Veo 3
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
- 21 May 2025
- Authors
- Abhishek Sharma, Alina Kuznetsova, Ali Razavi, Aleksander Holynski, Alina Kuznetsova, Ankush Gupta, Austin Waters, Ben Poole, Daniel Tanis, Derek Gasaway, Dumitru Erhan, Enric Corona, Frank Belletti, Gabe Barth-Maron, Hakan Erdogan, Henna Nandwani, Hernan Moraldo, Ilya Figotin, Igor Saprykin, Jason Baldridge, Jeff Donahue, Jimmy Shi, Kurtis David, Mai Gimenez, Medhini Narasimhan, Miaosen Wang, Min…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video, Vision
- Task
- Video generation, Image-to-video, Text-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
- API access
- Training code
- Unreleased
https://cloud.google.com/vertex-ai/generative-ai/docs/models/veo/3-0-generate-preview
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
https://deepmind.google/models/veo/evals/
Sources
Where this record came from and when it was last checked.
- Reference
- Our state-of-the-art video generation model
- Last updated
- 28 November 2025
What the numbers mean
Background
Veo 3 was published by Google DeepMind, in United States of America, in May 2025. The organisation is categorised as industry.
It works in Video, Vision, and is recorded as doing video generation, Image-to-video, Text-to-video.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Veo 3 — common questions
Is Veo 3 open source?
No. Veo 3 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Veo 3 have?
No parameter count has been published for Veo 3, which is why no memory or speed figure appears on this page.
Who created Veo 3?
Veo 3 was published by Google DeepMind, based in United States of America, categorised as industry.
When was Veo 3 released?
Veo 3 was published in May 2025.
What is Veo 3 used for?
Veo 3 works in Video, Vision, and is recorded as handling video generation, Image-to-video, Text-to-video. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run Veo 3?
None. Veo 3 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.
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.