DRGN-AI
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
- Stanford University,SLAC National Laboratory,Princeton University,Columbia University
- Organisation type
- Academia,Academia,Academia
- Country
- United States of America
- Published
- 2 June 2024
- Authors
- Axel Levy, Michal Grzadkowski, Frédéric Poitevin, Francesca Vallese, Oliver Biggs Clarke, Gordon Wetzstein, Ellen D. Zhong
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Cryo-EM image 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
- 11,639,799,808 tokens
710,437 Table S1
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
- 6.5 × 10¹⁹ FLOP
48*60*60*4*312000000000000*0.3=6.469632e+19
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
- 4
- Wall-clock time
- 48 hours
- Power draw
- 3.2 kW
Table S1
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
- Revealing biomolecular structure and motion with neural ab initio cryo-EM reconstruction
- Last updated
- 28 November 2025
What the numbers mean
Background
DRGN-AI was published by Stanford University,SLAC National Laboratory,Princeton University,Columbia University, in United States of America, in June 2024. The organisation is categorised as academia,Academia,Academia.
It works in Biology, and is recorded as doing cryo-EM image reconstruction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required around 6.5 × 10¹⁹ FLOP of arithmetic, on NVIDIA A100, which is a statement about the training budget rather than about inference.
The training set ran to roughly 11,639,799,808 tokens.
Answers
DRGN-AI — common questions
How many parameters does DRGN-AI have?
No parameter count has been published for DRGN-AI, which is why no memory or speed figure appears on this page.
Who created DRGN-AI?
DRGN-AI was published by Stanford University,SLAC National Laboratory,Princeton University,Columbia University, based in United States of America, categorised as academia,Academia,Academia.
When was DRGN-AI released?
DRGN-AI was published in June 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 DRGN-AI used for?
DRGN-AI works in Biology, and is recorded as handling cryo-EM image reconstruction. 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 DRGN-AI?
Around 6.5 × 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 DRGN-AI?
None. DRGN-AI 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 DRGN-AI open source?
The licensing for DRGN-AI was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.