ProtBERT-UniRef
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
- Technical University of Munich,NVIDIA,Seoul National University,Google,Oak Ridge National Laboratory,Med AI Technology
- Organisation type
- Academia,Industry,Academia,Industry,Government
- Country
- Germany, United States of America, Korea (Republic of), China
- Published
- 4 May 2021
- Authors
- Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, Burkhard Rost
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein or nucleotide language model (pLM/nLM)
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
- 420M
- Training data
- tokens
Table 2
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
- 7.3 × 10²¹ FLOP
Table 2, two stages with different sequence lengths Stage 1: 300k steps, sequence length 512, batch size 15360 Stage 2: 100k steps, sequence length 2048, batch size 2560 6ND formula Stage 1: 6*300000*15360*512*420000000=5.9454259e+21 Stage 2: 6*100000*2560*2048*420000000=1.3212058e+21 Total: 7.2666317e+21
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
- Google TPU v3
- Chips used
- 512
- Power draw
- 466.5 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
- 5
Sources
Where this record came from and when it was last checked.
- Reference
- ProtTrans:Towards Cracking the Language of Life's Code Through Self-Supervised Learning
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
ProtBERT-UniRef was published by Technical University of Munich,NVIDIA,Seoul National University,Google,Oak Ridge National Laboratory,Med AI Technology, in Germany, in May 2021. It comes out of academia,Industry,Academia,Industry,Government.
It works in Biology, and is recorded as doing proteins, Protein or nucleotide language model (pLM/nLM).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training run consumed about 7.3 × 10²¹ FLOP, on Google TPU v3. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
ProtBERT-UniRef — common questions
When was ProtBERT-UniRef released?
ProtBERT-UniRef was published in May 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 ProtBERT-UniRef used for?
ProtBERT-UniRef works in Biology, and is recorded as handling proteins, Protein or nucleotide language model (pLM/nLM). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train ProtBERT-UniRef?
Around 7.3 × 10²¹ FLOP, on Google TPU v3. 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 ProtBERT-UniRef?
None. ProtBERT-UniRef 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 ProtBERT-UniRef open source?
The licensing for ProtBERT-UniRef 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 ProtBERT-UniRef have?
ProtBERT-UniRef has 420M parameters. Table 2. 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 ProtBERT-UniRef?
ProtBERT-UniRef was published by Technical University of Munich,NVIDIA,Seoul National University,Google,Oak Ridge National Laboratory,Med AI Technology, based in Germany, categorised as academia,Industry,Academia,Industry,Government.
The other direction
Looking at it from the other side?
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