LM-GVP
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- Amazon Machine Learning Solutions Lab,Johnson & Johnson
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 21 September 2021
- Authors
- Zichen Wang, Steven A. Combs, Ryan Brand, Miguel Romero Calvo, Panpan Xu, George Price, Nataliya Golovach, Emmanuel O. Salawu, Colby J. Wise, Sri Priya Ponnapalli, Peter M. Clark
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein property prediction
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
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 V100
- Chips used
- 8
- Power draw
- 4.8 kW
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
- Open — downloadable
- Model access
- Open weights (non-commercial)
- Training code
- Open (non-commercial)
"The source code for training end-to-end models, together with the neural network weights are available for research and non-commercial use at https://github.com/aws-samples/lm-gvp" "Datasets used in this study are available to download at: https://github.com/flatironinstitute/DeepFRI/tree/master/preprocessing/data and https://github.com/songlab-cal/tape"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 11
Sources
Where this record came from and when it was last checked.
- Reference
- LM-GVP: A Generalizable Deep Learning Framework for Protein Property Prediction from Sequence and Structure
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
LM-GVP was published by Amazon Machine Learning Solutions Lab,Johnson & Johnson, in United States of America, in September 2021. The organisation is categorised as industry,Industry.
It works in Biology, and is recorded as doing protein property prediction.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Answers
LM-GVP — common questions
What GPU do I need to run LM-GVP?
We cannot say. LM-GVP has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is LM-GVP open source?
Its weights are published, so LM-GVP can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does LM-GVP have?
No parameter count has been published for LM-GVP, which is why no memory or speed figure appears on this page.
Who created LM-GVP?
LM-GVP was published by Amazon Machine Learning Solutions Lab,Johnson & Johnson, based in United States of America, categorised as industry,Industry.
When was LM-GVP released?
LM-GVP was published in September 2021. 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 LM-GVP used for?
LM-GVP works in Biology, and is recorded as handling protein property prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download LM-GVP?
The weights for LM-GVP are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
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.