NAEPro
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 California Santa Barbara (UCSB),Massachusetts Institute of Technology (MIT),Carnegie Mellon University (CMU)
- Organisation type
- Academia,Academia,Academia
- Country
- United States of America
- Published
- 6 October 2023
- Authors
- Zhenqiao Song, Yunlong Zhao, Wenxian Shi, Yang Yang, Lei Li
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.
- Training data
- tokens
- Epochs
- 100
β-lactamase calculation: 5,427 proteins × 286 residues = 1,552,122 residues Myoglobin calculation: 3,381 proteins × 153 residues = 517,143 residues Total datapoints: 1,552,122 + 517,143 = 2,069,265 residues (≈ 2.1e6) - These are two separate models, using β-lactamase
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.
- How it was established
- Hardware
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
- 1
- Power draw
- 327 W
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
- Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design
- Last updated
- 28 November 2025
What the numbers mean
What this model is
NAEPro was published by University of California Santa Barbara (UCSB),Massachusetts Institute of Technology (MIT),Carnegie Mellon University (CMU), in United States of America, in October 2023. academia,Academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein design.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
NAEPro — common questions
Is NAEPro open source?
The licensing for NAEPro was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does NAEPro have?
No parameter count has been published for NAEPro, which is why no memory or speed figure appears on this page.
Who created NAEPro?
NAEPro was published by University of California Santa Barbara (UCSB),Massachusetts Institute of Technology (MIT),Carnegie Mellon University (CMU), based in United States of America, categorised as academia,Academia,Academia.
When was NAEPro released?
NAEPro was published in October 2023. 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 NAEPro used for?
NAEPro works in Biology, and is recorded as handling protein design. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run NAEPro?
None. NAEPro 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.
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.