EPInformer
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
- The University of Hong Kong,Harvard Medical School
- Organisation type
- Academia,Academia
- Country
- Hong Kong, United States of America
- Published
- 1 August 2024
- Authors
- Jiecong Lin, Ruibang Luo, Luca Pinello
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Gene expression profile generation
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
- 447.1K
- 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
- 3.4 × 10¹⁷ FLOP
1*60*60*312000000000000*0.3=3.3696e+17
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
- 1
- Wall-clock time
- 1 hours
- Power draw
- 433 W
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
- 2
Sources
Where this record came from and when it was last checked.
- Reference
- EPInformer: a scalable deep learning framework for gene expression prediction by integrating promoter-enhancer sequences with multimodal epigenomic data
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
EPInformer was published by The University of Hong Kong,Harvard Medical School, in Hong Kong, in August 2024. The organisation is categorised as academia,Academia.
It works in Biology, and is recorded as doing gene expression profile generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required around 3.4 × 10¹⁷ FLOP of arithmetic, on NVIDIA A100, which is a statement about the training budget rather than about inference.
Answers
EPInformer — common questions
How many parameters does EPInformer have?
EPInformer has 447.1K 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.
Who created EPInformer?
EPInformer was published by The University of Hong Kong,Harvard Medical School, based in Hong Kong, categorised as academia,Academia.
When was EPInformer released?
EPInformer was published in August 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 EPInformer used for?
EPInformer works in Biology, and is recorded as handling gene expression profile generation. 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 EPInformer?
Around 3.4 × 10¹⁷ 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 EPInformer?
None. EPInformer 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 EPInformer open source?
The licensing for EPInformer was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.