Profile Prediction
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 Washington,Salesforce Research
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 1 December 2020
- Authors
- Pascal Sturmfels, Jesse Vig, Ali Madani, Nazneen Fatema Rajani
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), Proteins
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
32 million sequences × 250 residues/sequence = 8 billion data points [32 × 10^6 × 250 = 8 × 10^9]
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 × 10²⁰ FLOP
- How it was established
- Hardware
1. Hardware setup: 8x NVIDIA Tesla V100 GPUs (130 TFLOP/s each) 2. Training duration: 2 weeks (1.2e+6 seconds) - directly provided 3. Utilization rate: 40% 4. Final calculation: 8 GPUs × 1.30e+14 FLOP/s × 1.2e+6 seconds × 0.4 utilization = 5.0e+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 V100
- Chips used
- 8
- Power draw
- 4.9 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
- 27
Sources
Where this record came from and when it was last checked.
- Reference
- Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models
- Last updated
- 25 May 2026
What the numbers mean
Background
Profile Prediction was published by University of Washington,Salesforce Research, in the country recorded as United States of America, during December 2020. The category the publisher falls under is academia,Industry.
It works in the domain of Biology, and is recorded as performing the task of protein or nucleotide language model (pLM/nLM), Proteins.
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 a computation budget of roughly 5 × 10²⁰ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Profile Prediction — common questions
Profile Prediction— when was it released?
It 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.
Profile Prediction— 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), Proteins. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Profile Prediction— how much compute was used to train it?
Training consumed around 5 × 10²⁰ FLOP, on hardware recorded as NVIDIA V100. 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.
Profile Prediction— 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.
Profile Prediction— 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.
Profile Prediction— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Profile Prediction— who created it?
It was published by University of Washington,Salesforce Research, based in United States of America, an organisation categorised as academia,Industry.
The other direction
Looking at it from the other side?
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