Stable Video 3D (SV3D)
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- Stability AI
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 18 March 2024
- Authors
- Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, Varun Jampani
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, 3D modeling
- Task
- 3D reconstruction
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
"All three models (SV3Du, SV3Dc, SV3Dp) are trained for 105k iterations in total (SV3Dp is trained unconditionally for 55k iterations and conditionally for 50k iterations), with an effective batch size of 64 on 4 nodes of 8 80GB A100 GPUs for around 6 days."
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
- 5.2 × 10²⁰ FLOP
- How it was established
- Hardware
4 nodes * 8 GPUs * 144 hours * 3600 sec / hour * 312000000000000 FLOP / GPU / hour * 0.3 [assumed utilization] / 3 models = 5.1757056e+20 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 A100 SXM4 80 GB
- Chips used
- 32
- Wall-clock time
- 144 hours
- Power draw
- 25.3 kW
"on 4 nodes of 8 80GB A100 GPUs for around 6 days." 6*24 = 144 hours
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
- Open — downloadable
- Model access
- Open weights (non-commercial)
- Training code
- Unreleased
- Hugging Face
- stabilityai
License: Stability AI Community License. NC https://huggingface.co/stabilityai/sv3d
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
Stable Video 3D (SV3D) was published by Stability AI, in United Kingdom of Great Britain and Northern Ireland, in March 2024. It comes out of industry.
It works in Vision, 3D modeling, and is recorded as doing 3D reconstruction.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the stabilityai organisation on Hugging Face.
Training and provenance
Training it took roughly 5.2 × 10²⁰ FLOP of computation, on NVIDIA A100 SXM4 80 GB — a measure of what producing the model cost, not of how fast it answers.
Answers
Stable Video 3D (SV3D) — common questions
How much compute was used to train Stable Video 3D (SV3D)?
Around 5.2 × 10²⁰ FLOP, on NVIDIA A100 SXM4 80 GB. 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 Stable Video 3D (SV3D)?
We cannot say. Stable Video 3D (SV3D) has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is Stable Video 3D (SV3D) open source?
Its weights are published, so Stable Video 3D (SV3D) can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does Stable Video 3D (SV3D) have?
No parameter count has been published for Stable Video 3D (SV3D), which is why no memory or speed figure appears on this page.
Who created Stable Video 3D (SV3D)?
Stable Video 3D (SV3D) was published by Stability AI, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.
When was Stable Video 3D (SV3D) released?
Stable Video 3D (SV3D) was published in March 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 Stable Video 3D (SV3D) used for?
Stable Video 3D (SV3D) works in Vision, 3D modeling, and is recorded as handling 3D reconstruction. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download Stable Video 3D (SV3D)?
Its weights are published under the stabilityai organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
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.