ProteinStructureTransformer
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
- Max Planck Institute of Biochemistry
- Organisation type
- Academia
- Country
- Germany
- Published
- 26 January 2024
- Authors
- Dexiong Chen, Philip Hartout, Paolo Pellizzoni, Carlos Oliver, Karsten Borgwardt
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
- Base model
- ESM2-650M
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
- 1.1B
- Training data
- tokens
- Epochs
- 100
542,378 proteins × 300 residues/protein = 162,713,400 tokens (1.627e8) Final estimate: 1.6e8 datapoints
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
- 7.6 × 10²¹ FLOP
- How it was established
- Hardware
- Fine-tuning compute
- 5.7 × 10¹⁹ FLOP
GPU hour estimate 4*36000s*9.9e+14*0.4=5.7e+19 Base model: 7.560000000001e+21 Combined: 7616995200001001000000
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 H100 SXM5 80GB
- Chips used
- 4
- Wall-clock time
- 10 hours
- Power draw
- 5.5 kW
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 21
Sources
Where this record came from and when it was last checked.
- Reference
- ENDOWING PROTEIN LANGUAGE MODELS WITH STRUCTURAL KNOWLEDGE
- Last updated
- 25 May 2026
What the numbers mean
Background
ProteinStructureTransformer was published by Max Planck Institute of Biochemistry, in Germany, in January 2024. It comes out of academia.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
It is derived from ESM2-650M rather than trained from scratch, which is the usual way a specialised model is produced.
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 7.6 × 10²¹ FLOP of arithmetic, on NVIDIA H100 SXM5 80GB, which is a statement about the training budget rather than about inference.
Answers
ProteinStructureTransformer — common questions
How many parameters does ProteinStructureTransformer have?
ProteinStructureTransformer has 1.1B parameters. 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 ProteinStructureTransformer?
ProteinStructureTransformer was published by Max Planck Institute of Biochemistry, based in Germany, categorised as academia.
When was ProteinStructureTransformer released?
ProteinStructureTransformer was published in January 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 ProteinStructureTransformer used for?
ProteinStructureTransformer works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train ProteinStructureTransformer?
Around 7.6 × 10²¹ FLOP, on NVIDIA H100 SXM5 80GB. 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 ProteinStructureTransformer?
None. ProteinStructureTransformer 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 ProteinStructureTransformer open source?
The licensing for ProteinStructureTransformer 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?
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