OmegaPLM
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
- Massachusetts Institute of Technology (MIT),Westlake University
- Organisation type
- Academia,Academia
- Country
- United States of America, China
- Published
- 22 July 2022
- Authors
- Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, Jianzhu Ma, Jian Peng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein folding prediction
- Numerical format
- TF32
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
- 670M
- Training data
- 1,258,291,200,000 tokens
- Batch size
- 2,097,152
"Our model contains 66 layers with around 670 million parameters without sharing parameters, which doubles the layer count of ESM-1b but roughly retains the parameter count."
Number of sequences: 35 x 10^6 Sequence length: 512 Total data points: 35 x 10^6 x 512 = 1.792 x 10^10 tokens First stage crop size: 256 First stage data points: 35 x 10^6 x 256 = 8.96 x 10^9 tokens Additional structural data: ~110,000 sequences = 7.68 x 10^7 tokens Final estimate: 8.96 x 10^9 tokens
"[...] each batch contains 4,096 sequences and each sequence is padded or cropped to 512 residues" 4096 * 512 = 2097152
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 × 10²² FLOP
- How it was established
- Hardware
"OmegaPLM is implemented in PyTorch (44) and trained for 2,560 GPU Nvidia A100 80G days." "Default precision format in Nvidia A100 GPUs is set to TensorFloat-32 for matrix operations." Assume 0.3 utilization for language model Estimate: (2560 * 24 * 3600) s * 156e12 FLOP/s * 0.3 * = 1.04e22
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 SXM4 80 GB
- Chip-hours
- 61,440
- Compute cost
- $52,400
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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance
- Record confidence
- Confident
- Citations
- 445
"Here, we introduce OmegaFold, the first computational method to successfully predict high-resolution protein structure from a single primary sequence alone. Using a new combination of a protein language model that allows us to make predictions from single sequences and a geometry-inspired transformer model trained on protein structures, OmegaFold outperforms RoseTTAFold and achieves similar prediction accuracy to AlphaFold2 on recently released structures"
Sources
Where this record came from and when it was last checked.
- Reference
- High-resolution de novo structure prediction from primary sequence
- Last updated
- 1 January 2026
What the numbers mean
About this model
OmegaPLM was published by Massachusetts Institute of Technology (MIT),Westlake University, in United States of America, in July 2022. The organisation is categorised as academia,Academia.
It works in Biology, and is recorded as doing proteins, Protein folding prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Producing it required around 1 × 10²² FLOP of arithmetic, on NVIDIA A100 SXM4 80 GB, which is a statement about the training budget rather than about inference.
It was trained on about 1,258,291,200,000 tokens of text.
The reason it appears in this catalogue at all is historical significance.
Answers
OmegaPLM — common questions
How much compute was used to train OmegaPLM?
Around 1 × 10²² FLOP, on NVIDIA A100 SXM4 80 GB. 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 OmegaPLM?
None. OmegaPLM 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 OmegaPLM open source?
No. OmegaPLM has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does OmegaPLM have?
OmegaPLM has 670M parameters. "Our model contains 66 layers with around 670 million parameters without sharing parameters, which doubles the layer count of ESM-1b but roughly retains the parameter count.". 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 OmegaPLM?
OmegaPLM was published by Massachusetts Institute of Technology (MIT),Westlake University, based in United States of America, categorised as academia,Academia.
When was OmegaPLM released?
OmegaPLM was published in July 2022. 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 OmegaPLM used for?
OmegaPLM works in Biology, and is recorded as handling proteins, Protein folding prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.