RiNALMo
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
- University of Zagreb,Genome Institute of Singapore,Bioinformatics Institute
- Organisation type
- Academia,Academia,Government
- Country
- Croatia, Singapore
- Published
- 29 February 2024
- Authors
- Rafael Josip Penić, Tin Vlašić, Roland G. Huber, Yue Wan, Mile Šikić
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- RNA structure prediction, RNA splice-site prediction, Mean ribosome load prediction
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
- 650M
- Training data
- tokens
17,000,000 RNA sequences
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
- 1.1 × 10²¹ FLOP
- How it was established
- Hardware
1. Hardware: 7x NVIDIA A100 GPUs (3.12e14 FLOP/s per GPU) 2. Training duration: 2 weeks (directly provided) = 1,209,600 seconds 3. Utilization: 40% (0.4) 4. Calculation: 3.12e14 FLOP/s × 7 GPUs × 1,209,600s × 0.4 = 1.05e21 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
- 7
- Wall-clock time
- 336 hours (14 days)
- Power draw
- 5.5 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
- 108
Sources
Where this record came from and when it was last checked.
- Reference
- RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks
- Last updated
- 25 May 2026
What the numbers mean
Background
RiNALMo was published by University of Zagreb,Genome Institute of Singapore,Bioinformatics Institute, in Croatia, in February 2024. academia,Academia,Government is the category the publisher falls under.
It works in Biology, and is recorded as doing rNA structure prediction, RNA splice-site prediction, Mean ribosome load prediction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required around 1.1 × 10²¹ FLOP of arithmetic, on NVIDIA A100, which is a statement about the training budget rather than about inference.
Answers
RiNALMo — common questions
How many parameters does RiNALMo have?
RiNALMo has 650M 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 RiNALMo?
RiNALMo was published by University of Zagreb,Genome Institute of Singapore,Bioinformatics Institute, based in Croatia, categorised as academia,Academia,Government.
When was RiNALMo released?
RiNALMo was published in February 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 RiNALMo used for?
RiNALMo works in Biology, and is recorded as handling rNA structure prediction, RNA splice-site prediction, Mean ribosome load prediction. 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 RiNALMo?
Around 1.1 × 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 RiNALMo?
None. RiNALMo 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 RiNALMo open source?
The licensing for RiNALMo 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.