Zero-shot Monocular Scene Flow (ZeroMSF)
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
- NVIDIA,Brown University
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 20 January 2025
- Authors
- Yiqing Liang, Abhishek Badki, Hang Su, James Tompkin, Orazio Gallo
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- 3D modeling, Driving
- Task
- 3D reconstruction, Self-driving car
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
- Epochs
- 50
"1M annotated training samples across diverse synthetic scenes"
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
- 3.2 × 10¹⁹ FLOP
- How it was established
- Hardware
312000000000000 FLOP / GPU / sec * 8 GPUs * 12 hours * 3600 sec / hour * 0.3 [assumed utilization] = 3.234816e+19 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
- Chips used
- 8
- Wall-clock time
- 12 hours
- Power draw
- 6.3 kW
"All experiments are trained with 8 NVIDIA A100 GPUs for 50 epochs, which take 12 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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
"Code coming!" [July 2025 - still no release]
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
- Zero-Shot Monocular Scene Flow Estimation in the Wild
- Last updated
- 11 February 2026
What the numbers mean
Background
Zero-shot Monocular Scene Flow (ZeroMSF) was published by NVIDIA,Brown University, in United States of America, in January 2025. It comes out of industry,Academia.
It works in 3D modeling, Driving, and is recorded as doing 3D reconstruction, Self-driving car.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training run consumed about 3.2 × 10¹⁹ FLOP, on NVIDIA A100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
Zero-shot Monocular Scene Flow (ZeroMSF) — common questions
When was Zero-shot Monocular Scene Flow (ZeroMSF) released?
Zero-shot Monocular Scene Flow (ZeroMSF) was published in January 2025.
What is Zero-shot Monocular Scene Flow (ZeroMSF) used for?
Zero-shot Monocular Scene Flow (ZeroMSF) works in 3D modeling, Driving, and is recorded as handling 3D reconstruction, Self-driving car. 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 Zero-shot Monocular Scene Flow (ZeroMSF)?
Around 3.2 × 10¹⁹ FLOP, on NVIDIA A100. 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 Zero-shot Monocular Scene Flow (ZeroMSF)?
None. Zero-shot Monocular Scene Flow (ZeroMSF) 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 Zero-shot Monocular Scene Flow (ZeroMSF) open source?
No. Zero-shot Monocular Scene Flow (ZeroMSF) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Zero-shot Monocular Scene Flow (ZeroMSF) have?
No parameter count has been published for Zero-shot Monocular Scene Flow (ZeroMSF), which is why no memory or speed figure appears on this page.
Who created Zero-shot Monocular Scene Flow (ZeroMSF)?
Zero-shot Monocular Scene Flow (ZeroMSF) was published by NVIDIA,Brown University, based in United States of America, categorised as industry,Academia.
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.