ProSST
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
- Shanghai Jiao Tong University,Shanghai AI Lab,East China University of Science and Technology
- Organisation type
- Academia,Academia,Academia
- Country
- China
- Published
- 17 May 2024
- Authors
- Mingchen Li, Pan Tan, Xinzhu Ma, Bozitao Zhong, Huiqun Yu, Ziyi Zhou, Wanli Ouyang, Bingxin Zhou, Liang Hong, Yang Tan
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
- 110M
- Training data
- tokens
Explicitly denoted in Table 2
18.8M structures × 300 residues per structure = 5.64B data points ≈ 5.6B data points
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
- 6.5 × 10²⁰ FLOP
- How it was established
- Hardware
"All ProSST models is trained on a DGX-A800 GPU (8×80G) server in BF16 precision for about a month." 8*77970000000000*0.4*1month=646714368000000000000
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 A800 PCIe 40 GB
- Chips used
- 8
- Wall-clock time
- 720 hours (30 days)
- Power draw
- 4.0 kW
"All ProSST models is trained [...] for about a month."
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
- 57
Sources
Where this record came from and when it was last checked.
- Reference
- ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention
- Last updated
- 1 January 2026
What the numbers mean
What this model is
ProSST was published by Shanghai Jiao Tong University,Shanghai AI Lab,East China University of Science and Technology, in the country recorded as China, during May 2024. The publishing organisation is categorised as academia,Academia,Academia.
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.
Training and provenance
Producing it required arithmetic totalling around 6.5 × 10²⁰ FLOP, on hardware recorded as NVIDIA A800 PCIe 40 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
ProSST — common questions
ProSST— 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.
ProSST— how much compute was used to train it?
Training consumed around 6.5 × 10²⁰ FLOP, on hardware recorded as NVIDIA A800 PCIe 40 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.
ProSST— 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.
ProSST— 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.
ProSST— how many parameters does it have?
It has a parameter count of 110M. Explicitly denoted in Table 2. 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.
ProSST— who created it?
It was published by Shanghai Jiao Tong University,Shanghai AI Lab,East China University of Science and Technology, based in China, an organisation categorised as academia,Academia,Academia.
ProSST— 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.
The other direction
Looking at it from the other side?
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