVIDIA for autonomous driving research. It reads camera images, a text instruction and the vehicle's recent motion, then returns a planned path and a written explanation of its reasoning. You can also ask it open questions about what a driving scene shows. It builds on the Cosmos 3 Edge backbone. NVIDIA positions it as a base for fine-tuning, and it can be deployed in cars on the DRIVE AGX Thor platform. ### Who it suits Developers and researchers who work on autonomous vehicle systems and need a compact reasoning model for planning and decision-making. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Automotive | Autonomous driving software teams | Fine-tune the model for their vehicle and sensors | Adapt it to a different camera layout | | Research | Autonomous vehicle researchers | Study the written reasons next to each trajectory | Compare reasoning texts across sampled candidates | | Embedded systems | Teams deploying in vehicles | Run a compact model on in-vehicle hardware | Target DRIVE AGX Thor | Skip it if you want a general chatbot or an image tool, since it is built around driving scenes and camera input. ### Where to use it - Windows, macOS or Linux with Python, to download the files - NVIDIA DRIVE AGX Thor, the in-vehicle platform the card names for deployment - Fine-tuning workflows built on the separate NVIDIA source code repository | Use case | Who | What they get back | | --- | --- | --- | | Trajectory prediction | Planning engineers | 64 waypoints from 0.1 through 6.4 seconds, each with a position and rotation | | Causal explanations | Researchers | Written reasoning for each trajectory candidate | | Scene questions | Developers | Free-form answers about what the cameras see | ### How to set it up - Python and pip to run the download commands - Camera frames with capture timestamps, since the prompt encodes relative times - The inference code from the NVIDIA source repository, which the card links as its code 1. Install the Hugging Face tools. 2. Download the model files. 3. Get the inference code from the NVIDIA source repository and follow it. ```bash pip install -U huggingface_hub hf download nvidia/Alpamayo2-Edge ``` ### How to use it Prepare six camera views over several time steps, a navigation instruction as text and 20 past ego poses at 0.1-second steps. The card says to turn thinking off when applying the chat template, because the template turns it on by default. ```text enable_thinking=False ``` | Mode | What it does | | --- | --- | | Trajectory prediction | Predicts a path from six camera views, 22 frames in all, plus the instruction and ego poses | | Free-form visual question answering | Answers open questions from the same six cameras at four frames each | ### Example You supply the six camera views, an instruction such as turning at the next junction, and the recent ego poses. The model returns a 64-waypoint path out to 6.4 seconds and a text explanation of why it chose that path. ### For newcomers and experts If you are new to this, think of it as a driving planner that explains itself. Start by downloading the files and reading the NVIDIA code to see how camera frames are prepared. If you know the field, it is a VLA model with a Cosmos 3 Edge backbone and a diffusion-based action decoder. The action expert predicts acceleration and curvature per step, a unicycle model integrates them, and each trajectory candidate carries its own sampled reasoning. Six-camera frames and ego pose history → Navigation instruction as text → Backbone writes the driving reasoning → Action expert predicts acceleration and curvature → Unicycle model builds the trajectory ### Good to know - Weights: about 5.3 GB in BF16, according to Hugging Face - NVIDIA frames it as a base to fine-tune and deploy, so treat it as a starting point and not a finished driving product - Text output varies in length, and the trajectory output holds XYZ positions and 3x3 rotation matrices Parameters by part, in billions | Label | Value | | --- | --- | | Language model | 1.96 billion | | Vision encoder | 0.41 billion | | Projector | 0.08 billion | | Action expert | 0.2 billion | - License: OpenMDW-1.1 for the model weights, and Apache License 2.0 for the source code - Cost to run: none named, though in-vehicle deployment targets NVIDIA DRIVE AGX Thor hardware - Data it reaches: camera images, text instructions and ego motion history you supply - Not for: the source lists no exclusions ## README ````markdown # Alpamayo 2 Edge Reads multi-camera driving footage, a navigation instruction and motion history, then outputs a driving trajectory with a written reason, and answers questions about the scene. For autonomous vehicle developers and researchers. Made by nvidia on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Alpamayo 2 Edge is a vision-language-action model from NVIDIA for autonomous driving research. It reads camera images, a text instruction and the vehicle's recent motion, then returns a planned path and a written explanation of its reasoning. You can also ask it open questions about what a driving scene shows. It builds on the Cosmos 3 Edge backbone. NVIDIA positions it as a base for fine-tuning, and it can be deployed in cars on the DRIVE AGX Thor platform. Who it suits: Developers and researchers who work on autonomous vehicle systems and need a compact reasoning model for planning and decision-making. ```table Industry | Who uses it | How they use it | Example Automotive | Autonomous driving software teams | Fine-tune the model for their vehicle and sensors | Adapt it to a different camera layout Research | Autonomous vehicle researchers | Study the written reasons next to each trajectory | Compare reasoning texts across sampled candidates Embedded systems | Teams deploying in vehicles | Run a compact model on in-vehicle hardware | Target DRIVE AGX Thor ``` Skip it if you want a general chatbot or an image tool, since it is built around driving scenes and camera input. Where to use it: - Windows, macOS or Linux with Python, to download the files - NVIDIA DRIVE AGX Thor, the in-vehicle platform the card names for deployment - Fine-tuning workflows built on the separate NVIDIA source code repository ```table Use case | Who | What they get back Trajectory prediction | Planning engineers | 64 waypoints from 0.1 through 6.4 seconds, each with a position and rotation Causal explanations | Researchers | Written reasoning for each trajectory candidate Scene questions | Developers | Free-form answers about what the cameras see ``` How to set it up: - Python and pip to run the download commands - Camera frames with capture timestamps, since the prompt encodes relative times - The inference code from the NVIDIA source repository, which the card links as its code 1. Install the Hugging Face tools. 2. Download the model files. 3. Get the inference code from the NVIDIA source repository and follow it. ```bash pip install -U huggingface_hub hf download nvidia/Alpamayo2-Edge ``` How to use it: Prepare six camera views over several time steps, a navigation instruction as text and 20 past ego poses at 0.1-second steps. The card says to turn thinking off when applying the chat template, because the template turns it on by default. ```text enable_thinking=False ``` ```table Mode | What it does Trajectory prediction | Predicts a path from six camera views, 22 frames in all, plus the instruction and ego poses Free-form visual question answering | Answers open questions from the same six cameras at four frames each ``` Example: You supply the six camera views, an instruction such as turning at the next junction, and the recent ego poses. The model returns a 64-waypoint path out to 6.4 seconds and a text explanation of why it chose that path. For newcomers and experts: If you are new to this, think of it as a driving planner that explains itself. Start by downloading the files and reading the NVIDIA code to see how camera frames are prepared. If you know the field, it is a VLA model with a Cosmos 3 Edge backbone and a diffusion-based action decoder. The action expert predicts acceleration and curvature per step, a unicycle model integrates them, and each trajectory candidate carries its own sampled reasoning. ```flow Six-camera frames and ego pose history Navigation instruction as text Backbone writes the driving reasoning Action expert predicts acceleration and curvature Unicycle model builds the trajectory ``` Good to know: - Weights: about 5.3 GB in BF16, according to Hugging Face - NVIDIA frames it as a base to fine-tune and deploy, so treat it as a starting point and not a finished driving product - Text output varies in length, and the trajectory output holds XYZ positions and 3x3 rotation matrices ```chart Title: Parameters by part, in billions Language model: 1.96 billion Vision encoder: 0.41 billion Projector: 0.08 billion Action expert: 0.2 billion ``` - License: OpenMDW-1.1 for the model weights, and Apache License 2.0 for the source code - Cost to run: none named, though in-vehicle deployment targets NVIDIA DRIVE AGX Thor hardware - Data it reaches: camera images, text instructions and ego motion history you supply - Not for: the source lists no exclusions ## On Hugging Face, 13 November 2026 - Task: robotics - Parameters: 2.7B - Weights: about 5.3 GB in BF16; about 1.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 921 - Likes: 41 - License: openmdw-1.1 - Languages: en - Based on: nvidia/Cosmos3-Edge - Fine-tunes: 1 - Tags: safetensors, alpamayo2_edge, alpamayo, autonomous-driving, vision-language-action, pytorch, robotics ## Links - Model: https://huggingface.co/nvidia/Alpamayo2-Edge - Files: https://huggingface.co/nvidia/Alpamayo2-Edge/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/Alpamayo2-Edge ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [nvidia/Alpamayo2-Edge on Hugging Face](https://huggingface.co/nvidia/Alpamayo2-Edge) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Alpamayo 2 Edge (this page) | 921 | 41 | 2.7B | openmdw-1.1 | 2026-11-10 | | [Jocasta-diff-VLA](https://mindket.com/p/eb993a40-037d-4560-917d-fc85a9c3faa9) | 36 | 33 | — | apache-2.0 | 2026-11-06 | | [π₀.₅ (Pi05) (LeRobot)](https://mindket.com/p/5d6d125f-e079-443d-9e49-b579ca0d2ae1) | 21,996 | 119 | 3.6B | gemma | 2026-10-06 | | [Microduck Policies](https://mindket.com/p/e6c633f4-6450-40a5-90d0-834317954451) | 67,163 | 54 | — | apache-2.0 | 2026-11-12 | | [ActionCodec2](https://mindket.com/p/22d4deb5-757c-4521-9abe-90339b09bf0a) | 217 | 3 | — | apache-2.0 | 2026-10-29 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · Autonomous Bay fellow · 2026-11-12](https://bsky.app/profile/autonomousbay.bsky.social/post/3n24lgymvvs2x): huggingface.co/nvidia/Alpam... - [Bluesky · Object Detection fellow · 2026-11-12](https://bsky.app/profile/objectdetection.bsky.social/post/3n24lcsz6o22x): huggingface.co/nvidia/Alpam... - [Bluesky · Autonomous Robot fellow · 2026-11-12](https://bsky.app/profile/autonomousrobot.bsky.social/post/3n24lausemc2x): huggingface.co/nvidia/Alpam... - [Bluesky · Autonomous Device fellow · 2026-11-12](https://bsky.app/profile/autonomousdevice.bsky.social/post/3n24l6vwths2x): huggingface.co/nvidia/Alpam... - [Bluesky · Autonomous Vehicle fellow · 2026-11-12](https://bsky.app/profile/autonomousvehicle.bsky.social/post/3n24l52iodc2x): huggingface.co/nvidia/Alpam... - [Bluesky · Small \& Smart Dev fellow · 2026-11-12](https://bsky.app/profile/small8smart.dev/post/3n24ktyqyxs2x): huggingface.co/nvidia/Alpam... - [Bluesky · Autonomous Robot Development fellow · 2026-11-12](https://bsky.app/profile/autonomousrobot.dev/post/3n24kmwdof22x): huggingface.co/nvidia/Alpam... - [Bluesky · Autonomous Vehicle Development fellow · 2026-11-12](https://bsky.app/profile/autonomousvehicle.dev/post/3n24i7grq322x): huggingface.co/nvidia/Alpam... ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2026-08-12 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/Alpamayo2-Edge on Hugging Face](https://huggingface.co/nvidia/Alpamayo2-Edge) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ee30e2b4-7cdf-4933-b8a0-f9b993f16dd0) - [Its page on Mindket](https://mindket.com/p/ee30e2b4-7cdf-4933-b8a0-f9b993f16dd0)