BADGER
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,University of California (UC) Berkeley
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 24 June 2024
- Authors
- Yue Jian, Curtis Wu, Danny Reidenbach, Aditi S. Krishnapriyan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery
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
- 2.9M
- Training data
- 100,100 tokens
- Epochs
- 20
2.9M transformer model
100,000 (training) + 100 (testing) = 100,100 unique datapoints 20 epochs mentioned but only unique datapoints counted Final estimate: 1.0e5
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 6000 Ada Generation
- Chips used
- 1
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 4
Sources
Where this record came from and when it was last checked.
- Reference
- General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
- Last updated
- 25 May 2026
What the numbers mean
Background
BADGER was published by NVIDIA,University of California (UC) Berkeley, in United States of America, in June 2024. It comes out of industry,Academia.
It works in Biology, and is recorded as doing drug discovery.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Around 100,100 tokens went into training it.
Answers
BADGER — common questions
What GPU do I need to run BADGER?
None. BADGER 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 BADGER open source?
The licensing for BADGER 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 BADGER have?
BADGER has 2.9M parameters. 2.9M transformer model. 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.
Who created BADGER?
BADGER was published by NVIDIA,University of California (UC) Berkeley, based in United States of America, categorised as industry,Academia.
When was BADGER released?
BADGER 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 BADGER used for?
BADGER works in Biology, and is recorded as handling drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.