Thermostable protein design
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
- Indraprastha Institute of Information Technology Delhi
- Organisation type
- Academia
- Country
- India
- Published
- 24 September 2024
- Authors
- Purva Tijare, Nishant Kumar, Gajendra P. S. Raghava
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein design
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
- 738M
- Training data
- tokens
"This model comprises 36 layers and has a dimensionality of 1280, amounting to a total of 738 million parameters [28]."
"Training Dataset 13849 sequences" in Figure 1 Assumed length of 300 tokens per sequence: 13849*300
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
- 1.8 × 10¹⁶ FLOP
- How it was established
- Operation counting
6*4154700*738000000=1.8397012e+16
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Designing of thermostable proteins with a desired melting temperature
- Last updated
- 28 November 2025
What the numbers mean
Background
Thermostable protein design was published by Indraprastha Institute of Information Technology Delhi, in the country recorded as India, during September 2024. The publishing organisation is categorised as academia.
It works in the domain of Biology, and is recorded as performing the task of protein design.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Producing it required arithmetic totalling around 1.8 × 10¹⁶ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Thermostable protein design — common questions
Thermostable protein design— who created it?
It was published by Indraprastha Institute of Information Technology Delhi, based in India, an organisation categorised as academia.
Thermostable protein design— when was it released?
It was published in September 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.
Thermostable protein design— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein design. These are the areas it was designed around; they describe intent rather than a hard boundary.
Thermostable protein design— how much compute was used to train it?
Training consumed around 1.8 × 10¹⁶ FLOP. 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.
Thermostable protein design— 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.
Thermostable protein design— 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.
Thermostable protein design— how many parameters does it have?
It has a parameter count of 738M. "This model comprises 36 layers and has a dimensionality of 1280, amounting to a total of 738 million parameters [28].". 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.
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.