PeTriBERT
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
- University of Montpellier,BionomeeX
- Organisation type
- Academia,Industry
- Country
- France
- Published
- 13 August 2022
- Authors
- Baldwin Dumortier, Antoine Liutkus, Clément Carré, Gabriel Krouk
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein generation
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
- 40M
- Training data
- tokens
Training sequence data points = 290,000 proteins × 1,024 tokens/protein = 297,160,000 tokens (~2.97×10⁸)
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 × 10²⁰ FLOP
- How it was established
- Hardware
1. Hardware setup: 8x NVIDIA Tesla V100 SXM2 32GB GPUs (1.25 x 10^14 FLOP/s per GPU) 2. Training duration: 70 hours (directly provided) = 252,000 seconds 3. Utilization rate: 40% 4. Final calculation: 1.25 x 10^14 FLOP/s/GPU × 8 GPUs × 252,000 seconds × 0.4 = 1.0 x 10^20 FLOPs
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 Tesla V100 DGXS 32 GB
- Chips used
- 8
- Wall-clock time
- 70 hours
- Power draw
- 4.0 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
- 14
Sources
Where this record came from and when it was last checked.
- Reference
- PeTriBERT : Augmenting BERT with tridimensional encoding for inverse protein folding and design
- Last updated
- 28 November 2025
What the numbers mean
What this model is
PeTriBERT was published by University of Montpellier,BionomeeX, in France, in August 2022. It comes out of academia,Industry.
It works in Biology, and is recorded as doing protein generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took roughly 1 × 10²⁰ FLOP of computation, on NVIDIA Tesla V100 DGXS 32 GB — a measure of what producing the model cost, not of how fast it answers.
Answers
PeTriBERT — common questions
How much compute was used to train PeTriBERT?
Around 1 × 10²⁰ FLOP, on NVIDIA Tesla V100 DGXS 32 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 PeTriBERT?
None. PeTriBERT 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 PeTriBERT open source?
The licensing for PeTriBERT 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 PeTriBERT have?
PeTriBERT has 40M 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 PeTriBERT?
PeTriBERT was published by University of Montpellier,BionomeeX, based in France, categorised as academia,Industry.
When was PeTriBERT released?
PeTriBERT was published in August 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 PeTriBERT used for?
PeTriBERT works in Biology, and is recorded as handling protein generation. 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.