RoBERTa (PFAM)
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
- IBM Research,ETH Zurich
- Organisation type
- Industry,Academia
- Country
- United States of America, Switzerland
- Published
- 5 December 2020
- Authors
- Modestas Filipavicius, Matteo Manica, Joris Cadow, Maria Rodriguez Martinez
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)
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
- Epochs
- 2
31M sequences with an estiamted length of 300: 31M*300=9300000000
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
- 1.2 × 10¹⁹ FLOP
- How it was established
- Operation counting
1. Hardware: 4x NVIDIA Tesla P100 SXM2 GPUs (18.7 TFLOP/s per GPU in FP16) The architecture is based on Bert-base, with 110M parameters 6*9300000000*2*110000000=1.2276e+19
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 P100
- Chips used
- 4
- Power draw
- 2.0 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 (unrestricted)
- Training code
- Open source
MIT license https://github.com/PaccMann/paccmann_proteomics
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 19
Sources
Where this record came from and when it was last checked.
- Reference
- Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks
- Last updated
- 11 February 2026
What the numbers mean
About this model
RoBERTa (PFAM) was published by IBM Research,ETH Zurich, in United States of America, in December 2020. The organisation is categorised as industry,Academia.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Training and provenance
The training run consumed about 1.2 × 10¹⁹ FLOP, on NVIDIA P100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
RoBERTa (PFAM) — common questions
Who created RoBERTa (PFAM)?
RoBERTa (PFAM) was published by IBM Research,ETH Zurich, based in United States of America, categorised as industry,Academia.
When was RoBERTa (PFAM) released?
RoBERTa (PFAM) was published in December 2020. 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 RoBERTa (PFAM) used for?
RoBERTa (PFAM) works in Biology, and is recorded as handling 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.
Where can I download RoBERTa (PFAM)?
The weights for RoBERTa (PFAM) are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
How much compute was used to train RoBERTa (PFAM)?
Around 1.2 × 10¹⁹ FLOP, on NVIDIA P100. 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 RoBERTa (PFAM)?
We cannot say. RoBERTa (PFAM) 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 RoBERTa (PFAM) open source?
Its weights are published, so RoBERTa (PFAM) 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 RoBERTa (PFAM) have?
No parameter count has been published for RoBERTa (PFAM), which is why no memory or speed figure appears on this page.
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.