LBSTER
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
- Prescient Design,Genentech
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 15 May 2024
- Authors
- Nathan C. Frey, Taylor Joren, Aya Abdelsalam Ismail, Allen Goodman, Richard Bonneau, Kyunghyun Cho, Vladimir Gligorijević
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
- 67M
- Training data
- 3,375,000,000 tokens
- Epochs
- 4.06
"we are able to train a 67 million parameter model"
UniRef50 dataset size calculation: 43,000,000 sequences × 300 tokens/sequence = 1.29 × 10¹⁰ tokens Training process calculation: 1,048,576 tokens/step × 50,000 steps = 5.24288 × 10¹⁰ total tokens Epochs calculation: 5.24288 × 10¹⁰ / 1.29 × 10¹⁰ = 4.06 epochs Final unique tokens (first epoch only): 1.3 × 10¹⁰ 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
- 1.1 × 10¹⁹ FLOP
- How it was established
- Hardware
1 day on 1 A100 312000000000000*1day*0.4=10782720000000000000
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 A100 SXM4 80 GB
- Chips used
- 1
- Wall-clock time
- 24 hours
- Power draw
- 434 W
"we are able to train a 67 million parameter model in a single day
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
- 4
Sources
Where this record came from and when it was last checked.
- Reference
- Cramming Protein Language Model Training in 24 GPU Hours
- Last updated
- 28 November 2025
What the numbers mean
What this model is
LBSTER was published by Prescient Design,Genentech, in the country recorded as United States of America, during May 2024. It comes out of an organisation categorised as industry,Industry.
It works in the domain of Biology, and is recorded as performing the task of protein or nucleotide language model (pLM/nLM).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Training it took a computation budget of roughly 1.1 × 10¹⁹ FLOP, on hardware recorded as NVIDIA A100 SXM4 80 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 3,375,000,000 tokens of text.
Answers
LBSTER — common questions
LBSTER— who created it?
It was published by Prescient Design,Genentech, based in United States of America, an organisation categorised as industry,Industry.
LBSTER— 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.
LBSTER— 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). These are the areas it was designed around; they describe intent rather than a hard boundary.
LBSTER— how much compute was used to train it?
Training consumed around 1.1 × 10¹⁹ FLOP, on hardware recorded as NVIDIA A100 SXM4 80 GB. 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.
LBSTER— 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.
LBSTER— 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.
LBSTER— how many parameters does it have?
It has a parameter count of 67M. "we are able to train a 67 million parameter model". 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?
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