Seaweed-7B
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
- 11 April 2025
- Authors
- Team Seawead, Ceyuan Yang, Zhijie Lin, Yang Zhao, Shanchuan Lin, Zhibei Ma, Haoyuan Guo, Hao Chen, Lu Qi, Sen Wang, Feng Cheng, Feilong Zuo Xuejiao Zeng, Ziyan Yang, Fangyuan Kong, Zhiwu Qing, Fei Xiao, Meng Wei, Tuyen Hoang, Siyu Zhang, Peihao Zhu, Qi Zhao, Jiangqiao Yan, Liangke Gui, Sheng Bi, Jiashi Li, Yuxi Ren, Rui Wang, Huixia Li, Xuefeng Xiao, Shu Liu, Feng Ling, Heng Zhang, Houmin Wei, Hua…
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, Image-to-video, Audio generation
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.
- Parameters
- 7B
- 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
- 9 × 10²³ FLOP
- How it was established
- Hardware
989400000000000 FLOP / GPU / sec [H100 reported, bf16 assumed] * 665000 GPU-hours * 3600 sec / hour * 0.38 [reported utilization] = 9.0007697e+23 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H100 SXM5 80GB
- Chip-hours
- 665,000
- Hardware utilisation
- MFU 38.0%
"Finally, we optimize GPU utilization by implementing fused CUDA kernels to streamline fragmented I/O operations. As a result, Seaweed-7B achieves a Model FLOPs Utilization (MFU) of 38% in distributed training at large-scale."
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
- Unreleased
- 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
- Confident
- Citations
- 83
Sources
Where this record came from and when it was last checked.
- Reference
- Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Seaweed-7B was published by ByteDance, in China, in April 2025. It comes out of industry.
It works in Video, and is recorded as doing video generation, Text-to-video, Image-to-video, Audio generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Training it took roughly 9 × 10²³ FLOP of computation, on NVIDIA H100 SXM5 80GB — a measure of what producing the model cost, not of how fast it answers.
Answers
Seaweed-7B — common questions
How many parameters does Seaweed-7B have?
Seaweed-7B has 7B parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Seaweed-7B?
Seaweed-7B was published by ByteDance, based in China, categorised as industry.
When was Seaweed-7B released?
Seaweed-7B was published in April 2025.
What is Seaweed-7B used for?
Seaweed-7B works in Video, and is recorded as handling video generation, Text-to-video, Image-to-video, Audio generation. 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.
How much compute was used to train Seaweed-7B?
Around 9 × 10²³ FLOP, on NVIDIA H100 SXM5 80GB. 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.
What GPU do I need to run Seaweed-7B?
None. Seaweed-7B 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 Seaweed-7B open source?
No. Seaweed-7B has not had its weights published, so it exists only as a service controlled by its owner.
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.