scHyena
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
- Korea Advanced Institute of Science and Technology (KAIST)
- Organisation type
- Academia
- Country
- Korea (Republic of)
- Published
- 4 October 2024
- Authors
- Gyutaek Oh, Baekgyu Choi, Inkyung Jung, Jong Chul Ye
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.
- Training data
- tokens
Cells: 430,312 + 145,170 = 575,482 Genes per cell: 19,306 Total data points: 575,482 × 19,306 = 11,111,255,492 (1.11 × 10¹⁰) Final estimate: 1.11 × 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
- 8.6 × 10¹⁸ FLOP
- How it was established
- Hardware
3.5*24*60*60*2*35580000000000*0.4=8.6075136e+18
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 GeForce RTX 3090
- Chips used
- 2
- Power draw
- 1.4 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
- 12
Sources
Where this record came from and when it was last checked.
- Reference
- scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain
- Last updated
- 25 May 2026
What the numbers mean
About this model
scHyena was published by Korea Advanced Institute of Science and Technology (KAIST), in Korea (Republic of), in October 2024. academia is the category the publisher falls under.
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
Training it took roughly 8.6 × 10¹⁸ FLOP of computation, on NVIDIA GeForce RTX 3090 — a measure of what producing the model cost, not of how fast it answers.
Answers
scHyena — common questions
Who created scHyena?
scHyena was published by Korea Advanced Institute of Science and Technology (KAIST), based in Korea (Republic of), categorised as academia.
When was scHyena released?
scHyena was published in October 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 scHyena used for?
scHyena 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 scHyena?
Around 8.6 × 10¹⁸ FLOP, on NVIDIA GeForce RTX 3090. 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 scHyena?
None. scHyena 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 scHyena open source?
The licensing for scHyena 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 scHyena have?
No parameter count has been published for scHyena, which is why no memory or speed figure appears on this page.
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.