SS-pLM
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
- Nostrum Biodiscovery,Barcelona Supercomputing Center,Institucio Catalana de Recerca i Estudis Avancçats
- Organisation type
- Government,Research collective
- Country
- Spain
- Published
- 6 August 2023
- Authors
- Yaiza Serrano, Sergi Roda, Victor Guallar, Alexis Molina
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
- 14.8M
- Training data
- tokens
From Table 3
Total Datapoints = 1.75 × 10⁶ sequences × 300 tokens/sequence = 5.25 × 10⁸ 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
- 2.3 × 10¹⁹ FLOP
- How it was established
- Hardware
1. Hardware setup: 4x NVIDIA A30 GPUs (1.50×10¹⁴ FLOP/s per GPU in FP16) 2. Training duration: 1 day (86,400 seconds) - directly provided 3. Utilization rate: 40% 4. Calculation: 4 GPUs × 1.50×10¹⁴ FLOP/s × 86,400s × 0.40 = 2.07×10¹⁹ FLOPs 4*1.7e+14*0.4*86400s=2.3e+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 A30 PCIe
- Chips used
- 4
- Wall-clock time
- 24 hours
- Power draw
- 1.3 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
- 5
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient and accurate sequence generation with small-scale protein language models
- Last updated
- 28 November 2025
What the numbers mean
About this model
SS-pLM was published by Nostrum Biodiscovery,Barcelona Supercomputing Center,Institucio Catalana de Recerca i Estudis Avancçats, in Spain, in August 2023. The organisation is categorised as government,Research collective.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Producing it required around 2.3 × 10¹⁹ FLOP of arithmetic, on NVIDIA A30 PCIe, which is a statement about the training budget rather than about inference.
Answers
SS-pLM — common questions
Who created SS-pLM?
SS-pLM was published by Nostrum Biodiscovery,Barcelona Supercomputing Center,Institucio Catalana de Recerca i Estudis Avancçats, based in Spain, categorised as government,Research collective.
When was SS-pLM released?
SS-pLM was published in August 2023. 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 SS-pLM used for?
SS-pLM works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train SS-pLM?
Around 2.3 × 10¹⁹ FLOP, on NVIDIA A30 PCIe. 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 SS-pLM?
None. SS-pLM 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 SS-pLM open source?
The licensing for SS-pLM 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 SS-pLM have?
SS-pLM has 14.8M parameters. From Table 3. 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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