Text2Protein
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 California San Diego,Brown University
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 13 September 2024
- Authors
- Ramtin Hosseini, Siyang Zhang, Pengtao Xie
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.
- Training data
- tokens
Training set: 10,898 * 0.95 = 10,353 proteins Average residues per protein: 100 Total datapoints = 10,353 * 100 = 1,035,300 Range validation: Minimum (40 residues): 10,353 * 40 = 414,120 Maximum (256 residues): 10,353 * 256 = 2,648,768 Final estimate: ~1.0e6 datapoints
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
- 5.5 × 10¹⁹ FLOP
- How it was established
- Hardware
1. Hardware setup: 1x NVIDIA RTX 3090 (1.60e14 FLOP/s FP16 tensor) 2. Training duration: 10 days provided directly (864,000 seconds) 3. Utilization rate: 40% 4. Calculation: 1.60e14 FLOP/s × 1 GPU × 864,000s × 0.4 = 5.5e19 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 GeForce RTX 3090
- Wall-clock time
- 240 hours (10 days)
"We trained the diffusion model for approximately 500,000 iterations with a batch size of 248. The entire training process, conducted on an NVIDIA RTX 3090 GPU, took about 10 days to complete."
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
- Text2Protein: A Generative Model for Designated Protein Design on Given Description
- Last updated
- 28 November 2025
What the numbers mean
Background
Text2Protein was published by University of California San Diego,Brown University, in United States of America, in September 2024. The organisation is categorised as academia,Academia.
It works in Biology, and is recorded as doing protein design.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training it took roughly 5.5 × 10¹⁹ FLOP of computation, on NVIDIA GeForce RTX 3090 — a measure of what producing the model cost, not of how fast it answers.
Answers
Text2Protein — common questions
What GPU do I need to run Text2Protein?
None. Text2Protein 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 Text2Protein open source?
The licensing for Text2Protein 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 Text2Protein have?
No parameter count has been published for Text2Protein, which is why no memory or speed figure appears on this page.
Who created Text2Protein?
Text2Protein was published by University of California San Diego,Brown University, based in United States of America, categorised as academia,Academia.
When was Text2Protein released?
Text2Protein 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.
What is Text2Protein used for?
Text2Protein works in Biology, and is recorded as handling protein design. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train Text2Protein?
Around 5.5 × 10¹⁹ FLOP, on NVIDIA GeForce RTX 3090. 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.
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.