Movie Gen Audio
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
- Meta AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 4 October 2024
- Authors
- Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha, Ann Lee, Apoorv Vyas, Bowen Shi, Chih-Yao Ma, Ching-Yao Chuang, David Yan, Dhruv Choudhary, Dingkang Wang, Geet Sethi, Guan Pang, Haoyu Ma, Ishan Misra, Ji Hou, Jialiang Wang, Kiran Jagadeesh, Kunpeng Li, Luxin Zhang, Mannat Singh, Mary Williamson, Matt Le, Mitesh Kumar Singh, Peizhao Zhang, Peter Vajda, Quentin Duval, Rohit Gir…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Audio
- Task
- 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
- 13B
- Training data
- tokens
13B
It was trained on O(1k) hours of audio
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
- 1.4 × 10²³ FLOP
- How it was established
- Hardware
Pre trained for 14 days on 384 H100s. I assumed a 0.3 utilization rate
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
- Chips used
- 384
- Wall-clock time
- 360 hours (15 days)
- Power draw
- 529.4 kW
14 days of pretraining, 1 day of fine tuning
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.
- Likely above 10²³ FLOP
- Yes
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Movie Gen: A Cast of Media Foundation Models
- Last updated
- 28 November 2025
What the numbers mean
Background
Movie Gen Audio was published by Meta AI, in the country recorded as United States of America, during October 2024. The publishing organisation is categorised as industry.
It works in the domain of Audio, and is recorded as performing the task of audio generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The training run consumed about 1.4 × 10²³ FLOP, on hardware recorded as NVIDIA H100 SXM5 80GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Movie Gen Audio — common questions
Movie Gen Audio— what is it used for?
It works in the domain of Audio, and is recorded as handling the task of 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.
Movie Gen Audio— how much compute was used to train it?
Training consumed around 1.4 × 10²³ FLOP, on hardware recorded as 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.
Movie Gen Audio— 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.
Movie Gen Audio— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Movie Gen Audio— how many parameters does it have?
It has a parameter count of 13B. 13B. 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.
Movie Gen Audio— who created it?
It was published by Meta AI, based in United States of America, an organisation categorised as industry.
Movie Gen Audio— when was it released?
It was published in October 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.