OmniNA
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
- Tianjin Medical University
- Organisation type
- Academia
- Country
- China
- Published
- 15 January 2024
- Authors
- Xilin Shen, Xiangchun Li
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
- 1.7B
- Training data
- tokens
- Epochs
- 0.22
From Nucleotide Sequences: 1,076,200,000,000 tokens From Text Annotations: 197,000,000 words (estimated at 197000000/0.75=262666666 tokens Total: 1076462666666
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
- 2.5 × 10²¹ FLOP
- How it was established
- Operation counting
200k steps, 2048batch, 601 sequence length 200k*2048*601=246169600000 Compute: 6*1.7e+9*2.5e+11=2.5e+21 Epoch: 246169600000/1076462666666=0.22
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 SXM4 80 GB
- Chips used
- 8
- 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
- Likely
- Citations
- 3
Sources
Where this record came from and when it was last checked.
- Reference
- OmniNA: A foundation model for nucleotide sequences
- Last updated
- 28 November 2025
What the numbers mean
Background
OmniNA was published by Tianjin Medical University, in China, in January 2024. It comes out of academia.
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.
How it was trained
Training it took roughly 2.5 × 10²¹ FLOP of computation, on NVIDIA A100 SXM4 80 GB — a measure of what producing the model cost, not of how fast it answers.
Answers
OmniNA — common questions
How much compute was used to train OmniNA?
Around 2.5 × 10²¹ FLOP, on NVIDIA A100 SXM4 80 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.
What GPU do I need to run OmniNA?
None. OmniNA 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 OmniNA open source?
The licensing for OmniNA 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 OmniNA have?
OmniNA has 1.7B 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 OmniNA?
OmniNA was published by Tianjin Medical University, based in China, categorised as academia.
When was OmniNA released?
OmniNA was published in January 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 OmniNA used for?
OmniNA works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). These are the areas it was designed around; they describe intent rather than a hard boundary.
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.