LLPS
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
- InstaDeep
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 24 May 2024
- Authors
- Benoit Gaujac, Jérémie Donà, Liviu Copoiu, Timothy Atkinson, Thomas Pierrot, Thomas D. Barrett
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.
- Parameters
- 344M
- Training data
- 1,209,000,000 tokens
- Epochs
- 250
70 million total structural tokens = 310,000 protein structures x ~225 tokens/structure = 7.0 x 10^7 tokens
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 v4
- Chips used
- 128
- Power draw
- 86.0 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
- 17
Sources
Where this record came from and when it was last checked.
- Reference
- Learning the Language of Protein Structure
- Last updated
- 25 May 2026
What the numbers mean
Background
LLPS was published by InstaDeep, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during May 2024. The publishing organisation is categorised as industry.
It works in the domain of Biology, and is recorded as performing the task of 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.
What went into building it
Training consumed a corpus of around 1,209,000,000 tokens of text.
Answers
LLPS — common questions
LLPS— when was it released?
It was published in May 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.
LLPS— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of 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.
LLPS— what GPU do I need to run it?
None. This 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.
LLPS— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
LLPS— how many parameters does it have?
It has a parameter count of 344M. 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.
LLPS— who created it?
It was published by InstaDeep, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
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.