ProstT5
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
- Technical University of Munich,Seoul National University,Institute for Advanced Study,TUM School of Life Sciences Weihenstephan
- Organisation type
- Academia,Academia,Academia,Academia
- Country
- Germany, Korea (Republic of), United States of America
- Published
- 24 March 2024
- Authors
- Michael Heinzinger, Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Milot Mirdita, Martin Steinegger, Burkhard Rost
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
- 3B
- Training data
- tokens
Number of samples = 34M Tokens per sequence = 238 Total = 34,000,000 × 238 = 8,092,000,000 tokens ≈ 8.1B
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
- 3.1 × 10²¹ FLOP
- How it was established
- Hardware
1. Hardware setup: 8x NVIDIA A100 GPUs (3.12e14 FLOP/s per GPU) 2. Training duration: Directly provided - 36 days total (10 days pre-training + 26 days translation training) = 3,110,400 seconds 3. Utilization rate: 40% 4. Final calculation: 3.12e14 FLOP/s × 8 GPUs × 3,110,400s × 0.4 = 3.1e21 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 A100
- Chips used
- 8
- Wall-clock time
- 864 hours (36 days)
- Power draw
- 6.3 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
- 212
Sources
Where this record came from and when it was last checked.
- Reference
- Bilingual Language Model for Protein Sequence and Structure
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
ProstT5 was published by Technical University of Munich,Seoul National University,Institute for Advanced Study,TUM School of Life Sciences Weihenstephan, in Germany, in March 2024. The organisation is categorised as academia,Academia,Academia,Academia.
It works in Biology, and is recorded as doing 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
The training run consumed about 3.1 × 10²¹ FLOP, on NVIDIA A100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
ProstT5 — common questions
Who created ProstT5?
ProstT5 was published by Technical University of Munich,Seoul National University,Institute for Advanced Study,TUM School of Life Sciences Weihenstephan, based in Germany, categorised as academia,Academia,Academia,Academia.
When was ProstT5 released?
ProstT5 was published in March 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.
What is ProstT5 used for?
ProstT5 works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train ProstT5?
Around 3.1 × 10²¹ FLOP, on NVIDIA A100. 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 ProstT5?
None. ProstT5 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 ProstT5 open source?
The licensing for ProstT5 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 ProstT5 have?
ProstT5 has 3B parameters. 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?
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.