Improved motif-scaffolding with SE(3) flow matching
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
- University of Oxford,Massachusetts Institute of Technology (MIT),Microsoft Research AI for Science
- Organisation type
- Academia,Academia,Industry
- Country
- United Kingdom of Great Britain and Northern Ireland, United States of America
- Published
- 8 January 2024
- Authors
- Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein design
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
- 16.8M
- 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
- 1.6 × 10¹⁹ FLOP
- How it was established
- Hardware
1. Hardware: 2x NVIDIA RTX A6000 (3.87e13 FLOP/s per GPU) 2. Training duration: 6 days = 518,400 seconds (directly provided) 3. Utilization: 40% 4. Calculation: 2 GPUs × 3.87e13 FLOP/s × 518,400s × 0.40 = 1.6e19 FLOPs
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 RTX A6000
- Chips used
- 2
- Wall-clock time
- 144 hours
- Power draw
- 1.2 kW
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
- Improved motif-scaffolding with SE(3) flow matching
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
Improved motif-scaffolding with SE(3) flow matching was published by University of Oxford,Massachusetts Institute of Technology (MIT),Microsoft Research AI for Science, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during January 2024. The category the publisher falls under is academia,Academia,Industry.
It works in the domain of Biology, and is recorded as performing the task of protein design.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took a computation budget of roughly 1.6 × 10¹⁹ FLOP, on hardware recorded as NVIDIA RTX A6000. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Improved motif-scaffolding with SE(3) flow matching — common questions
Improved motif-scaffolding with SE(3) flow matching— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein design. 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.
Improved motif-scaffolding with SE(3) flow matching— how much compute was used to train it?
Training consumed around 1.6 × 10¹⁹ FLOP, on hardware recorded as NVIDIA RTX A6000. 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.
Improved motif-scaffolding with SE(3) flow matching— 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.
Improved motif-scaffolding with SE(3) flow matching— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Improved motif-scaffolding with SE(3) flow matching— how many parameters does it have?
It has a parameter count of 16.8M. 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.
Improved motif-scaffolding with SE(3) flow matching— who created it?
It was published by University of Oxford,Massachusetts Institute of Technology (MIT),Microsoft Research AI for Science, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia,Academia,Industry.
Improved motif-scaffolding with SE(3) flow matching— when was it released?
It was published in January 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.