ByteDance Seaweed
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
- ByteDance
- Organisation type
- Industry
- Country
- China
- Published
- 24 September 2024
- Authors
- ByteDance
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video
- Task
- Video generation, 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
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 5.8 × 10²² FLOP
2.394e9 * 6e13 * 0.4 = 5.75e22
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)
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- ByteDance enters AI video race with Doubao’s PixelDance and Seaweed
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
ByteDance Seaweed was published by ByteDance, in the country recorded as China, during September 2024. The category the publisher falls under is industry.
It works in the domain of Video, and is recorded as performing the task of video generation, Text-to-video.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Producing it required arithmetic totalling around 5.8 × 10²² FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
ByteDance Seaweed — common questions
ByteDance Seaweed— what is it used for?
It works in the domain of Video, and is recorded as handling the task of video generation, 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.
ByteDance Seaweed— how much compute was used to train it?
Training consumed around 5.8 × 10²² FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
ByteDance Seaweed— what GPU do I need to run it?
None. This 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.
ByteDance Seaweed— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
ByteDance Seaweed— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
ByteDance Seaweed— who created it?
It was published by ByteDance, based in China, an organisation categorised as industry.
ByteDance Seaweed— when was it released?
It was published in September 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.
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.