SSA
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
- Massachusetts Institute of Technology (MIT)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 22 February 2019
- Authors
- Tristan Bepler, Bonnie Berger
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein embedding
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 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
- 1.3 × 10¹⁹ FLOP
- How it was established
- Hardware
"All models were implemented in PyTorch and trained on a single NVIDIA Tesla V100 GPU. Each model took roughly 3 days to train and required 16 GB of GPU RAM" 1.3e+14*0.4*259200s=1.3e+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 V100
- Chips used
- 1
- Wall-clock time
- 72 hours
- Power draw
- 340 W
"Each model took roughly 3 days to train and required 16 GB of GPU RAM"
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
- 344
Sources
Where this record came from and when it was last checked.
- Reference
- Learning protein sequence embeddings using information from structure
- Last updated
- 25 May 2026
What the numbers mean
Background
SSA was published by Massachusetts Institute of Technology (MIT), in United States of America, in February 2019. It comes out of academia.
It works in Biology, and is recorded as doing protein embedding.
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 1.3 × 10¹⁹ FLOP of computation, on NVIDIA V100 — a measure of what producing the model cost, not of how fast it answers.
Answers
SSA — common questions
How much compute was used to train SSA?
Around 1.3 × 10¹⁹ FLOP, on 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.
What GPU do I need to run SSA?
None. SSA 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 SSA open source?
The licensing for SSA 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 SSA have?
No parameter count has been published for SSA, which is why no memory or speed figure appears on this page.
Who created SSA?
SSA was published by Massachusetts Institute of Technology (MIT), based in United States of America, categorised as academia.
When was SSA released?
SSA was published in February 2019. 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 SSA used for?
SSA works in Biology, and is recorded as handling protein embedding. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.