Veo
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 14 May 2024
- Authors
- Abhishek Sharma, Adams Yu, Ali Razavi, Andeep Toor, Andrew Pierson, Ankush Gupta, Austin Waters, Aäron van den Oord, Daniel Tanis, Dumitru Erhan, Eric Lau, Eleni Shaw, Gabe Barth-Maron, Greg Shaw, Han Zhang, Henna Nandwani, Hernan Moraldo, Hyunjik Kim, Irina Blok, Jakob Bauer, Jeff Donahue, Junyoung Chung, Kory Mathewson, Kurtis David, Lasse Espeholt, Marc van Zee, Matt McGill, Medhini Narasimhan,…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video, Vision
- Task
- Video generation, Text-to-video, Image-to-video
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Hosted access (no API)
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- We’re introducing Veo, our most capable model for generating high-definition video
- Last updated
- 28 November 2025
What the numbers mean
Background
Veo was published by Google DeepMind, in United States of America, in May 2024. industry is the category the publisher falls under.
It works in Video, Vision, and is recorded as doing video generation, Text-to-video, Image-to-video.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Veo — common questions
What GPU do I need to run Veo?
None. Veo is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Veo open source?
No. Veo has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Veo have?
No parameter count has been published for Veo, which is why no memory or speed figure appears on this page.
Who created Veo?
Veo was published by Google DeepMind, based in United States of America, categorised as industry.
When was Veo released?
Veo was published in May 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Veo used for?
Veo works in Video, Vision, and is recorded as handling video generation, Text-to-video, Image-to-video. These are the areas it was designed around; they describe intent rather than a hard boundary.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.